https://github.com/uchiyama33/aosa
공식 코드Adaptive occlusion sensitivity analysis for visually explaining video recognition networks
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Video Classification is the task of producing a label that is relevant to the video given its frames. A good video level classifier is one that not only provides accurate frame labels, but also best describes the entire video given the features and the annotations of the various frames in the video. For example, a video might contain a tree in some frame, but the label that is central to the video might be something else (e.g., “hiking”). The granularity of the labels that are needed to describe the frames and the video depends on the task. Typical tasks include assigning one or more global labels to the video, and assigning one or more labels for each frame inside the video.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Adaptive occlusion sensitivity analysis for visually explaining video recognition networks
Long-term Leap Attention, Short-term Periodic Shift for Video Classification
VidModEx: Interpretable and Efficient Black Box Model Extraction for High-Dimensional Spaces
Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments
Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset
The Monkeytyping Solution to the YouTube-8M Video Understanding Challenge
Revisiting Classifier: Transferring Vision-Language Models for Video Recognition
Overlooked Video Classification in Weakly Supervised Video Anomaly Detection
SmallBigNet: Integrating Core and Contextual Views for Video Classification
Generalized Few-Shot Video Classification with Video Retrieval and Feature Generation
Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior
Active Contrastive Learning of Audio-Visual Video Representations
Few-Shot Classification of Interactive Activities of Daily Living (InteractADL)
Unsupervised Pre-training for Temporal Action Localization Tasks
Movie Genre Classification by Language Augmentation and Shot Sampling
A Unified Multimodal De- and Re-coupling Framework for RGB-D Motion Recognition
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs
Video-based surgical skill assessment using 3D convolutional neural networks
LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning
Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.