9 papers
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…
Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Model Enhancement
Qianhan Feng, Wenshuo Li, Tong Lin +1
Vision-Language Models (VLMs) bring powerful understanding and reasoning capabilities to multimodal tasks. Meanwhile, the great need for capable aritificial intelligence on mobile…
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…
Visual-textual Dermatoglyphic Animal Biometrics: A First Case Study on Panthera tigris
Wenshuo Li, Majid Mirmehdi, Tilo Burghardt
Biologists have long combined visuals with textual field notes to re-identify (Re-ID) animals. Contemporary AI tools automate this for species with distinctive morphological featur…
ViSpec: Accelerating Vision-Language Models with Vision-Aware Speculative Decoding
Jialiang Kang, Han Shu, Wenshuo Li +2
Speculative decoding is a widely adopted technique for accelerating inference in large language models (LLMs), yet its application to vision-language models (VLMs) remains underexp…
TinySAM: Pushing the Envelope for Efficient Segment Anything Model
Han Shu, Wenshuo Li, Yehui Tang +5
Recently segment anything model (SAM) has shown powerful segmentation capability and has drawn great attention in computer vision fields. Massive following works have developed var…