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20232026
most citedDescribe Anything in Medical Images

2 citations · 3 across the 9 of their papers we have counts for

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11 papers · 1 filter

cs.CV2026

Dynamic Hub-and-Spoke Memory for Streaming Video Understanding

Xinru Jiang, Lin Zhao, Xi Xiao +7

Streaming video understanding requires answering questions at arbitrary times over a continuously growing visual stream. The central challenge is to compactly remember long-range h…

cs.CV2026

Staying VIGILant: Mitigating Visual Laziness via Counterfactual Visual Alignment in MLLMs

Xi Xiao, Chen Liu, Chih-Ting Liao +9

Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception, enabling joint reasoning over images and text. Despite inheriting strong reason…

cs.CV2026

PhyGround: Benchmarking Physical Reasoning in Generative World Models

Juyi Lin, Arash Akbari, Yumei He +13

Generative world models are increasingly used for video generation, where learned simulators are expected to capture the physical rules that govern real-world dynamics. However, ev…

cs.CV2026

Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation

Xi Xiao, Chenrui Ma, Yunbei Zhang +7

Low-Rank Adaptation (LoRA) has become a cornerstone of parameter-efficient fine-tuning (PEFT). Yet, its efficacy is hampered by two fundamental limitations: semantic drift, by trea…

cs.CV2026

HIERAMP: Coarse-to-Fine Autoregressive Amplification for Generative Dataset Distillation

Lin Zhao, Xinru Jiang, Xi Xiao +7

Dataset distillation often prioritizes global semantic proximity when creating small surrogate datasets for original large-scale ones. However, object semantics are inherently hier…

cs.CV2026

S2DiT: Sandwich Diffusion Transformer for Mobile Streaming Video Generation

Lin Zhao, Yushu Wu, Aleksei Lebedev +11

Diffusion Transformers (DiTs) have recently improved video generation quality. However, their heavy computational cost makes real-time or on-device generation infeasible. In this w…