{"total":34,"rows":[{"id":"2203.03821","title":"CF-ViT: A General Coarse-to-Fine Method for Vision Transformer","created":"2022-03-08","cited_by_count":63,"area":null},{"id":"2211.09703","title":"EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual Backbones","created":"2022-11-17","cited_by_count":43,"area":null},{"id":"2201.03014","title":"Glance and Focus Networks for Dynamic Visual Recognition","created":"2022-01-09","cited_by_count":41,"area":null},{"id":"2203.08481","title":"Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding","created":"2022-03-16","cited_by_count":33,"area":null},{"id":"2306.11248","title":"Dynamic Perceiver for Efficient Visual Recognition","created":"2023-06-20","cited_by_count":33,"area":null},{"id":"2301.02217","title":"EgoDistill: Egocentric Head Motion Distillation for Efficient Video Understanding","created":"2023-01-05","cited_by_count":32,"area":null},{"id":"2207.11660","title":"MAR: Masked Autoencoders for Efficient Action Recognition","created":"2022-07-24","cited_by_count":29,"area":null},{"id":"2306.11726","title":"How can objects help action recognition?","created":"2023-06-20","cited_by_count":21,"area":null},{"id":"2209.13465","title":"AdaFocusV3: On Unified Spatial-temporal Dynamic Video Recognition","created":"2022-09-27","cited_by_count":18,"area":null},{"id":"2207.10388","title":"NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition","created":"2022-07-21","cited_by_count":15,"area":null},{"id":"2211.11446","title":"SMAUG: Sparse Masked Autoencoder for Efficient Video-Language Pre-training","created":"2022-11-21","cited_by_count":15,"area":null},{"id":"2308.13998","title":"Computation-efficient Deep Learning for Computer Vision: A Survey","created":"2023-08-27","cited_by_count":14,"area":null},{"id":"2409.00342","title":"AdaNAT: Exploring Adaptive Policy for Token-Based Image Generation","created":"2024-08-31","cited_by_count":13,"area":null},{"id":"2207.09759","title":"Task-adaptive Spatial-Temporal Video Sampler for Few-shot Action Recognition","created":"2022-07-20","cited_by_count":9,"area":null},{"id":"2207.10379","title":"Temporal Saliency Query Network for Efficient Video Recognition","created":"2022-07-21","cited_by_count":9,"area":null},{"id":"2412.11228","title":"Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition","created":"2024-12-15","cited_by_count":9,"area":null},{"id":"2209.12797","title":"Rethinking Resolution in the Context of Efficient Video Recognition","created":"2022-09-26","cited_by_count":8,"area":null},{"id":"2211.16726","title":"Boosted Dynamic Neural Networks","created":"2022-11-30","cited_by_count":8,"area":null},{"id":"2305.06114","title":"Few-shot Action Recognition via Intra- and Inter-Video Information Maximization","created":"2023-05-10","cited_by_count":7,"area":null},{"id":"2501.07451","title":"A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion","created":"2025-01-13","cited_by_count":5,"area":null},{"id":"2207.09927","title":"ViGAT: Bottom-up event recognition and explanation in video using factorized graph attention network","created":"2022-07-20","cited_by_count":4,"area":null},{"id":"2308.04834","title":"View while Moving: Efficient Video Recognition in Long-untrimmed Videos","created":"2023-08-09","cited_by_count":4,"area":null},{"id":"2308.09322","title":"Audio-Visual Glance Network for Efficient Video Recognition","created":"2023-08-18","cited_by_count":4,"area":null},{"id":"2407.04274","title":"Fine-grained Dynamic Network for Generic Event Boundary Detection","created":"2024-07-05","cited_by_count":4,"area":null},{"id":"2506.21080","title":"EgoAdapt: Adaptive Multisensory Distillation and Policy Learning for Efficient Egocentric Perception","created":"2025-06-26","cited_by_count":4,"area":null},{"id":"2602.22073","title":"AdaSpot: Spend Resolution Where It Matters for Precise Event Spotting","created":"2026-02-25","cited_by_count":2,"area":null},{"id":"2603.14252","title":"MistExit: Learning to Exit for Early Mistake Detection in Procedural Videos","created":"2026-03-15","cited_by_count":2,"area":null},{"id":"2210.13605","title":"GliTr: Glimpse Transformers with Spatiotemporal Consistency for Online Action Prediction","created":"2022-10-24","cited_by_count":1,"area":null},{"id":"2307.01545","title":"EffSeg: Efficient Fine-Grained Instance Segmentation using Structure-Preserving Sparsity","created":"2023-07-04","cited_by_count":1,"area":null},{"id":"2310.18656","title":"Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation","created":"2023-10-28","cited_by_count":1,"area":null},{"id":"2409.05260","title":"Scalable Frame Sampling for Video Classification: A Semi-Optimal Policy Approach with Reduced Search Space","created":"2024-09-09","cited_by_count":1,"area":null},{"id":"2207.03095","title":"EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2022: Team HNU-FPV Technical Report","created":"2022-07-07","cited_by_count":0,"area":null},{"id":"2403.11959","title":"IVAC-P2L: Leveraging Irregular Repetition Priors for Improving Video Action Counting","created":"2024-03-18","cited_by_count":0,"area":null},{"id":"2502.21004","title":"Soften the Mask: Adaptive Temporal Soft Mask for Efficient Dynamic Facial Expression Recognition","created":"2025-02-28","cited_by_count":0,"area":null}],"next_cursor":null}