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20222026
most citedExploring Temporally Dynamic Data Augmentation for Video Recognition

5 citations · 16 across the 18 of their papers we have counts for

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cs.CV2026

HOIGS: Human-Object Interaction Gaussian Splatting

Taewoo Kim, Suwoong Yeom, Jaehyun Pyun +6

Reconstructing dynamic scenes with complex human-object interactions is a fundamental challenge in computer vision and graphics. Existing Gaussian Splatting methods either rely on…

cs.CV2026

SeaCache: Spectral-Evolution-Aware Cache for Accelerating Diffusion Models

Jiwoo Chung, Sangeek Hyun, MinKyu Lee +5

Diffusion models are a strong backbone for visual generation, but their inherently sequential denoising process leads to slow inference. Previous methods accelerate sampling by cac…

cs.CV2026

HARMONI: Aligning Human and Scene Priors for Multi-View 4D Reconstruction

Sangmin Kim, Minhyuk Hwang, Geonho Cha +2

Recent advances in 3D foundation models have enabled joint reconstruction of humans and their surrounding environments. However, combining independently trained human and scene pri…

cs.CV2026

Video-Oasis: Rethinking Evaluation of Video Understanding

Geuntaek Lim, Sungjune Park, Jaeyun Lee +5

The inherent complexity of video understanding makes it difficult to determine whether Video-LLM benchmark performance stems from visual perception, linguistic reasoning, or knowle…

cs.CV2026

Why Can't I Open My Drawer? Mitigating Object-Driven Shortcuts in Zero-Shot Compositional Action Recognition

Geo Ahn, Inwoong Lee, Taeoh Kim +3

Zero-Shot Compositional Action Recognition (ZS-CAR) requires recognizing novel verb-object combinations composed of previously observed primitives. In this work, we tackle a key fa…