The SkatingVerse Workshop & Challenge: Methods and Results
원문 초록
The SkatingVerse Workshop & Challenge aims to encourage research in developing novel and accurate methods for human action understanding. The SkatingVerse dataset used for the SkatingVerse Challenge has been publicly released. There are two subsets in the dataset, i.e., the training subset and testing subset. The training subsets consists of 19,993 RGB video sequences, and the testing subsets consists of 8,586 RGB video sequences. Around 10 participating teams from the globe competed in the SkatingVerse Challenge. In this paper, we provide a brief summary of the SkatingVerse Workshop & Challenge including brief introductions to the top three methods. The submission leaderboard will be reopened for researchers that are interested in the human action understanding challenge. The benchmark dataset and other information can be found at: https://skatingverse.github.io/.
함께 살펴볼 논문
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LLaVA-Pose: Enhancing Human Pose and Action Understanding via Keypoint-Integrated Instruction Tuning
F$^3$Set: Towards Analyzing Fast, Frequent, and Fine-grained Events from Videos
LLaVAction: evaluating and training multi-modal large language models for action recognition
SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization
EgoExo-Fitness: Towards Egocentric and Exocentric Full-Body Action Understanding
OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding
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