{"total":77,"rows":[{"id":"2203.03570","title":"Kubric: A scalable dataset generator","created":"2022-03-07","cited_by_count":381,"area":null},{"id":"2306.08637","title":"TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement","created":"2023-06-14","cited_by_count":287,"area":null},{"id":"2211.03726","title":"TAP-Vid: A Benchmark for Tracking Any Point in a Video","created":"2022-11-07","cited_by_count":263,"area":null},{"id":"2204.04153","title":"Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories","created":"2022-04-08","cited_by_count":217,"area":null},{"id":"2306.05422","title":"Tracking Everything Everywhere All at Once","created":"2023-06-08","cited_by_count":193,"area":null},{"id":"2405.16009","title":"Streaming Long Video Understanding with Large Language Models","created":"2024-05-25","cited_by_count":167,"area":null},{"id":"2305.14344","title":"Siamese Masked Autoencoders","created":"2023-05-23","cited_by_count":76,"area":null},{"id":"2403.14548","title":"DINO-Tracker: Taming DINO for Self-Supervised Point Tracking in a Single Video","created":"2024-03-21","cited_by_count":64,"area":null},{"id":"2107.02156","title":"Do Different Tracking Tasks Require Different Appearance Models?","created":"2021-07-05","cited_by_count":62,"area":null},{"id":"2307.12698","title":"MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features","created":"2023-07-24","cited_by_count":57,"area":null},{"id":"2406.04221","title":"Matching Anything by Segmenting Anything","created":"2024-06-06","cited_by_count":50,"area":null},{"id":"2403.13042","title":"TAPTR: Tracking Any Point with Transformers as Detection","created":"2024-03-19","cited_by_count":45,"area":null},{"id":"2112.01515","title":"TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation","created":"2021-12-02","cited_by_count":38,"area":null},{"id":"2201.08379","title":"Learning Pixel Trajectories with Multiscale Contrastive Random Walks","created":"2022-01-20","cited_by_count":36,"area":null},{"id":"2203.14333","title":"Locality-Aware Inter-and Intra-Video Reconstruction for Self-Supervised Correspondence Learning","created":"2022-03-27","cited_by_count":26,"area":null},{"id":"2210.07199","title":"Self-Supervised Geometric Correspondence for Category-Level 6D Object Pose Estimation in the Wild","created":"2022-10-13","cited_by_count":22,"area":null},{"id":"2304.00571","title":"DropMAE: Learning Representations via Masked Autoencoders with Spatial-Attention Dropout for Temporal Matching Tasks","created":"2023-04-02","cited_by_count":22,"area":null},{"id":"2304.06211","title":"Boosting Video Object Segmentation via Space-time Correspondence Learning","created":"2023-04-13","cited_by_count":21,"area":null},{"id":"2210.06433","title":"Self-supervised video pretraining yields robust and more human-aligned visual representations","created":"2022-10-12","cited_by_count":20,"area":null},{"id":"2308.09951","title":"Semantics Meets Temporal Correspondence: Self-supervised Object-centric Learning in Videos","created":"2023-08-19","cited_by_count":20,"area":null},{"id":"2105.06463","title":"Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency","created":"2021-05-13","cited_by_count":19,"area":null},{"id":"2301.00394","title":"Deep Learning Technique for Human Parsing: A Survey and Outlook","created":"2023-01-01","cited_by_count":18,"area":null},{"id":"2203.16521","title":"CoordGAN: Self-Supervised Dense Correspondences Emerge from GANs","created":"2022-03-30","cited_by_count":17,"area":null},{"id":"2303.10100","title":"Unified Mask Embedding and Correspondence Learning for Self-Supervised Video Segmentation","created":"2023-03-17","cited_by_count":16,"area":null},{"id":"2112.05181","title":"Contextualized Spatio-Temporal Contrastive Learning with Self-Supervision","created":"2021-12-09","cited_by_count":15,"area":null},{"id":"2207.13492","title":"Time to augment self-supervised visual representation learning","created":"2022-07-27","cited_by_count":15,"area":null},{"id":"2501.12218","title":"Exploring Temporally-Aware Features for Point Tracking","created":"2025-01-21","cited_by_count":15,"area":null},{"id":"2210.00221","title":"Motion-inductive Self-supervised Object Discovery in Videos","created":"2022-10-01","cited_by_count":14,"area":null},{"id":"2303.12001","title":"ViC-MAE: Self-Supervised Representation Learning from Images and Video with Contrastive Masked Autoencoders","created":"2023-03-21","cited_by_count":14,"area":null},{"id":"2207.10456","title":"Semantic-Aware Fine-Grained Correspondence","created":"2022-07-21","cited_by_count":13,"area":null},{"id":"2209.07778","title":"Spatial-then-Temporal Self-Supervised Learning for Video Correspondence","created":"2022-09-16","cited_by_count":13,"area":null},{"id":"2301.03330","title":"HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition","created":"2023-01-09","cited_by_count":12,"area":null},{"id":"2508.01345","title":"Predicting Video Slot Attention Queries from Random Slot-Feature Pairs","created":"2025-08-02","cited_by_count":12,"area":null},{"id":"2311.17893","title":"Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation","created":"2023-11-29","cited_by_count":10,"area":null},{"id":"2406.07398","title":"Visual Representation Learning with Stochastic Frame Prediction","created":"2024-06-11","cited_by_count":10,"area":null},{"id":"2407.06704","title":"Self-supervised visual learning from interactions with objects","created":"2024-07-09","cited_by_count":10,"area":null},{"id":"2409.16288","title":"Self-Supervised Any-Point Tracking by Contrastive Random Walks","created":"2024-09-24","cited_by_count":10,"area":null},{"id":"2512.13684","title":"Recurrent Video Masked Autoencoders","created":"2025-12-15","cited_by_count":10,"area":null},{"id":"2205.05487","title":"Scene Consistency Representation Learning for Video Scene Segmentation","created":"2022-05-11","cited_by_count":9,"area":null},{"id":"2208.00150","title":"Learning Shadow Correspondence for Video Shadow Detection","created":"2022-07-30","cited_by_count":9,"area":null},{"id":"2302.09043","title":"Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequences","created":"2023-02-17","cited_by_count":9,"area":null},{"id":"2306.02000","title":"Context-PIPs: Persistent Independent Particles Demands Spatial Context Features","created":"2023-06-03","cited_by_count":9,"area":null},{"id":"2212.09735","title":"Correspondence Distillation from NeRF-based GAN","created":"2022-12-19","cited_by_count":8,"area":null},{"id":"2203.15312","title":"In-N-Out Generative Learning for Dense Unsupervised Video Segmentation","created":"2022-03-29","cited_by_count":7,"area":null},{"id":"2105.05838","title":"Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning","created":"2021-05-12","cited_by_count":6,"area":null},{"id":"2212.10147","title":"Bridging Images and Videos: A Simple Learning Framework for Large Vocabulary Video Object Detection","created":"2022-12-20","cited_by_count":6,"area":null},{"id":"2309.15110","title":"Doduo: Learning Dense Visual Correspondence from Unsupervised Semantic-Aware Flow","created":"2023-09-26","cited_by_count":6,"area":null},{"id":"2112.04480","title":"Exploring Temporal Granularity in Self-Supervised Video Representation Learning","created":"2021-12-08","cited_by_count":5,"area":null},{"id":"2203.05553","title":"Transfer of Representations to Video Label Propagation: Implementation Factors Matter","created":"2022-03-10","cited_by_count":5,"area":null},{"id":"2208.01036","title":"Face-to-Face Contrastive Learning for Social Intelligence Question-Answering","created":"2022-07-29","cited_by_count":5,"area":null},{"id":"2507.09513","title":"Animal behavioral analysis and neural encoding with transformer-based self-supervised pretraining","created":"2025-07-13","cited_by_count":5,"area":null},{"id":"2303.13743","title":"TEGLO: High Fidelity Canonical Texture Mapping from Single-View Images","created":"2023-03-24","cited_by_count":4,"area":null},{"id":"2308.03040","title":"Learning Fine-Grained Features for Pixel-wise Video Correspondences","created":"2023-08-06","cited_by_count":4,"area":null},{"id":"2503.19953","title":"Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals","created":"2025-03-25","cited_by_count":4,"area":null},{"id":"2408.10292","title":"Leveraging Superfluous Information in Contrastive Representation Learning","created":"2024-08-19","cited_by_count":3,"area":null},{"id":"2502.03270","title":"The Temporal Trap: Entanglement in Pre-Trained Visual Representations for Visuomotor Policy Learning","created":"2025-02-05","cited_by_count":3,"area":null},{"id":"2506.03148","title":"Self-Supervised Spatial Correspondence Across Modalities","created":"2025-06-03","cited_by_count":3,"area":null},{"id":"2112.00804","title":"PreViTS: Contrastive Pretraining with Video Tracking Supervision","created":"2021-12-01","cited_by_count":2,"area":null},{"id":"2506.05543","title":"FRAME: Pre-Training Video Feature Representations via Anticipation and Memory","created":"2025-06-05","cited_by_count":2,"area":null},{"id":"2510.05558","title":"Midway Network: Learning Representations for Recognition and Motion from Latent Dynamics","created":"2025-10-07","cited_by_count":2,"area":null},{"id":"2511.07696","title":"FlowFeat: Pixel-Dense Embedding of Motion Profiles","created":"2025-11-10","cited_by_count":2,"area":null},{"id":"2511.10762","title":"Attentive Feature Aggregation or: How Policies Learn to Stop Worrying about Robustness and Attend to Task-Relevant Visual Cues","created":"2025-11-13","cited_by_count":2,"area":null},{"id":"2203.09343","title":"CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation","created":"2022-03-17","cited_by_count":1,"area":null},{"id":"2311.00961","title":"Concatenated Masked Autoencoders as Spatial-Temporal Learner","created":"2023-11-02","cited_by_count":1,"area":null},{"id":"2407.06531","title":"Decomposition Betters Tracking Everything Everywhere","created":"2024-07-09","cited_by_count":1,"area":null},{"id":"2501.02966","title":"Human Gaze Boosts Object-Centered Representation Learning","created":"2025-01-06","cited_by_count":1,"area":null},{"id":"2404.09504","title":"Learning Tracking Representations from Single Point Annotations","created":"2024-04-15","cited_by_count":0,"area":null},{"id":"2404.13505","title":"Dynamic in Static: Hybrid Visual Correspondence for Self-Supervised Video Object Segmentation","created":"2024-04-21","cited_by_count":0,"area":null},{"id":"2405.10439","title":"Beyond Traditional Single Object Tracking: A Survey","created":"2024-05-16","cited_by_count":0,"area":null},{"id":"2405.14010","title":"One-shot Training for Video Object Segmentation","created":"2024-05-22","cited_by_count":0,"area":null},{"id":"2503.12026","title":"Leveraging Motion Information for Better Self-Supervised Video Correspondence Learning","created":"2025-03-15","cited_by_count":0,"area":null},{"id":"2504.21692","title":"Enhancing Self-Supervised Fine-Grained Video Object Tracking with Dynamic Memory Prediction","created":"2025-04-30","cited_by_count":0,"area":null},{"id":"2508.15959","title":"Representation Learning with Adaptive Superpixel Coding","created":"2025-08-21","cited_by_count":0,"area":null},{"id":"2511.17805","title":"A Stitch in Time: Learning Procedural Workflow via Self-Supervised Plackett-Luce Ranking","created":"2025-11-21","cited_by_count":0,"area":null},{"id":"2602.04462","title":"Temporal Slowness in Central Vision Drives Semantic Object Learning","created":"2026-02-04","cited_by_count":0,"area":null},{"id":"2603.17813","title":"M2P: Improving Visual Foundation Models with Mask-to-Point Weakly-Supervised Learning for Dense Point Tracking","created":"2026-03-18","cited_by_count":0,"area":null},{"id":"2609.31725","title":"SWT: Self-Supervised Video Object Segmentation via Sliding, Wavelet and Transportation","created":"2026-09-22","cited_by_count":0,"area":null}],"next_cursor":null}