{"total":19,"rows":[{"id":"2111.02398","title":"Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods","created":"2021-11-01","cited_by_count":83,"area":null},{"id":"2106.01603","title":"CT-Net: Channel Tensorization Network for Video Classification","created":"2021-06-03","cited_by_count":26,"area":null},{"id":"2203.08897","title":"Gate-Shift-Fuse for Video Action Recognition","created":"2022-03-16","cited_by_count":15,"area":null},{"id":"2206.05275","title":"Spatial-temporal Concept based Explanation of 3D ConvNets","created":"2022-06-09","cited_by_count":12,"area":null},{"id":"2112.03020","title":"Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning","created":"2021-12-06","cited_by_count":6,"area":null},{"id":"2301.00436","title":"Hierarchical Explanations for Video Action Recognition","created":"2023-01-01","cited_by_count":5,"area":null},{"id":"2109.00222","title":"Spatio-Temporal Perturbations for Video Attribution","created":"2021-09-01","cited_by_count":3,"area":null},{"id":"2409.02371","title":"Unfolding Videos Dynamics via Taylor Expansion","created":"2024-09-04","cited_by_count":3,"area":null},{"id":"2111.01215","title":"Gradient Frequency Modulation for Visually Explaining Video Understanding Models","created":"2021-11-01","cited_by_count":2,"area":null},{"id":"2207.12859","title":"Adaptive occlusion sensitivity analysis for visually explaining video recognition networks","created":"2022-07-26","cited_by_count":2,"area":null},{"id":"2303.09941","title":"Leaping Into Memories: Space-Time Deep Feature Synthesis","created":"2023-03-17","cited_by_count":2,"area":null},{"id":"2511.03725","title":"Disentangled Concepts Speak Louder Than Words: Explainable Video Action Recognition","created":"2025-11-05","cited_by_count":2,"area":null},{"id":"2203.13038","title":"Interpretable Prediction of Pulmonary Hypertension in Newborns using Echocardiograms","created":"2022-03-24","cited_by_count":1,"area":null},{"id":"2210.17130","title":"BOREx: Bayesian-Optimization--Based Refinement of Saliency Map for Image- and Video-Classification Models","created":"2022-10-31","cited_by_count":1,"area":null},{"id":"2405.10082","title":"An Integrated Framework for Multi-Granular Explanation of Video Summarization","created":"2024-05-16","cited_by_count":1,"area":null},{"id":"2405.14584","title":"SE3D: A Framework For Saliency Method Evaluation In 3D Imaging","created":"2024-05-23","cited_by_count":1,"area":null},{"id":"2207.13306","title":"Object-ABN: Learning to Generate Sharp Attention Maps for Action Recognition","created":"2022-07-27","cited_by_count":0,"area":null},{"id":"2401.11796","title":"REVEX: A Unified Framework for Removal-Based Explainable Artificial Intelligence in Video","created":"2024-01-22","cited_by_count":0,"area":null},{"id":"2511.18359","title":"TRANSPORTER: Transferring Visual Semantics from VLM Manifolds","created":"2025-11-23","cited_by_count":0,"area":null}],"next_cursor":null}