https://github.com/anonrep/iba-for-video-sequences
공식 코드Extending Information Bottleneck Attribution to Video Sequences
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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.
Extending Information Bottleneck Attribution to Video Sequences
Over-the-Air Adversarial Flickering Attacks against Video Recognition Networks
Learnable pooling with Context Gating for video classification
Learnable pooling with Context Gating for video classification
Training compact deep learning models for video classification using circulant matrices
Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications
Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks
MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding
Exploring Temporal Information for Improved Video Understanding
DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer Models
MU-Bench: A Multitask Multimodal Benchmark for Machine Unlearning
MLtuner: System Support for Automatic Machine Learning Tuning
A Dataset for Medical Instructional Video Classification and Question Answering
Differentiable Resolution Compression and Alignment for Efficient Video Classification and Retrieval
Text-to-feature diffusion for audio-visual few-shot learning
Temporal and cross-modal attention for audio-visual zero-shot learning
Self-Supervised Pretraining of 3D Features on any Point-Cloud
Is Space-Time Attention All You Need for Video Understanding?
Video Classification with Channel-Separated Convolutional Networks
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
The effectiveness of MAE pre-pretraining for billion-scale pretraining
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
Learning To Recognize Procedural Activities with Distant Supervision
Two-Stream Convolutional Networks for Action Recognition in Videos
TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs
Verbs in Action: Improving verb understanding in video-language models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.