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Activity Recognition
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Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.
Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
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Exploring FMCW Radars and Feature Maps for Activity Recognition: A Benchmark Study
Understanding the Limits of Lifelong Knowledge Editing in LLMs
Gate-Shift-Pose: Enhancing Action Recognition in Sports with Skeleton Information
Semi-Supervised Audio-Visual Video Action Recognition with Audio Source Localization Guided Mixup
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A Simple and Efficient Baseline for Video Action Recognition
Unlocking Efficient, Scalable, and Continual Knowledge Editing with Basis-Level Representation Fine-Tuning
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports
Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
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