activity
20202026
most citedPointMixup: Augmentation for Point Clouds

4 citations · 7 across the 7 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Training-Free Hidden-State Refinement for Flow-Matching Image Generators

Yuanyi Yan, Xinzhe Rao, Canyu Shen +5

We aim to improve frozen flow-matching image generators by adding inference computation inside the denoiser, without changing model weights or the outer sampler. Existing generator…

cs.AI2026

From State to Action: OODA-Tool for Reliable Multi-Turn Tool Use

Rongfeng Guo, Yinxuan Huang, Yusen Wu +5

Reliable multi-turn tool use requires an agent to preserve an evolving task state and ensure that each action remains consistent with it. However, direct function-calling and ReAct…

cs.CV2021

Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting

Martine Toering, Ioannis Gatopoulos, Maarten Stol +1

Instance-level contrastive learning techniques, which rely on data augmentation and a contrastive loss function, have found great success in the domain of visual representation lea…

cs.CV20201 cited

Localizing the Common Action Among a Few Videos

Pengwan Yang, Vincent Tao Hu, Pascal Mettes +1

This paper strives to localize the temporal extent of an action in a long untrimmed video. Where existing work leverages many examples with their start, their ending, and/or the cl…

cs.CV20204 cited

PointMixup: Augmentation for Point Clouds

Yunlu Chen, Vincent Tao Hu, Efstratios Gavves +4

This paper introduces data augmentation for point clouds by interpolation between examples. Data augmentation by interpolation has shown to be a simple and effective approach in th…