Faster-TAD: Temporal Action Detection with Proposal Generation and Classification in a Unified Network for ActivityNet Challenge 2022
This technical report outlines a novel approach to temporal action localization (TAL) in untrimmed videos using a unified network called Faster-TAD, achieving competitive results in the ActivityNet Challenge 2022.
관성 센서 기반 인간 활동 인식을 위한 시간적 행동 지역화: 기존 방식보다 우수한 성능 입증
본 논문에서는 관성 센서 데이터를 활용한 인간 활동 인식 분야에서 시간적 행동 지역화(TAL) 모델의 적용 가능성을 체계적으로 보여주고, 기존의 고정된 시간 윈도우 기반 분류 방식보다 우수한 성능을 입증합니다.
Temporal Action Localization with Multimodal and Unimodal Transformers: A Winning Solution for the Perception Test Challenge 2024
This research paper presents a novel approach to temporal action localization in videos, combining multimodal and unimodal transformers to achieve state-of-the-art results on the Perception Test Challenge 2024 dataset.
Video Self-Stitching Graph Network for Improving Temporal Action Localization of Short Actions
A multi-level cross-scale solution called video self-stitching graph network (VSGN) is proposed to tackle the challenge of large action scale variation, especially for short actions, in temporal action localization.
Scaling End-to-End Temporal Action Localization with Long-Short-range Adapters
LoSA, a memory-and-parameter-efficient backbone adapter, enables end-to-end training of large video foundation models for improved temporal action localization in untrimmed videos.
© 2024 by Linnk AI