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20232026
most citedSAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory

10 citations · 15 across the 21 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.CV2025

Warehouse Spatial Question Answering with LLM Agent

Hsiang-Wei Huang, Jen-Hao Cheng, Kuang-Ming Chen +8

Spatial understanding has been a challenging task for existing Multi-modal Large Language Models~(MLLMs). Previous methods leverage large-scale MLLM finetuning to enhance MLLM's sp…

cs.CV2025

ToSA: Token Merging with Spatial Awareness

Hsiang-Wei Huang, Wenhao Chai, Kuang-Ming Chen +2

Token merging has emerged as an effective strategy to accelerate Vision Transformers (ViT) by reducing computational costs. However, existing methods primarily rely on the visual t…

cs.CV2025

Adapting SAM 2 for Visual Object Tracking: 1st Place Solution for MMVPR Challenge Multi-Modal Tracking

Cheng-Yen Yang, Hsiang-Wei Huang, Pyong-Kun Kim +5

We present an effective approach for adapting the Segment Anything Model 2 (SAM2) to the Visual Object Tracking (VOT) task. Our method leverages the powerful pre-trained capabiliti…

cs.CV2025

TEMPURA: Temporal Event Masked Prediction and Understanding for Reasoning in Action

Jen-Hao Cheng, Vivian Wang, Huayu Wang +11

Understanding causal event relationships and achieving fine-grained temporal grounding in videos remain challenging for vision-language models (VLMs). We propose TEMPURA (Temporal…

cs.CV2025

Technical Report for ReID-SAM on SkiTB Visual Tracking Challenge 2025

Kunjun Li, Cheng-Yen Yang, Hsiang-Wei Huang +1

This report introduces ReID-SAM, a novel model developed for the SkiTB Challenge that addresses the complexities of tracking skier appearance. Our approach integrates the SAMURAI t…

cs.CV2025

PackDiT: Joint Human Motion and Text Generation via Mutual Prompting

Zhongyu Jiang, Wenhao Chai, Zhuoran Zhou +3

Human motion generation has advanced markedly with the advent of diffusion models. Most recent studies have concentrated on generating motion sequences based on text prompts, commo…