Novel single and multi-layer echo-state recurrent autoencoders for representation learning. Deep learning for image colorization: Current and future prospects. [18] Wang, Xiaolong and Abhinav Gupta. The authors presented the SR-based image fusion using sparse coding through Orthogonal Matching Pursuit (OMP) and a sparse vector fusion strategy with the maximum L 1-norm for the coefficient combination . estimator : In this we have to pass the metric or the model for which we need to optimize the parameters. Step 4 - Using RandomizedSearchCV and Printing the results. 07, Jun 20. Cannot retrieve contributors at this time. Image colorization has seen significant advancements using Deep Learning. param_distributions : In this we have to pass the dictionary of parameters that we need to optimize. Image colorization is the process of taking grayscale images (as input) and then producing colorized images (as output) that represents the semantic colors and tones of the input. 9) Build CNN for Image Colorization using Deep Transfer Learning. Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation: ECCV: code: 50: Efficient end-to-end learning for quantizable representations: ICML: Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search: ICML: code: 23: To Trust Or Not To Trust A Classifier: NIPS: End-To-End Machine Learning Projects with Source Code for Practice in November 2021. Sparsification of Decomposable Submodular Functions print(" Results from Random Search " ) ML - Saving a Deep Learning model in Keras. Colorization can be used as a powerful self-supervised task: a model is trained to color a grayscale input image; precisely the task is to map this image to a distribution over quantized color value outputs (Zhang et al. 2016).. We are using the inbuilt diabetes dataset to train the model and we used train_test_split to split the data into two parts train and test. Colorization of Black and White Images. [21] https://cloud.tencent.com/developer/article/1389555, [22] https://ankeshanand.com/blog/2020/01/26/contrative-self-supervised-learning.html, [23] Hjelm, R. Devon et al. ab Split-Brain Autoencoders [12] Next, convert the RBG format to LAB one. In CVPR 2016. Papers: Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction. A brief background of AI, and specifically machine learning (ML) algorithms, is provided including convolutional neural networks (CNNs), generative adversarial networks (GANs), recurrent neural networks (RNNs) min_weight_fraction_leaf=0.0, n_estimators=737, This paper reviews the current state of the art in artificial intelligence (AI) technologies and applications in the context of the creative industries. Step 1: Encoding the input data The Auto-encoder first tries to encode the data He naff, Ali Razavi, Carl Doersch, S. M. Ali Eslami, Aaron van den Oord; Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song. Momentum Contrast for Unsupervised Visual Representation Learning. ArXiv abs/1911.05722 (2019): n. pag. Image Processing Project -Train a model for colorization to make grayscale images colorful using convolutional autoencoders. GoArt Magenta . You signed in with another tab or window. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Unsupervised Representation Learning by Predicting Image Rotations (ICLR18)ConvNets2D Magenta is an open-source research project that explores the role of machine learning as a tool in the creative process. 9) Build CNN for Image Colorization using Deep Transfer Learning. So this is the recipe on How we can find optimal parameters using RandomizedSearchCV for Regression. Time-Contrastive Networks: Self-Supervised Learning from Video. 2018 IEEE International Conference on Robotics and Automation (ICRA) (2017): 1134-1141. Results from Random Search cv : In this we have to pass a interger value, as it signifies the number of splits that is needed for cross validation. Temporal Based3. SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-Resolution Jiangning Zhang, Chao Xu, Jian Li, Yue Han, Yabiao Wang, Ying Tai, Yong Liu. linkhttps://amitness.com/2020/02/illustrated-self-supervised-learning/, https://mp.weixin.qq.com/s/VvUj0S2OTf8BowGRjDuVag, https://zhuanlan.zhihu.com/p/342922164, Yann Lecun , Word2VecGloveELMOBERT, , , 0/90/180/270 cat / dog cat / dog , , Yann LeCun AAAI 2020 , https://www.bilibili.com/video/BV1V7411573v?from=search&seid=12729545036652967460, -encoder-decoderL2, , Papers: Colorful Image Colorization | Real-Time User-Guided Image Colorization with Learned Deep Priors | Let there be Color! Challenging AI Projects in Computer Vision for Experts GANs, and more specifically their discriminators and generators, can be architected in a variety of ways to solve a wide range of image processing problems. Data-Efficient Image Recognition with Contrastive Predictive Coding Olivier J. In doing so it can learn to disentangle aspects of images such as hair styles, the presence of objects, or emotions, all through unsupervised training. Applied Deep Learning (YouTube Playlist)Course Objectives & Prerequisites: This is a two-semester-long course primarily designed for graduate students. WHEN IS THE CLEANING OF SUBJECTIVE DATA RELEVANT TO TRAIN UGC VIDEO QUALITY METRICS? Self-Supervised Visual Feature Learning with Deep Neural Networks: A Survey. Also be sure to check out this GitHub repo. 2016).. Data-Efficient Image Recognition with Contrastive Predictive Coding Olivier J. A Conditional GAN (cGAN), solves this by leveraging additional information such as label data (aka class labels). Deep learning for image colorization: Current and future prospects. The hyperparameters tunning is also explained in this. subsample=0.40247913722860207, tol=0.0001, Learn to build a Multiple linear regression model in Python on Time Series Data. 2) Text Classification with Transformers-RoBERTa and XLNet Model. Article 105051 Download PDF. Deep learning is a class of machine learning algorithms that: 199200 uses multiple layers to progressively extract higher-level features from the raw input. Before using RandomizedSearchCV first look at its parameters: estimator : In this we have to pass the metric or the model for which we need to optimize the parameters. Article 105051 Download PDF. Magenta is an open-source research project that explores the role of machine learning as a tool in the creative process. Article 105006 Download PDF. This architecture is summarized in the following diagram: For more information about InfoGAN, check out this article. The following example shows a standard GAN for generating images of handwritten digits, that is enhanced with label data to generate only images of the numbers 8 and 0: Here, labels can be one-hot encoded to remove ordinality and then input to both the discriminator and generator as additional layers, where they are then concatenated with their respective image inputs (i.e., concatenated with noise for the generator, and with the training set for the generator). To train an SRGAN, a high-resolution image is first downsampled into a lower resolution image and input into a generator. Self-supervised representation learning by counting features. Contrastive Multiview Coding. ArXiv abs/1906.05849 (2019): n. pag. Machine learning practitioners are increasingly turning to the power of generative adversarial networks (GANs) for image processing. Output of this snippet is given below: ProjectPro is a unique platform and helps many people in the industry to solve real-life problems with a step-by-step walkthrough of projects. Applications that really benefit from using GANs include: generating art and photos from text-based descriptions, upscaling images, transferring images across domains (e.g., changing day time scenes to night time), and many Data-Efficient Image Recognition with Contrastive Predictive Coding Olivier J. Deep learning for image colorization: Current and future prospects. Papers: Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction. AICVNLPGraphRecSysRL [26] Tian, Yonglong et al. ICLR 2019. Learning Visual Features by Colorization for Slide-Consistent Survival Prediction from Whole Slide Images. VACA: Designing Variational Graph Autoencoders for Causal Queries Pablo Sanchez-Martin, Miriam Rateike, Isabel Valera. The authors provide the following overview of their models architecture: While solving this problem is possible with a regular GAN, output images can lack details and may be limited to lower resolutions. Naima Chouikhi, Boudour Ammar, Amir Hussain, Adel M. Alimi. This information can then be used to control certain aspects of the generated images. Use-Case: This project can be used to color old historical images to obtain more information from them. ab Split-Brain Autoencoders [12] A Simple Framework for Contrastive Learning of Visual Representations. ArXiv abs/2002.05709 (2020): n. pag. @bingo [2] [3]@Naiyan Wang survey[4] @Sherlock [5] Self-Supervised Learning @Sherlock , , , , Representation Learning- L1 L2 , pretext, Pretrain-Fintune Pretrain - Finetune Downstream task Pretrain - Finetune pretext , 3 1. ChromaGAN is an example of a picture colorization model. Deep learning is a class of machine learning algorithms that: 199200 uses multiple layers to progressively extract higher-level features from the raw input. Since then, various innovative SR models and fusion strategies have been developed. : Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification, Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, Context encoders: Feature learning by inpainting, Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction, Unsupervised learning of visual representations by solving jigsaw puzzles, Unsupervised Visual Representation Learning by Context Prediction, Unsupervised Representation Learning by Predicting Image Rotations, Deep clustering for unsupervised learning of visual features, Self-labelling via simultaneous clustering and representation learning, CliqueCNN: Deep Unsupervised Exemplar Learning, Shuffle and Learn: Unsupervised Learning using Temporal Order Verification, Self-Supervised Video Representation Learning With Odd-One-Out Networks. 3*3 8 , , 3 100, 4 3 2 , 8 8 . For example, given images of faces where some are wearing glasses, an InfoGAN could be trained to disentangle pixels for glasses, and then use that to generate new faces with glasses. The following summary can help you choose which GAN might be right for your application: What type of GANs do you currently work with? from keras.datasets import cifar100 Colorization Autoencoders using Keras. Segmentation can be accomplished using Pix2Pix, a type of cGAN for image-to-image translation, where a PatchGAN discriminator is first trained to classify whether generated images with these translations are real or fake, and then used to train a U-Net-based generator to produce increasingly believable translations. print("\n The best estimator across ALL searched params:\n", randm_src.best_estimator_) This can be done by RandomizedSearchCV. Naima Chouikhi, Boudour Ammar, Amir Hussain, Adel M. Alimi. Shanshan Huang, Xin Jin, Qian Jiang, Li Liu. Next, convert the RBG format to LAB one. [10] Devlin, Jacob et al. This process, was conventionally done by hand with human effort, considering the difficulty of the task. So we have defined an object to use RandomizedSearchCV with the important parameters. WHAT IF IMAGE SELF-SIMILARITY CAN BE BETTER EXPLOITED IN DATA FIDELITY TERMS? Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation. Short Bio Alex's research is centered around machine learning and computer vision. As we discussed in our blog Top Five Ways That Machine Learning is Being Used for Image Processing and Computer Vision, object segmentation is a method to partition groups of pixels from a digital image into segments which can then be labelled, located, and even tracked as objects in one or more images. [15] Gidaris, Spyros et al. received your notification of the results by email, please contact us at icip2022@cmsworkshops.com. Without such a condition, a standard GAN (sometimes called an unconditional GAN) simply relies on mapping the data in the latent space to that of the generated images. GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None, Deep Graph Infomax. ArXiv abs/1809.10341 (2018): n. pag. IS THE U-NET DIRECTIONAL-RELATIONSHIP AWARE? In ECCV 2016. Training of an Auto-encoder for data compression: For a data compression procedure, the most important aspect of the compression is the reliability of the reconstruction of the compressed data. It basically contains two parts: the first one is an encoder which is similar to the convolution neural network except for the last layer. This can also result in more stable or faster training, while potentially increasing the quality of generated images. Applications that really benefit from using GANs include: generating art and photos from text-based descriptions, upscaling images, transferring images across domains (e.g., changing day time scenes to night time), and many Using the method to_categorical(), a numpy array (or) a vector which has integers that represent different categories, can be converted into a numpy array (or) a matrix which has binary values and has columns equal to the number of categories in the data. But how find which set of hyperparameters gives the best result? from sklearn.model_selection import RandomizedSearchCV NeurIPS 2019 Iterative Contrastive Learning for Single Image Raindrop Removal, ITERATIVE KERNEL RECONSTRUCTION FOR DEEP LEARNING-BASED BLIND IMAGE SUPER-RESOLUTION, Iterative Seeded Region Growing for Brain Tissue Segmentation, Joint disentanglement of labels and their features with VAE, JOINT MOTION CORRECTION AND 3D SEGMENTATION WITH GRAPH-ASSISTED NEURAL NETWORKS FOR RETINAL OCT, Joint Motion-Correction and Reconstruction in Cryo-EM Tomography, JPEG PLENO LIGHT FIELD ENCODER WITH BREAKPOINT DEPENDENT AFFINE WAVELET TRANSFORM FOR DISPARITY MAPS, Knowledge Distillation for Multi-Target Domain Adaptation in Real-Time Person Re-Identification, LATENCY COMPENSATION THROUGH IMAGE WARPING FOR REMOTE RENDERING-BASED VOLUMETRIC VIDEO STREAMING, Latent Preserving Generative Adversarial Network for Imbalance classification, Latent Vector Prototypes Guided Conditional Face Synthesis, LB-NERF: LIGHT BENDING NEURAL RADIANCE FIELDS FOR TRANSPARENT MEDIUM, LEARNABLE PIXEL CLUSTERING VIA STRUCTURE AND SEMANTIC DUAL CONSTRAINTS FOR UNSUPERVISED IMAGE SEGMENTATION, LEARNED IMAGE COMPRESSION WITH MULTI-SCALE SPATIAL AND CONTEXTUAL INFORMATION FUSION, Learned Video Compression with Residual Prediction and Feature-aided Loop Filter, LEARNING AN EVOLVED MIXTURE MODEL FOR TASK-FREE CONTINUAL LEARNING, LEARNING CONTEXTUALLY FUSED AUDIO-VISUAL REPRESENTATIONS FOR AUDIO-VISUAL SPEECH RECOGNITION, LEARNING FREQUENCY-SPECIFIC QUANTIZATION SCALING IN VVC FOR STANDARD-COMPLIANT TASK-DRIVEN IMAGE CODING, Learning from Designers: Fashion Compatibility Analysis via Dataset Distillation, Learning from Noisy Labels via Meta Credible Label Elicitation, LEARNING FROM SYNTHETIC DATA FOR CROWD INSTANCE SEGMENTATION IN THE WILD, Learning graph features for colored mesh visual quality assessment, LEARNING HETERO-SYNAPTIC DELAYS FOR MOTION DETECTION IN A SINGLE LAYER OF SPIKING NEURONS, Learning Selective Assignment Network for Scene-aware Vehicle Detection, LEARNING TO GENERATE HIGH-QUALITY IMAGES FOR HOMOGRAPHY ESTIMATION, LEARNING TO JOINTLY SEGMENT THE LIVER, LESIONS AND VESSELS FROM PARTIALLY ANNOTATED DATASETS, LEARNING TRAJECTORY-CONDITIONED RELATIONS TO PREDICT PEDESTRIAN CROSSING BEHAVIOR, LEARNING-BASED END-TO-END VIDEO COMPRESSION WITH SPATIAL-TEMPORAL ADAPTATION, Learning-based Lossless Point Cloud Geometry Coding using Sparse Tensors, LE-BEIT: A LOCAL-ENHANCED SELF-SUPERVISED TRANSFORMER FOR SEMANTIC SEGMENTATION OF HIGH RESOLUTION REMOTE SENSING IMAGES, Light Field Image Quality Assessment with Dense Atrous Convolutions, LIGHT FIELD INTEGRAL IMAGE CODING OPTIMIZATION UNDER 2D HIERARCHICAL CODING STRUCTURE, LIGHTER AND FASTER TWO-PATHWAY CMRNET FOR VIDEO SALIENCY PREDICTION, LIGHTWEIGHT DUAL-DOMAIN NETWORK FOR REAL-TIME MEDICAL IMAGE SEGMENTATION, LINEAR DISCRIMINANT ANALYSIS METRIC LEARNING USING SIAMESE NEURAL NETWORKS, LISNET: A COVID-19 LUNG INFECTION SEGMENTATION NETWORK BASED ON EDGE SUPERVISION AND MULTI-SCALE CONTEXT AGGREGATION, LOCAL AND GLOBAL FUSION NETWORK FOR LEARNED IMAGE COMPRESSION, LOCALIZATION AND CLASSIFICATION OF PARASITIC EGGS IN MICROSCPIC IMAGES USING AN EFFICIENTDET DETECTOR, LOW SNR MULTIFRAME REGISTRATION FOR CUBESATS, LOW-COMPLEXITY MULTI-TYPE TREE PARTITIONING FOR VERSATILE VIDEO CODING BASED ON MACHINE LEARNING, LOW-COMPLEXITY SCALER BASED ON CONVOLUTIONAL NEURAL NETWORKS FOR ADAPTIVE VIDEO STREAMING, LOW-LIGHT IMAGE ENHANCEMENT METHOD BY USING A MODIFIED GAMMA TRANSFORM FOR CONVEX COMBINATION COEFFICIENTS, MAANU-NET: MULTI-LEVEL ATTENTION AND ATROUS PYRAMID NESTED U-NET FOR WRECKED OBJECTS SEGMENTATION IN FORWARD-LOOKING SONAR IMAGES, MACHINE LEARNING BASED EFFICIENT QT-MTT PARTITIONING FOR VVC INTER CODING, MANet: Improving Video Denoising with a Multi-Alignment Network, MANet: Mitral Annulus Point Tracking Network in Cardiac Magnetic Resonance, Mapping functional changes in the embryonic heart of Atlantic salmon post viral infection using AI technique, MASK GUIDED SPATIAL-TEMPORAL FUSION NETWORK FOR MULTIPLE OBJECT TRACKING, MASKED FACE RECOGNITION VIA SELF-ATTENTION BASED LOCAL CONSISTENCY REGULARIZATION, MASKFORMER WITH IMPROVED ENCODER-DECODER MODULE FOR SEMANTIC SEGMENTATION OF FINE-RESOLUTION REMOTE SENSING IMAGES, MASK-GUIDED ATTENTION AND EPISODE ADAPTIVE WEIGHTS FOR FEW-SHOT SEGMENTATION, MASK-VIT: AN OBJECT MASK EMBEDDING IN VISION TRANSFORMER FOR FINE-GRAINED VISUAL CLASSIFICATION, MAXIMUM LIKELIHOOD SURFACE PROFILOMETRY VIA OPTICAL COHERENCE TOMOGRAPHY, MCFM: MUTUAL CROSS FUSION MODULE FOR INTERMEDIATE FUSION-BASED ACTION SEGMENTATION, MCGNET: MULTI-LEVEL CONTEXT-AWARE AND GEOMETRIC-AWARE NETWORK FOR 3D OBJECT DETECTION, MDNET: MOTION DISTINCTION NETWORK FOR EFFECTIVE ACTION RECOGNITION, MEASURABLY STRONGER EXPLANATION RELIABILITY VIA MODEL CANONIZATION, MEASURING CLASS-IMBALANCE SENSITIVITY OF DETERMINISTIC PERFORMANCE EVALUATION METRICS, MEMORY REDUCTION OF CGH CALCULATION BASED ON INTEGRATING POINT LIGHT SOURCES, MEMORY-EFFICIENT LEARNED IMAGE COMPRESSION WITH PRUNED HYPERPRIOR MODULE, MERGED U-NET FOR BONE TUMORS X-RAY IMAGES SEGMENTATION, META-BNS FOR ADVERSARIAL DATA-FREE QUANTIZATION, META-LEARNED INITIALIZATION FOR 3D HUMAN RECOVERY, MICROSCALE IMAGE ENHANCEMENT VIA PCA AND WELL-EXPOSEDNESS MAPS, MinConvNets: A new class of multiplication-less Neural Networks, MIXED MEMBERSHIP GENERATIVE ADVERSARIAL NETWORKS, MIXTURE OF TEACHER EXPERTS FOR SOURCE-FREE DOMAIN ADAPTIVE OBJECT DETECTION, MIXUP-BASED DEEP METRIC LEARNING APPROACHES FOR INCOMPLETE SUPERVISION, MLP-Stereo: Heterogeneous Feature Fusion in MLP for Stereo Matching, MLSA-UNET: END-TO-END MULTI-LEVEL SPATIAL ATTENTION GUIDED UNET FOR INDUSTRIAL DEFECT SEGMENTATION, MLS-GAN: MULTI-LEVEL SEMANTIC GUIDED IMAGE COLORIZATION, MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation, MMSR: MULTIPLE-MODEL LEARNED IMAGE SUPER-RESOLUTION BENEFITING FROM CLASS-SPECIFIC IMAGE PRIORS, MODEL ATTRIBUTION OF FACE-SWAP DEEPFAKE VIDEOS, MODELING THE HEVC ENCODING ENERGY USING THE ENCODER PROCESSING TIME, MODULAR AND LIGHTWEIGHT NETWORKS FOR BI-SCALE STYLE TRANSFER, MONET: MULTI-SCALE OVERLAP NETWORK FOR DUPLICATION DETECTION IN BIOMEDICAL IMAGES, Monitoring of Varroa Infestation rate in Beehives: A Simple AI Approach, MONO6D: MONOCULAR VEHICLE 6D POSE ESTIMATION WITH 3D PRIORS, MONOTONICALLY CONVERGENT REGULARIZATION BY DENOISING, MOUSE ARTERIAL WALL IMAGING AND ANALYSIS FROM SYNCHROTRON X-RAY MICROTOMOGRAPHY, Moving object detection in noisy video sequences using deep convolutional disentangled representations, MR. TOMP : INVERSION OF THE MODULO RADON TRANSFORM (MRT) VIA ORTHOGONAL MATCHING PURSUIT (OMP), MRS-XNET: AN EXPLAINABLE ONE-DIMENSIONAL DEEP NEURAL NETWORK FOR MAGNETIC SPECTROSCOPIC DATA CLASSIFICATION, MSL-FER: MIRRORED SELF-SUPERVISED LEARNING FOR FACIAL EXPRESSION RECOGNITION, MULTI LABEL IMAGE CLASSIFICATION USING ADAPTIVE GRAPH CONVOLUTIONAL NETWORKS (ML-AGCN), MULTI OBJECT TRACKING BASED ON UNCERTAINTY-AWARE RE-ID, MULTI-BRANCH TENSOR NETWORK STRUCTURE FOR TENSOR-TRAIN DISCRIMINANT ANALYSIS, Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding, MULTI-FIELD DE-INTERLACING USING DEFORMABLE CONVOLUTION RESIDUAL BLOCKS AND SELF-ATTENTION, Multifractal anomaly detection in images via space-scale surrogates, Multi-frame Video Prediction with Learnable Motion Encodings, Multi-granularity Aggregation Transformer for Light Field Image Super-Resolution, MULTI-LABEL AERIAL IMAGE CLASSIFICATION BASED ON IMAGE-SPECIFIC CONCEPT GRAPHS, MULTI-LATENT GAN INVERSION FOR UNSUPERVISED 3D SHAPE COMPLETION, MULTI-MODAL TRANSFORMER FOR RGB-D SALIENT OBJECT DETECTION, MULTI-MODALITY DIVERSITY FUSION NETWORK WITH SWINTRANSFORMER FOR RGB-D SALIENT OBJECT DETECTION, MULTIPLE-PHASES-SECTIONALIZED-MODULATION SAR BARRAGE JAMMING METHOD BASED ON NLFM SIGNAL, MULTI-SCALE DEFORMABLE TRANSFORMER ENCODER BASED SINGLE-STAGE PEDESTRIAN DETECTION, MULTI-SCALE END-TO-END LEARNING FOR POINT CLOUD GEOMETRY COMPRESSION, MULTI-SCALE RAFT: COMBINING HIERARCHICAL CONCEPTS FOR LEARNING-BASED OPTICAL FLOW ESTIMATION, MULTI-SCALE TRANSFORMER-BASED FEATURE COMBINATION FOR IMAGE RETRIEVAL, MULTI-STAGE FEATURE ALIGNMENT NETWORK FOR VIDEO SUPER-RESOLUTION, MULTI-STEP TEST-TIME ADAPTATION WITH ENTROPY MINIMIZATION AND PSEUDO-LABELING, Multitask learning via pseudo-label generation and ensemble prediction for parasitic egg cell detection: IEEE ICIP Challenge 2022, MULTI-VIEW 3D RECONSTRUCTION FROM VIDEO WITH TRANSFORMER, MULTI-VIEW FEATURE BOOSTING NETWORK FOR DEEP SUBSPACE CLUSTERING, MVMO: A MULTI-OBJECT DATASET FOR WIDE BASELINE MULTI-VIEW SEMANTIC SEGMENTATION, MWNET: A TRACKING METHOD FOR FREQUENTLY OCCLUDED SCENES BASED ON MATTER WAVES, Natural Image Matting with Shifted Window self-Attention, NBD-GAP: NON-BLIND IMAGE DEBLURRING WITHOUT CLEAN TARGET IMAGES, NCTR: NEIGHBORHOOD CONSENSUS TRANSFORMER FOR FEATURE MATCHING, Neural Architecture Search For Fracture Classification, NEURAL NETWORK FRAGILE WATERMARKING WITH NO MODEL PERFORMANCE DEGRADATION, Neuro-Inspired Deep Neural Networks with Sparse, Strong Activations, NEW ACTIVE LEARNING APPROACH FOR SEABED SEGMENTATION, NLCMAP: A FRAMEWORK FOR THE EFFICIENT MAPPING OF NON-LINEAR CONVOLUTIONAL NEURAL NETWORKS ON FPGA ACCELERATORS, Nondeterministic Deformation analysis using Quasiconformal Geometry, NON-DETERMINISTIC FACE MASK REMOVAL BASED ON 3D PRIORS, NON-ITERATIVE OPTIMIZATION OF PSEUDO-LABELING THRESHOLDS FOR TRAINING OBJECT DETECTION MODELS FROM MULTIPLE DATASETS, NONLINEAR ORTHOGONAL NMF ON THE STIEFEL MANIFOLD WITH GRAPH-BASED TOTAL VARIATION REGULARIZATION, NON-RIGID MULTIPLE POINT SET REGISTRATION USING LATENT GAUSSIAN MIXTURE, Non-separable Filtering with Side-Information and Contextually-Designed Filters for Next Generation Video Codecs, NOVEL RECONSTRUCTION WITH INTER-FRAME MOTION COMPENSATION FOR FAST SUPER-RESOLUTION LIVE CELL IMAGING, NPCFORMER: AUTOMATIC NASOPHARYNGEAL CARCINOMA SEGMENTATION BASED ON BOUNDARY ATTENTION AND GLOBAL POSITION CONTEXT ATTENTION, Object-Aware Self-supervised Multi-Label Learning, OBJECT-CENTRIC AND MEMORY-GUIDED NORMALITY RECONSTRUCTION FOR VIDEO ANOMALY DETECTION, Occlusion-invariant Representation Alignment for Entity Re-identification, OCTA RETINAL VESSEL SEGMENTATION BASED ON VESSEL THICKNESS INCONSISTENCY LOSS, OMNET: REAL-TIME STEREO MATCHING WITH UNSUPERVISED OCCLUSION MASK, OMNIDIRECTIONAL VIDEO QUALITY INDEX ACCOUNTING FOR JUDDER, ON ADVERSARIAL ROBUSTNESS OF DEEP IMAGE DEBLURRING, ON MONOCULAR DEPTH ESTIMATION AND UNCERTAINTY QUANTIFICATION USING CLASSIFICATION APPROACHES FOR REGRESSION, ON QUANTIZATION OF IMAGE CLASSIFICATION NEURAL NETWORKS FOR COMPRESSION WITHOUT RETRAINING, On the accuracy of open video quality metrics for local decision in AV1 video codec, ON THE BENEFIT OF PARAMETER-DRIVEN APPROACHES FOR THE MODELING AND THE PREDICTION OF SATISFIED USER RATIO FOR COMPRESSED VIDEO, ON THE LIMITS OF PERCEPTUAL QUALITY MEASURES FOR ENHANCED UNDERWATER IMAGES, ON THE LINK BETWEEN EMOTION, ATTENTION AND CONTENT IN VIRTUAL IMMERSIVE ENVIRONMENTS, ON THE RELEVANCE OF MULTI-GRAPH MATCHING FOR SULCAL GRAPHS, ONE-CYCLE PRUNING: PRUNING CONVNETS WITH TIGHT TRAINING BUDGET, ONLINE ADAPTIVE PERSONALIZATION FOR FACE ANTI-SPOOFING, OPEN-WORLD OBJECT DETECTION VIA DISCRIMINATIVE CLASS PROTOTYPE LEARNING, OPTICS LENS DESIGN FOR PRIVACY-PRESERVING SCENE CAPTIONING, Optimal Fractional Fourier Filtering for Graph Signals, OPTIMAL NOISE-AWARE IMAGING WITH SWITCHABLE PREFILTERS, Optimal transport with a new preprocessing for deep-learning full waveform inversion, OPTIMAL TRANSPORT-BASED GRAPH MATCHING FOR 3D RETINAL OCT IMAGE REGISTRATION, OPTIMIZED DECODING-ENERGY-AWARE ENCODING IN PRACTICAL VVC IMPLEMENTATIONS, OPTIMIZED LEARNED ENTROPY CODING PARAMETERS FOR PRACTICAL NEURAL-BASED IMAGE AND VIDEO COMPRESSION, OPTIMIZING AV1 ENCODER FOR REAL-TIME COMMUNICATION, ORTHONORMAL CONVOLUTIONS FOR THE ROTATION BASED ITERATIVE GAUSSIANIZATION, OSEGNET: OPERATIONAL SEGMENTATION NETWORK FOR COVID-19 DETECTION USING CHEST X-RAY IMAGES, PAIRWISE ROTATIONAL-DIFFERENCE LBP FOR FINE-GRAINED LEAF IMAGE RETRIEVAL, Panoptic-DeepLab-DVA: Improving Panoptic DeepLab with Dual Value Attention and instance Boundary Aware Regression, PANORAMIC VIEWPORT PREDICTION RELYING ON EMOTIONAL ATTENTION MAP, PARALLEL ATTRIBUTE COMPUTATION FOR DISTRIBUTED COMPONENT FORESTS, PARALLEL PARTITIONING: PATH REDUCING AND UNIONFIND BASED WATERSHED FOR THE GPU, PARASITIC EGG DETECTION AND CLASSIFICATION BY UTILIZING THE YOLO ALGORITHM WITH DEEP LATENT SPACE IMAGE RESTORATION AND GRABCUT AUGMENTATION, Parasitic Egg Detection and Classification with Transformer-based architectures, PARASITIC EGG DETECTION WITH A DEEP LEARNING ENSEMBLE, Partial Point Cloud Registration via Soft Segmentation, Partition and Reunion: A Viewpoint-Aware Loss for Vehicle Re-identification, PASNet: A self-adaptive point cloud sorting approach to an improved feature extraction, PASTS: TOWARD EFFECTIVE DISTILLING TRANSFORMER FOR PANORAMIC SEMANTIC SEGMENTATION, PATIENT AWARE ACTIVE LEARNING FOR FINE-GRAINED OCT CLASSIFICATION, PCA Event-Based Optical Flow: A Fast and Accurate 2D Motion Estimation, PCRP: UNSUPERVISED POINT CLOUD OBJECT RETRIEVAL AND POSE ESTIMATION, PDE-CONSTRAINED OPTIMIZATION FOR NUCLEAR MECHANICS, PERCEPTION-DISTORTION TRADE-OFF IN THE SR SPACE SPANNED BY FLOW MODELS, PERSON RE-IDENTIFICATION IN PANORAMIC VIEWS BASED ON BAYESIAN TRANSFORMERS, PET/CT CO-SEGMENTATION BASED ON HYBRID ACTIVE CONTOUR MODEL, P-FRAME CODING WITH GENERALIZED DIFFERENCE: A NOVEL CONDITIONAL CODING APPROACH, PGTNet: Prototype Guided Transfer Network for Few-shot Anomaly Localization, PGUNET: COVID-19 CT IMAGE SEGMENTATION USING GAN AND FEATURE PYRAMID, POINT CLOUD COMPLETION BY MINIMIZING PREDICTION ERRORS IN BOTH 2D AND 3D SPACES, POINTIVAE: INVERTIBLE VARIATIONAL AUTOENCODER FRAMEWORK FOR 3D POINT CLOUD GENERATION, Polygon-free: Unconstrained Scene Text Detection with Box Annotations, POSE CALIBRATED FEATURE AGGREGATION FOR FACE SET RECOGNITION, POSITIVE UNLABELED LEARNING BY SEMI-SUPERVISED LEARNING, PPT: ANOMALY DETECTION DATASET OF PRINTED PRODUCTS WITH TEMPLATES, PRACTICAL BULK DENOISING OF LARGE BINARY IMAGES, PREDICTING HUMAN PERCEPTION OF SCENE COMPLEXITY, PREDICTING PATH LOSS DISTRIBUTIONS OF A WIRELESS COMMUNICATION SYSTEM FOR MULTIPLE BASE STATION ALTITUDES FROM SATELLITE IMAGES, PREDICTING RADIOLOGIST ATTENTION DURING MAMMOGRAM READING WITH DEEP AND SHALLOW HIGH-RESOLUTION ENCODING, PREDICTING SOIL PROPERTIES FROM HYPERSPECTRAL SATELLITE IMAGES, Predicting the colors of reference surfaces for color constancy, PRIOR SEMANTIC HARMONIZATION NETWORK FOR FEW-SHOT SEMANTIC SEGMENTATION, PROBABILITY MODEL ESTIMATION FOR M-ARY RANDOM VARIABLES, PROBING SEISMOGENIC FAULTS WITH MACHINE LEARNING, PROGRESSIVE TRAINING ENABLED FINE-GRAINED RECOGNITION, PROHIBITED OBJECT DETECTION IN X-RAY IMAGES WITH DYNAMIC DEFORMABLE CONVOLUTION AND ADAPTIVE IOU, PROTOTYPE QUEUE LEARNING FOR MULTI-CLASS FEW-SHOT SEMANTIC SEGMENTATION, ProX: A REVERSED ONCE-FOR-ALL NETWORK TRAINING PARADIGM FOR EFFICIENT EDGE MODELS TRAINING IN MEDICAL IMAGING, PURE VERSUS HYBRID TRANSFORMERS FOR MULTI-MODAL BRAIN TUMOR SEGMENTATION: A COMPARATIVE STUDY, Pyramid Knowledge Distillation for Efficient Human Pose Estimation, Quadtree-based Guided CNN for AV1 In-loop Filtering, Quality evaluation of holographic images coded with standard codecs, Query Learning of Both Thing and Stuff for Panoptic Segmentation, Query-Efficient Adversarial Attack Based on Latin Hypercube Sampling, QUES-TO-VISUAL GUIDED VISUAL QUESTION ANSWERING, RAIN-PRIOR INJECTED KNOWLEDGE DISTILLATION FOR SINGLE IMAGE DERAINING, Random Generated Dictionaries for Convolutional Sparse Coding: An ELM interpretation for simple CSC applications, REAL- AND COMPLEX-VALUED NEURAL NETWORKS FOR SAR IMAGE SEGMENTATION THROUGH DIFFERENT POLARIMETRIC REPRESENTATIONS, REAL-TIME COMPUTATION OF 3D WIREFRAMES IN COMPUTER-GENERATED HOLOGRAPHY, REAL-WORLD IMAGE SUPER-RESOLUTION VIA KERNEL AUGMENTATION AND STOCHASTIC VARIATION, REAL-WORLD VIDEO ANOMALY DETECTION BY EXTRACTING SALIENT FEATURES, RECOGNITION-AWARE DEEP VIDEO COMPRESSION FOR REMOTE SURVEILLANCE, RECURRENT ATTENTIVE DECOMPOSITION NETWORK FOR LOW-LIGHT IMAGE ENHANCEMENT, REDUCED DEPENDENCY FAST UNSUPERVISED 3D FACE RECONSTRUCTION, REFERENCE GUIDED REFLECTION REMOVAL USING DEEP VISUAL ATTRIBUTE CUES, REFERENCE-BASED BLIND SUPER-RESOLUTION KERNEL ESTIMATION, REFERENCE-BASED JPEG IMAGE ARTIFACTS REMOVAL, REFERENCE-GUIDED TEXTURE AND STRUCTURE INFERENCE FOR IMAGE INPAINTING, Refining Self-Supervised Learning in Imaging: Beyond Linear Metric, REGIONAL SALIENCY MAP ATTACK FOR MEDICAL IMAGE SEGMENTATION, REGION-OF-INTEREST CODING SCHEMES FOR HTTP ADAPTIVE STREAMING WITH VVC, REINFORCING NEURON EXTRACTION FROM CALCIUM IMAGING DATA VIA DEPTH-ESTIMATION CONSTRAINED NONNEGATIVE MATRIX FACTORIZATION, RELATION ENHANCED VISION LANGUAGE PRE-TRAINING, Relational Future Captioning Model for Explaining Likely Collisions in Daily Tasks, RELATION-GUIDED NETWORK FOR IMAGE-TEXT RETRIEVAL, REPNP: PLUG-AND-PLAY WITH DEEP REINFORCEMENT LEARNING PRIOR FOR ROBUST IMAGE RESTORATION, Representation Learning Optimization for 3D Point Cloud Quality Assessment Without Reference, REPRESENTATION LEARNING USING RANK LOSS FOR ROBUST NEUROSURGICAL SKILLS EVALUATION, REPRODUCING SENSORY INDUCED HALLUCINATIONS VIA NEURAL FIELDS, RESIDUAL GRAPH ATTENTION NETWORK AND EXPRESSION-RESPECT DATA AUGMENTATION AIDED VISUAL GROUNDING, RESIDUAL SWIN TRANSFORMER UNET WITH CONSISTENCY REGULARIZATION FOR AUTOMATIC BREAST ULTRASOUND TUMOR SEGMENTATION, Residual U-Structure Nested Conditional Adversarial Nets Colorized CT Improves Deep Learning Based Abdominal Multi-Organ Segmentation, RETHINKING EFFICACY OF SOFTMAX FOR LIGHTWEIGHT NON-LOCAL NEURAL NETWORKS, RETHINKING UNIFIED SPECTRAL-SPATIAL-BASED HYPERSPECTRAL IMAGE CLASSIFICATION UNDER 3D CONFIGURATION OF VISION TRANSFORMER, RETHINKING UNSUPERVISED NEURAL SUPERPIXEL SEGMENTATION, Retina-inspired spatio-temporal filtering for dynamic video coding, REVERSE ERROR MODELING FOR IMPROVED SEMANTIC SEGMENTATION, Revisiting Artistic Style Transfer for Data Augmentation in a Real-case Scenario, REVISITING CLICK-BASED INTERACTIVE VIDEO OBJECT SEGMENTATION, Revisiting Dead Leaves Model: Training With Synthetic Data, REVISITING NATURAL SCENE STATISTICAL MODELING USING DEEP FEATURES FOR OPINION-UNAWARE IMAGE QUALITY ASSESSMENT, REVISITING SPATIAL INDUCTIVE BIAS WITH MLP-LIKE MODEL, REVISITING THE EFFICIENCY OF UGC VIDEO QUALITY ASSESSMENT, REVIVING ITERATIVE TRAINING WITH MASK GUIDANCE FOR INTERACTIVE SEGMENTATION, RIChEx: A ROBUST INTER-FRAME CHANGE EXPOSURE FOR SEGMENTING MOVING OBJECTS, ROBUST 3D CELL SEGMENTATION: EXTENDING THE VIEW OF CELLPOSE, ROBUST AND ACCURATE OBJECT DETECTION VIA SELF-KNOWLEDGE DISTILLATION, Robust calibration-marker and laser-line detection for underwater 3D shape reconstruction by Deep Neural Network, ROBUST GRID DETECTION IN HISTORICAL MAP IMAGES, Robust Temporally Coherent Strategy for Few-shot Video Instance Segmentation, ROTATION-EQUIVARIANT GRAPH CONVOLUTIONAL NETWORKS FOR SPHERICAL DATA VIA GLOBAL-LOCAL ATTENTION, RPFNET: COMPLEMENTARY FEATURE FUSION FOR HAND GESTURE RECOGNITION, RW-HAZE: A REAL-WORLD BENCHMARK DATASET TO EVALUATE QUANTITATIVELY DEHAZING ALGORITHMS, RWN: Robust Watermarking Network for Image Cropping Localization, SALIENCY DETECTION VIA GLOBAL CONTEXT ENHANCED FEATURE FUSION AND EDGE WEIGHTED LOSS, SAR IMAGE SUPER-RESOLUTION RECONSTRUCTION BASED ON FULL-RESOLUTION DISCRIMINATION, SAT: SELF-ADAPTIVE TRAINING FOR FASHION COMPATIBILITY PREDICTION, SATELLITE IMAGE CHANGE DETECTION USING DISJOINT INFORMATION AND LOCAL DISSIMILARITY MAP, SAVE: Spatial-Attention Visual Exploration, SCANPATH PREDICTION VIA SEMANTIC REPRESENTATION OF THE SCENE, SCENE CONTEXT ENHANCED NETWORK FOR PERSON SEARCH, SCENE REPRESENTATION LEARNING FROM VIDEOS USING SELF-SUPERVISED AND WEAKLY-SUPERVISED TECHNIQUES, SCINET: SEMANTIC CUE INFUSION NETWORK FOR LANE DETECTION, SEGMENTATION-FREE SUPER-RESOLVED 4D FLOW MRI RECONSTRUCTION EXPLOITING NAVIER-STOKES EQUATIONS AND SPATIAL REGULARIZATION, Self-SuperFlow: Self-supervised Scene Flow Prediction in Stereo Sequences, SELF-SUPERVISED CLASS-COGNIZANT FEW-SHOT CLASSIFICATION, SELF-SUPERVISED COOPERATIVE COLORIZATION OF ACHROMATIC FACES, SELF-SUPERVISED DOMAIN ADAPTATION IN CROWD COUNTING, SELF-SUPERVISED FRONTALIZATION AND ROTATION GAN WITH RANDOM SWAP FOR POSE-INVARIANT FACE RECOGNITION, SELF-SUPERVISED LEARNING FOR TEXTURE CLASSIFICATION USING LIMITED LABELED DATA, SELF-SUPERVISED LEARNING OF OPTICAL FLOW, DEPTH, CAMERA POSE AND RIGIDITY SEGMENTATION WITH OCCLUSION HANDLING, SELF-SUPERVISED PRETRAINING FOR DEEP HASH-BASED IMAGE RETRIEVAL, SEMANTIC ALIGNMENT FOR MULTI-ITEM COMPRESSION, SEMANTIC UNFOLDING OF STYLEGAN LATENT SPACE, SEMI-OVERCOMPLETE CONVOLUTIONAL AUTO-ENCODER EMBEDDING AS SHAPE PRIORS FOR DEEP VESSEL SEGMENTATION, SEMI-SUPERVISED 3D MEDICAL IMAGE SEGMENTATION VIA BOUNDARY-AWARE CONSISTENT HIDDEN REPRESENTATION LEARNING, Semi-supervised Deep Convolutional Transform Learning for Hyperspectral Image Classification, SEMI-SUPERVISED RANKING FOR OBJECT IMAGE BLUR ASSESSMENT, SEQDNET: IMPROVING MISSING VALUE BY SEQUENTIAL DEPTH NETWORK, SEQUENTIAL CROSS ATTENTION BASED MULTI-TASK LEARNING, SEVERITY CLASSIFICATION IN CASES OF COLLAGEN VI-RELATED MYOPATHY WITH CONVOLUTIONAL NEURAL NETWORKS AND HANDCRAFTED TEXTURE FEATURES, SFIC: SPARSITY-DRIVEN FACIAL IMAGE COMPRESSION NETWORK, SHUFFLE ATTENTION MULTIPLE INSTANCES LEARNING FOR BREAST CANCER WHOLE SLIDE IMAGE CLASSIFICATION, Sign-OPT+: An improved sign optimization adversarial attack, Simulator Attack+ For Black-box Adversarial Attack, SIMULTANEOUS LEARNING AND COMPRESSION FOR CONVOLUTION NEURAL NETWORKS, SIMULTANEOUS SMOOTHING AND SHARPENING USING iWGIF, SIMURGH: A Framework for CAD-Driven Deep Learning Based X-ray CT Reconstruction, SINGLE IMAGE DEHAZING VIA MODEL-BASED DEEP-LEARNING, Single Image Reflection Removal based on Bi-Channels Prior, SKIP-MLP NETWORK FOR POINT CLOUD CLASSIFICATION, SLICING AIDED HYPER INFERENCE AND FINE-TUNING FOR SMALL OBJECT DETECTION, SLTFill: Spatial and Light Transformer for Multi-Reference Image Inpainting, SMART LEARNING OF CLICK AND REFINE FOR NUCLEI SEGMENTATION ON HISTOLOGY IMAGES, SOLI RADAR IMAGE-BASED TARGET LOCALIZATION, SPATIAL MOMENT POOLING IMPROVES NEURAL IMAGE ASSESSMENT, SPATIAL SENSITIVE GRAD-CAM: VISUAL EXPLANATIONS FOR OBJECT DETECTION BY INCORPORATING SPATIAL SENSITIVITY, SPATIAL-SEMANTIC ATTENTION FOR GROUNDED IMAGE CAPTIONING, SPATIO-TEMPORAL ATTENTION GRAPH FOR MONOCULAR 3D HUMAN POSE ESTIMATION, SPATIO-TEMPORAL PARALLELIZATION SCHEME FOR HEVC ENCODING ON MULTI-COMPUTER SYSTEMS, SPEAKER EXTRACTION WITH CO-SPEECH GESTURES CUE, SRK-NET: LEARNING TO DETECT REPEATABLE KEYPOINTS WITH LOCAL SALIENCY KNOWLEDGE, SRL-SOA: SELF-REPRESENTATION LEARNING WITH SPARSE 1D-OPERATIONAL AUTOENCODER FOR HYPERSPECTRAL IMAGE BAND SELECTION, SSP-REGULARIZER: A STAR SHAPE PRIOR BASED REGULARIZER FOR VESSEL LUMEN SEGMENTATION IN OCT IMAGES, STABLE CLUSTERING ENSEMBLE BASED ON EVIDENCE THEORY, STACKING MORE LINEAR OPERATIONS WITH ORTHOGONAL REGULARIZATION TO LEARN BETTER, StarVQA: Space-Time Attention for Video Quality Assessment, STATISTICAL ANALYSIS OF INTER CODING IN VVC TEST MODEL (VTM), Stochastic Binary-Ternary Quantization for Communication Efficient Federated Computation, STREAMING-CAPABLE HIGH-PERFORMANCE ARCHITECTURE OF LEARNED IMAGE COMPRESSION CODECS, STRONG-WEAK INTEGRATED SEMI-SUPERVISION FOR UNSUPERVISED DOMAIN ADAPTATION, STRUCTURED DROPCONNECT FOR UNCERTAINTY INFERENCE IN IMAGE CLASSIFICATION, STYLE TRANSFER USING OPTIMAL TRANSPORT VIA WASSERSTEIN DISTANCE, SUB-APERTURE FEATURE ADAPTATION IN SINGLE IMAGE SUPER-RESOLUTION MODEL FOR LIGHT FIELD IMAGING, Subjective and Objective Quality Assessment of High-Motion Sports Videos at Low-Bitrates, SUBJECTIVE ASSESSMENT OF HIGH DYNAMIC RANGE VIDEOS UNDER DIFFERENT AMBIENT CONDITIONS, SUBJECTIVE QUALITY EVALUATION OF POINT CLOUDS WITH 3D STEREOSCOPIC VISUALIZATION, SUB-PIXEL OPTICAL SATELLITE IMAGE REGISTRATION FOR GROUND DEFORMATION USING DEEP LEARNING, SUBSPACE MODELING FOR FAST OUT-OF-DISTRIBUTION AND ANOMALY DETECTION, SUPERPIXEL GROUP-CORRELATION NETWORK FOR CO-SALIENCY DETECTION, Super-resolution Magnetic Resonance Imaging Using Segmented Signals in Phase-Scrambling Fourier Transform Imaging and Deep Learning, SUPERVISING REMOTE SENSING CHANGE DETECTION MODELS WITH 3D SURFACE SEMANTICS, Surveillance Video Quality Assessment Based on Quality Related Retraining, SVBR-NET: A NON-BLIND SPATIALLY VARYING DEFOCUS BLUR REMOVAL NETWORK, SVG VECTOR FONT GENERATION FOR CHINESE CHARACTERS WITH TRANSFORMER, SWIS: SELF-SUPERVISED REPRESENTATION LEARNING FOR WRITER INDEPENDENT OFFLINE SIGNATURE VERIFICATION, SWITCHABLE CNN-BASED SAME-RESOLUTION AND SUPER-RESOLUTION IN-LOOP RESTORATION FOR NEXT GENERATION VIDEO CODECS, SYGNET: A SVD-YOLO BASED GHOSTNET FOR REAL-TIME DRIVING SCENE PARSING, SYNCHRONIZED AUDIO-VISUAL FRAMES WITH FRACTIONAL POSITIONAL ENCODING FOR TRANSFORMERS IN VIDEO-TO-TEXT TRANSLATION, Task-aware Few-shot Visual Classification with Improved Self-supervised Metric Learning, TASK-DRIVEN SELF-SUPERVISED BI-CHANNEL NETWORKS LEARNING FOR DIAGNOSIS OF BREAST CANCERS WITH MAMMOGRAPHY, Taxonomy Driven Learning of Semantic Hierarchy of Classes, TEMPORAL AXIAL ATTENTION FOR LIDAR-BASED 3D OBJECT DETECTION IN AUTONOMOUS DRIVING, TEMPORAL FLOW MASK ATTENTION FOR OPEN-SET LONG-TAILED RECOGNITION OF WILD ANIMALS IN CAMERA-TRAP IMAGES, TEMPORALLY PRECISE ACTION SPOTTING IN SOCCER VIDEOS USING DENSE DETECTION ANCHORS, TENSOR-BASED DEEPFAKE DETECTION IN SCALED AND COMPRESSED IMAGES, TEXTURE-GUIDED END-TO-END DEPTH MAP COMPRESSION, THE BRIO-TA DATASET: UNDERSTANDING ANOMALOUS ASSEMBLY PROCESS IN MANUFACTURING, THE EFFECT OF SPATIAL AND TEMPORAL OCCLUSION ON WORD LEVEL SIGN LANGUAGE RECOGNITION, THE HYPERVIEW CHALLENGE: ESTIMATING SOIL PARAMETERS FROM HYPERSPECTRAL IMAGES, THE LIFECYCLE OF A NEURAL NETWORK IN THE WILD: A MULTIPLE INSTANCE LEARNING STUDY ON CANCER DETECTION FROM BREAST BIOPSIES IMAGED WITH NOVEL TECHNIQUE, THE RISE OF THE LOTTERY HEROES: WHY ZERO-SHOT PRUNING IS HARD, THERMAL TO VISIBLE IMAGE SYNTHESIS UNDER ATMOSPHERIC TURBULENCE, TOPOLOGICALLY-CONSISTENT MAGNITUDE PRUNING FOR VERY LIGHTWEIGHT GRAPH CONVOLUTIONAL NETWORKS, TOWARD SNOW REMOVAL VIA THE DIVERSITY AND COMPLEXITY OF SNOW IMAGE, TOWARDS A UNIFIED VIEW OF UNSUPERVISED NON-LOCAL METHODS FOR IMAGE DENOISING: THE NL-RIDGE APPROACH, TOWARDS EFFICIENT VARIATIONAL AUTO-ENCODER USING WASSERSTEIN DISTANCE, TOWARDS GENERALIZABLE DEEPFAKE FACE FORGERY DETECTION WITH SEMI-SUPERVISED LEARNING AND KNOWLEDGE DISTILLATION, Towards Lightweight Neural Network-based Chroma Intra Prediction for Video Coding, TOWARDS MODEL QUANTIZATION ON THE RESILIENCE AGAINST MEMBERSHIP INFERENCE ATTACKS, TOWARDS ZERO-LATENCY VIDEO TRANSMISSION THROUGH FRAME EXTRAPOLATION, TRAINING STRATEGY FOR LIMITED LABELED DATA BY LEARNING FROM CONFUSION, TRAJECTORY-BASED PATTERN OF LIFE ANALYSIS, Transform Skip Inspired End-to-End Compression for Screen Content Image, TRANSFORMATION-BASED ADVERSARIAL DEFENSE VIA SPARSE REPRESENTATION, TRANSFORMER BASED SELF-CONTEXT AWARE PREDICTION FOR FEW-SHOT ANOMALY DETECTION IN VIDEOS, TRANSFORMER COMPRESSED SENSING VIA GLOBAL IMAGE TOKENS, TRANSFORMER VISUAL TRACKER BASED ON TEMPLATE FEATURES CORRESPONDING TO FOREGROUND REGION, TRANSFORMER-BASED APPROACH FOR DOCUMENT LAYOUT UNDERSTANDING, TRANSFORMERS FOR WORKOUT VIDEO SEGMENTATION, Translated Skip Connections - Expanding the Receptive Fields of Fully Convolutional Neural Networks, TRANSLATION OF ILLUSTRATION ARTIST STYLE USING SAILORMOONREDRAW DATA, TRELLIS-CODED QUANTIZATION FOR END-TO-END LEARNED IMAGE COMPRESSION, TRUNCATED LOTTERY TICKET FOR DEEP PRUNING, TUNING NEURAL ODE NETWORKS TO INCREASE ADVERSARIAL ROBUSTNESS IN IMAGE FORENSICS, Two distillation perspectives based on Tanimoto coefficient, TWO-STEP COLOR-POLARIZATION DEMOSAICKING NETWORK, TWO-STREAM NON-UNIFORM CONCENTRATION REASONING NETWORK FOR SINGLE IMAGE AIR POLLUTION ESTIMATION, U-DEEPDIG: SCALABLE DEEP DECISION BOUNDARY INSTANCE GENERATION, UNBIASED VALIDATION OF THE ALGORITHMS FOR AUTOMATIC NEEDLE LOCALIZATION IN ULTRASOUND-GUIDED BREAST BIOPSIES, UNCERTAINTY AWARE MULTITASK PYRAMID VISION TRANSFORMER FOR UAV-BASED OBJECT RE-IDENTIFICATION, UNDERSAMPLED DYNAMIC FOURIER PTYCHOGRAPHY VIA PHASELESS PCA, UNROLLING GRAPH TOTAL VARIATION FOR LIGHT FIELD IMAGE DENOISING, UNSUPERVISED AND ADAPTIVE PERIMETER INTRUSION DETECTOR, UNSUPERVISED ANOMALY DETECTION WITH SELF-TRAINING AND KNOWLEDGE DISTILLATION, UNSUPERVISED DOMAIN ADAPTATION PERSON RE-IDENTIFICATION BY CAMERA-AWARE STYLE DECOUPLING AND UNCERTAINTY MODELING, UNSUPERVISED DOMAIN-ADAPTIVE PERSON RE-IDENTIFICATION WITH MULTI-CAMERA CONSTRAINTS, UNSUPERVISED GENERATIVE NETWORK FOR BLIND HYPERSPECTRAL IMAGE SUPER-RESOLUTION, UNSUPERVISED GENERATIVE VARIATIONAL CONTINUAL LEARNING, UNSUPERVISED IMAGE FUSION USING DEEP IMAGE PRIORS, UNSUPERVISED MULTI-TASK LEARNING FOR 3D SUBTOMOGRAM IMAGE ALIGNMENT, CLUSTERING AND SEGMENTATION, Unsupervised Point Cloud Pre-training via Contrasting and Clustering, UNSUPERVISED VIDEO SEGMENTATION ALGORITHMS BASED ON FLEXIBLY REGULARIZED MIXTURE MODELS, USAGE OF VEHICLE RE-IDENTIFICATION MODELS FOR IMPROVED PERSISTENT MULTIPLE OBJECT TRACKING IN WIDE AREA MOTION IMAGERY, USING DEEP LEARNING TO IMPROVE DETECTION AND DECODING OF BARCODES, USING VISION TRANSFORMERS IN 3-D MEDICAL IMAGE CLASSIFICATIONS, UTILITY AND FEASIBILITY OF A CENTER SURROUND EVENT CAMERA, UTILIZING EXCESS RESOURCES IN TRAINING NEURAL NETWORKS, VARIANCE-REDUCED RANDOMIZED KACZMARZ ALGORITHM IN XFEL SINGLE-PARTICLE IMAGING PHASE RETRIEVAL, VARIATIONAL DEPTH ESTIMATION ON HYPERSPHERE FOR PANORAMA, VCT-NET: AN OCTA RETINAL VESSEL SEGMENTATION NETWORK BASED ON CONVOLUTION AND TRANSFORMER, VEFNET: AN EVENT-RGB CROSS MODALITY FUSION NETWORK FOR VISUAL PLACE RECOGNITION, Ventriloquist-Net: Leveraging Speech Cues for Emotive Talking Head Generation, Vessel Segmentation and Dirt/Reflection Detection for Retinal Fundus Photographs, VG-GAN: CONDITIONAL GAN FRAMEWORK FOR GRAPHICAL DESIGN GENERATION, VIDEO SIGNAL-DEPENDENT NOISE ESTIMATION VIA INTER-FRAME PREDICTION, Video-Analytics Task-Aware Quad-Tree Partitioning and Quantization for HEVC, VIDEO-GROUNDED DIALOGUES WITH JOINT VIDEO AND IMAGE TRAINING, VIEWPORT-ORIENTED PANORAMIC IMAGE INPAINTING, VISUAL SENTIMENT PREDICTION USING CROSS-WAY FEW-SHOT LEARNING BASED ON KNOWLEDGE DISTILLATION, VISUAL SOUND SOURCE SEPARATION WITH PARTIAL SUPERVISION LEARNING, Visual Tempo Contrastive Learning for Few-shot Action Recognition, VITRANSPAD: VIDEO TRANSFORMER USING CONVOLUTION AND SELF-ATTENTION FOR FACE PRESENTATION ATTACK DETECTION, VLCap: Vision-Language with Contrastive Learning for Coherent Video Paragraph Captioning, WEIGHTED SUPERVISED CONTRASTIVE LEARNING AND DOMAIN MIXTURE FOR GENERALIZED PERSON RE-IDENTIFICATION.
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