https://github.com/anshulbshah/halp
공식 코드HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions
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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.
HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions
End-to-End Learning of Visual Representations from Uncurated Instructional Videos
Two-stream Flow-guided Convolutional Attention Networks for Action Recognition
Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark
Action Recognition based on Cross-Situational Action-object Statistics
Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs
EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity Understanding
Late Temporal Modeling in 3D CNN Architectures with BERT for Action Recognition
Learning Self-Similarity in Space and Time as Generalized Motion for Video Action Recognition
History Matters: Temporal Knowledge Editing in Large Language Model
UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition
A Multi-viewpoint Outdoor Dataset for Human Action Recognition
Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities
WiFi CSI Based Temporal Activity Detection via Dual Pyramid Network
Reconstructing undersampled photoacoustic microscopy images using deep learning
FPGA-QHAR: Throughput-Optimized for Quantized Human Action Recognition on The Edge
Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
Editing Implicit Assumptions in Text-to-Image Diffusion Models
Model Editing as a Double-Edged Sword: Steering Agent Ethical Behavior Toward Beneficence or Harm
Expose Before You Defend: Unifying and Enhancing Backdoor Defenses via Exposed Models
Towards Unified Multimodal Editing with Enhanced Knowledge Collaboration
Recognizing Involuntary Actions from 3D Skeleton Data Using Body States
Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action Recognition
Exploring Modulated Detection Transformer as a Tool for Action Recognition in Videos
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.