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Xin Jin

20 papers hereh-index 7282 citations23 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author14
  • last author2

Across the 17 of 20 papers where every author was matched, so the position is known.

fields
  • cs.CV13
  • cs.LG3
  • cs.RO2
  • cs.AI1
  • cs.CL1
same name
  • Xin Jin — 40 papers, h 29
  • Xin Jin — 14 papers, h 7
  • Xin Jin — 14 papers, h 7
  • Xin Jin — 12 papers, h 7
  • Xin Jin — 11 papers, h 18
  • Xin Jin — 11 papers, h 6

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
collaborators
Showing 2025Show all

4 papers · 1 filter

cs.LG2025

Goal-Driven Reward by Video Diffusion Models for Reinforcement Learning

Qi Wang, Mian Wu, Yuyang Zhang +7

Reinforcement Learning (RL) has achieved remarkable success in various domains, yet it often relies on carefully designed programmatic reward functions to guide agent behavior. Des…

cs.CV2025

ANTS: Adaptive Negative Textual Space Shaping for OOD Detection via Test-Time MLLM Understanding and Reasoning

Wenjie Zhu, Yabin Zhang, Xin Jin +2

The introduction of negative labels (NLs) has proven effective in enhancing Out-of-Distribution (OOD) detection. However, existing methods often lack an understanding of OOD images…

cs.CV2025

Perceiving and Acting in First-Person: A Dataset and Benchmark for Egocentric Human-Object-Human Interactions

Liang Xu, Chengqun Yang, Zili Lin +11

Learning action models from real-world human-centric interaction datasets is important towards building general-purpose intelligent assistants with efficiency. However, most existi…

cs.CV2025

Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning

Qi Wang, Zhipeng Zhang, Baao Xie +6

Training visual reinforcement learning (RL) in practical scenarios presents a significant challenge, i.e., RL agents suffer from low sample efficiency in environments wi…

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