collaborators

10 papers

cs.CV2026

CapProbe: Evaluating Detailed Image Captions via Full-Scene Dense Question Answering

Mouxiao Huang, Qiangyu Yan, Borui Jiang +1

Evaluating detailed image captions from Vision-Language Models (VLMs) requires going beyond surface-level semantic similarity. Reference-based metrics (e.g., CIDEr and SPICE) and L…

cs.CV2026

SlimVLM: Sensitivity-aware Dynamic Structured Pruning with Adaptive Visual Token Selection for Efficient Vision-Language Models

Yaozhi Wen, Jialong Guo, Zhenliang Ni +2

While Vision-Language Models (VLMs) have demonstrated remarkable performance in processing and understanding both text and images, their large parameter sizes lead to significant c…

cs.CV2026

SJD-PAC: Accelerating Speculative Jacobi Decoding via Proactive Drafting and Adaptive Continuation

Jialiang Kang, Han Shu, Wenshuo Li +2

Speculative Jacobi Decoding (SJD) offers a draft-model-free approach to accelerate autoregressive text-to-image synthesis. However, the high-entropy nature of visual generation yie…

cs.CL2026

Thinking-while-speaking: A Controlled, Interleaved Reasoning Method for Real-Time Speech Generation

Xuan Du, Qiangyu Yan, Wenshuo Li +4

The thinking-while-speaking paradigm aims to make AI communication more human. A key challenge is maintaining fluent speech while performing deep reasoning. Our method, InterRS, ta…

cs.CV2026

TinySAM 2: Extreme Memory Compression for Efficient Track Anything Model

Zhaoyuan Ding, Yijing Yang, Han Shu +1

Segment Anything Model 2 (SAM 2) serves as a core foundation model in the field of video segmentation. Building upon the original SAM model, it introduces a memory bank mechanism a…

cs.CV2026

VLM-Pruner: Buffering for Spatial Sparsity in an Efficient VLM Centrifugal Token Pruning Paradigm

Zhenkai Wu, Xiaowen Ma, Zhenliang Ni +4

Vision-language models (VLMs) excel at image understanding tasks, but the large number of visual tokens imposes significant computational costs, hindering deployment on mobile devi…