https://github.com/dibschat/tempAgg
공식 코드Temporal Aggregate Representations for Long-Range Video Understanding
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Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.
In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.
Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Temporal Aggregate Representations for Long-Range Video Understanding
Fine-grained Action Analysis: A Multi-modality and Multi-task Dataset of Figure Skating
Multi-task Deep Learning for Real-Time 3D Human Pose Estimation and Action Recognition
Coarse or Fine? Recognising Action End States without Labels
Just Add $π$! Pose Induced Video Transformers for Understanding Activities of Daily Living
Seeing the Pose in the Pixels: Learning Pose-Aware Representations in Vision Transformers
Image Manipulation Detection by Multi-View Multi-Scale Supervision
Mass-Editing Memory with Attention in Transformers: A cross-lingual exploration of knowledge
HaltingVT: Adaptive Token Halting Transformer for Efficient Video Recognition
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
Benchmarking and Rethinking Knowledge Editing for Large Language Models
Evaluating the Ripple Effects of Knowledge Editing in Language Models
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders
Interferometric Graph Transform: a Deep Unsupervised Graph Representation
E$^2$(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition
What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions
With a Little Help from my Temporal Context: Multimodal Egocentric Action Recognition
EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognition
CoME: An Unlearning-based Approach to Conflict-free Model Editing
Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D Space
FT-HID: A Large Scale RGB-D Dataset for First and Third Person Human Interaction Analysis
Joint Knowledge Editing for Information Enrichment and Probability Promotion
OccamNets: Mitigating Dataset Bias by Favoring Simpler Hypotheses
Event Stream based Human Action Recognition: A High-Definition Benchmark Dataset and Algorithms
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.