{"total":62,"rows":[{"id":"2102.04906","title":"Dynamic Neural Networks: A Survey","created":"2021-02-09","cited_by_count":408,"area":null},{"id":"2305.06988","title":"Self-Chained Image-Language Model for Video Localization and Question Answering","created":"2023-05-11","cited_by_count":203,"area":null},{"id":"2211.09552","title":"UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormer","created":"2022-11-17","cited_by_count":116,"area":null},{"id":"2105.03245","title":"Adaptive Focus for Efficient Video Recognition","created":"2021-05-07","cited_by_count":77,"area":null},{"id":"2104.13400","title":"FrameExit: Conditional Early Exiting for Efficient Video Recognition","created":"2021-04-27","cited_by_count":59,"area":null},{"id":"2104.09952","title":"MGSampler: An Explainable Sampling Strategy for Video Action Recognition","created":"2021-04-20","cited_by_count":49,"area":null},{"id":"2112.14238","title":"AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition","created":"2021-12-28","cited_by_count":37,"area":null},{"id":"2105.05165","title":"AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition","created":"2021-05-11","cited_by_count":36,"area":null},{"id":"2108.01316","title":"RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting","created":"2021-08-03","cited_by_count":36,"area":null},{"id":"2108.10394","title":"Dynamic Network Quantization for Efficient Video Inference","created":"2021-08-23","cited_by_count":36,"area":null},{"id":"2106.02342","title":"ASCNet: Self-supervised Video Representation Learning with Appearance-Speed Consistency","created":"2021-06-04","cited_by_count":35,"area":null},{"id":"2108.03825","title":"Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video","created":"2021-08-09","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":"2103.15395","title":"No frame left behind: Full Video Action Recognition","created":"2021-03-29","cited_by_count":24,"area":null},{"id":"2201.04388","title":"OCSampler: Compressing Videos to One Clip with Single-step Sampling","created":"2022-01-12","cited_by_count":23,"area":null},{"id":"2103.11770","title":"AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition","created":"2021-03-22","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":"2105.12043","title":"Temporal Action Proposal Generation with Transformers","created":"2021-05-25","cited_by_count":17,"area":null},{"id":"2112.07984","title":"Temporal Action Proposal Generation with Background Constraint","created":"2021-12-15","cited_by_count":16,"area":null},{"id":"2206.08016","title":"Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches","created":"2022-06-16","cited_by_count":15,"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":"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":"2308.13998","title":"Computation-efficient Deep Learning for Computer Vision: A Survey","created":"2023-08-27","cited_by_count":14,"area":null},{"id":"2105.12085","title":"DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning","created":"2021-05-25","cited_by_count":13,"area":null},{"id":"2211.05039","title":"Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task","created":"2022-11-09","cited_by_count":13,"area":null},{"id":"2109.12343","title":"Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems","created":"2021-09-25","cited_by_count":12,"area":null},{"id":"2307.08908","title":"What Can Simple Arithmetic Operations Do for Temporal Modeling?","created":"2023-07-18","cited_by_count":12,"area":null},{"id":"2211.00833","title":"Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning","created":"2022-11-02","cited_by_count":11,"area":null},{"id":"2308.08327","title":"AdaBrowse: Adaptive Video Browser for Efficient Continuous Sign Language Recognition","created":"2023-08-16","cited_by_count":10,"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":"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":"2205.09676","title":"Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search","created":"2022-05-19","cited_by_count":6,"area":null},{"id":"2208.13363","title":"Survey: Exploiting Data Redundancy for Optimization of Deep Learning","created":"2022-08-29","cited_by_count":6,"area":null},{"id":"2210.08164","title":"Linear Video Transformer with Feature Fixation","created":"2022-10-15","cited_by_count":6,"area":null},{"id":"2201.04021","title":"Optimization Planning for 3D ConvNets","created":"2022-01-11","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":"2209.09194","title":"Differentiable Frequency-based Disentanglement for Aerial Video Action Recognition","created":"2022-09-15","cited_by_count":4,"area":null},{"id":"2305.17569","title":"Collaborative Multi-Agent Video Fast-Forwarding","created":"2023-05-27","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":"2405.06574","title":"Deep video representation learning: a survey","created":"2024-05-10","cited_by_count":4,"area":null},{"id":"2104.00179","title":"Selective Feature Compression for Efficient Activity Recognition Inference","created":"2021-04-01","cited_by_count":3,"area":null},{"id":"2105.03827","title":"Good Practices and A Strong Baseline for Traffic Anomaly Detection","created":"2021-05-09","cited_by_count":3,"area":null},{"id":"2111.11653","title":"Modeling Temporal Concept Receptive Field Dynamically for Untrimmed Video Analysis","created":"2021-11-23","cited_by_count":3,"area":null},{"id":"2304.10316","title":"Search-Map-Search: A Frame Selection Paradigm for Action Recognition","created":"2023-04-20","cited_by_count":3,"area":null},{"id":"2104.09760","title":"HCMS: Hierarchical and Conditional Modality Selection for Efficient Video Recognition","created":"2021-04-20","cited_by_count":2,"area":null},{"id":"2201.06304","title":"Action Keypoint Network for Efficient Video Recognition","created":"2022-01-17","cited_by_count":2,"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":"2109.02137","title":"Efficient Action Recognition Using Confidence Distillation","created":"2021-09-05","cited_by_count":1,"area":null},{"id":"2301.07565","title":"Gated-ViGAT: Efficient Bottom-Up Event Recognition and Explanation Using a New Frame Selection Policy and Gating Mechanism","created":"2023-01-18","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":"2103.13021","title":"Convex Online Video Frame Subset Selection using Multiple Criteria for Data Efficient Autonomous Driving","created":"2021-03-24","cited_by_count":0,"area":null},{"id":"2104.10492","title":"Skimming and Scanning for Untrimmed Video Action Recognition","created":"2021-04-21","cited_by_count":0,"area":null},{"id":"2401.04975","title":"HaltingVT: Adaptive Token Halting Transformer for Efficient Video Recognition","created":"2024-01-10","cited_by_count":0,"area":null},{"id":"2503.23447","title":"CA^2ST: Cross-Attention in Audio, Space, and Time for Holistic Video Recognition","created":"2025-03-30","cited_by_count":0,"area":null},{"id":"2505.18048","title":"SHARDeg: A Benchmark for Skeletal Human Action Recognition in Degraded Scenarios","created":"2025-05-23","cited_by_count":0,"area":null},{"id":"2507.15628","title":"A Survey on Efficiency Optimization Techniques for DNN-based Video Analytics: Process Systems, Algorithms, and Applications","created":"2025-07-21","cited_by_count":0,"area":null},{"id":"2609.10355","title":"Why Is Video Still So Expensive? A Survey of Inference-Efficiency Mechanisms in Video and Audiovisual LLMs","created":"2026-09-09","cited_by_count":0,"area":null},{"id":"2609.33492","title":"FedMHAR: Federated Multimodal Human Activity Recognition using Multi-Agent Reinforcement Learning","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}