{"total":48,"rows":[{"id":"2403.16182","title":"EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric View of Procedural Activities in Real World","created":"2024-03-24","cited_by_count":95,"area":null},{"id":"2308.07123","title":"An Outlook into the Future of Egocentric Vision","created":"2023-08-14","cited_by_count":84,"area":null},{"id":"2112.03596","title":"E$^2$(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition","created":"2021-12-07","cited_by_count":50,"area":null},{"id":"2206.06243","title":"Contrastive Learning for Unsupervised Domain Adaptation of Time Series","created":"2022-06-13","cited_by_count":36,"area":null},{"id":"2212.10888","title":"A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability","created":"2022-12-21","cited_by_count":31,"area":null},{"id":"2308.12962","title":"Motion-Guided Masking for Spatiotemporal Representation Learning","created":"2023-08-24","cited_by_count":30,"area":null},{"id":"2209.10904","title":"AcroFOD: An Adaptive Method for Cross-domain Few-shot Object Detection","created":"2022-09-22","cited_by_count":25,"area":null},{"id":"2208.07365","title":"Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective","created":"2022-08-15","cited_by_count":21,"area":null},{"id":"2409.05202","title":"A Survey on Mixup Augmentations and Beyond","created":"2024-09-08","cited_by_count":20,"area":null},{"id":"2310.17942","title":"Diversifying Spatial-Temporal Perception for Video Domain Generalization","created":"2023-10-27","cited_by_count":19,"area":null},{"id":"2212.01555","title":"Contrastive Domain Adaptation for Time-Series via Temporal Mixup","created":"2022-12-03","cited_by_count":18,"area":null},{"id":"2308.09139","title":"The Unreasonable Effectiveness of Large Language-Vision Models for Source-free Video Domain Adaptation","created":"2023-08-17","cited_by_count":16,"area":null},{"id":"2403.01560","title":"XOV-Action: Towards Generalizable Open-Vocabulary Action Recognition","created":"2024-03-03","cited_by_count":15,"area":null},{"id":"2303.13505","title":"A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action Recognition","created":"2023-03-23","cited_by_count":14,"area":null},{"id":"2202.04828","title":"Learning Latent Causal Dynamics","created":"2022-02-10","cited_by_count":13,"area":null},{"id":"2211.10412","title":"Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey","created":"2022-11-17","cited_by_count":12,"area":null},{"id":"2309.01265","title":"SOAR: Scene-debiasing Open-set Action Recognition","created":"2023-09-03","cited_by_count":12,"area":null},{"id":"2312.02914","title":"Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training","created":"2023-12-05","cited_by_count":12,"area":null},{"id":"2208.05187","title":"Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation","created":"2022-08-10","cited_by_count":7,"area":null},{"id":"2312.00826","title":"DEVIAS: Learning Disentangled Video Representations of Action and Scene","created":"2023-11-30","cited_by_count":7,"area":null},{"id":"2203.16244","title":"CycDA: Unsupervised Cycle Domain Adaptation from Image to Video","created":"2022-03-30","cited_by_count":6,"area":null},{"id":"2311.18572","title":"Source-free Video Domain Adaptation by Learning from Noisy Labels","created":"2023-11-30","cited_by_count":6,"area":null},{"id":"2202.02802","title":"Low-confidence Samples Matter for Domain Adaptation","created":"2022-02-06","cited_by_count":4,"area":null},{"id":"2402.00868","title":"We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline","created":"2024-02-01","cited_by_count":4,"area":null},{"id":"2403.05535","title":"Tell, Don't Show!: Language Guidance Eases Transfer Across Domains in Images and Videos","created":"2024-03-08","cited_by_count":4,"area":null},{"id":"2409.19635","title":"Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation","created":"2024-09-29","cited_by_count":4,"area":null},{"id":"2503.00202","title":"MIDAS: Mixing Ambiguous Data with Soft Labels for Dynamic Facial Expression Recognition","created":"2025-02-28","cited_by_count":4,"area":null},{"id":"2305.08420","title":"Exploring Few-Shot Adaptation for Activity Recognition on Diverse Domains","created":"2023-05-15","cited_by_count":2,"area":null},{"id":"2509.08908","title":"Diffusion-Based Action Recognition Generalizes to Untrained Domains","created":"2025-09-10","cited_by_count":2,"area":null},{"id":"2510.14666","title":"Geometric Moment Alignment for Domain Adaptation via Siegel Embeddings","created":"2025-10-16","cited_by_count":2,"area":null},{"id":"2211.03004","title":"Bringing Online Egocentric Action Recognition into the wild","created":"2022-11-06","cited_by_count":1,"area":null},{"id":"2301.03322","title":"Simplifying Open-Set Video Domain Adaptation with Contrastive Learning","created":"2023-01-09","cited_by_count":1,"area":null},{"id":"2311.12467","title":"GLAD: Global-Local View Alignment and Background Debiasing for Unsupervised Video Domain Adaptation with Large Domain Gap","created":"2023-11-21","cited_by_count":1,"area":null},{"id":"2311.17942","title":"Object-based (yet Class-agnostic) Video Domain Adaptation","created":"2023-11-29","cited_by_count":1,"area":null},{"id":"2401.12421","title":"AdaEmbed: Semi-supervised Domain Adaptation in the Embedding Space","created":"2024-01-23","cited_by_count":1,"area":null},{"id":"2502.16637","title":"Time Series Domain Adaptation via Latent Invariant Causal Mechanism","created":"2025-02-23","cited_by_count":1,"area":null},{"id":"2506.20867","title":"Enhancing Ambiguous Dynamic Facial Expression Recognition with Soft Label-based Data Augmentation","created":"2025-06-25","cited_by_count":1,"area":null},{"id":"2603.29666","title":"CoRe-DA: Contrastive Regression for Unsupervised Domain Adaptation in Surgical Skill Assessment","created":"2026-03-31","cited_by_count":1,"area":null},{"id":"2112.01176","title":"Overcoming the Domain Gap in Neural Action Representations","created":"2021-12-02","cited_by_count":0,"area":null},{"id":"2308.06451","title":"Semantic Equivariant Mixup","created":"2023-08-12","cited_by_count":0,"area":null},{"id":"2308.10205","title":"GeT: Generative Target Structure Debiasing for Domain Adaptation","created":"2023-08-20","cited_by_count":0,"area":null},{"id":"2403.16242","title":"Adversarially Masked Video Consistency for Unsupervised Domain Adaptation","created":"2024-03-24","cited_by_count":0,"area":null},{"id":"2411.15557","title":"Semantically Guided Action Anticipation","created":"2024-11-23","cited_by_count":0,"area":null},{"id":"2604.09955","title":"Learnable Motion-Focused Tokenization for Effective and Efficient Video Unsupervised Domain Adaptation","created":"2026-04-10","cited_by_count":0,"area":null},{"id":"2604.22595","title":"EV-CLIP: Efficient Visual Prompt Adaptation for CLIP in Few-shot Action Recognition under Visual Challenges","created":"2026-04-24","cited_by_count":0,"area":null},{"id":"2605.19510","title":"Return of Frustratingly Easy Unsupervised Video Domain Adaptation","created":"2026-05-19","cited_by_count":0,"area":null},{"id":"2607.23735","title":"Source-Free Controlled Adaptation of Teachers for Continual Test-Time Adaptation","created":"2026-07-26","cited_by_count":0,"area":null},{"id":"2608.16804","title":"Cross-Sign Language Transfer Learning Using Domain Adaptation with Multi-scale Temporal Alignment","created":"2026-08-17","cited_by_count":0,"area":null}],"next_cursor":null}