1 citations · 1 across the 8 of their papers we have counts for
8 papers
Rotation-free Online Handwritten Character Recognition Using Linear Recurrent Units
Zhe Ling, Sicheng Yu, Danyu Yang
Online handwritten character recognition leverages stroke order and dynamic features, which generally provide higher accuracy and robustness compared with offline recognition. Howe…
AutoV: Loss-Oriented Ranking for Visual Prompt Retrieval in LVLMs
Yuan Zhang, Chun-Kai Fan, Sicheng Yu +6
Inspired by text prompts in large language models, visual prompts have been explored to enhance the perceptual capabilities of large vision-language models (LVLMs). However, perfor…
3D Question Answering via only 2D Vision-Language Models
Fengyun Wang, Sicheng Yu, Jiawei Wu +3
Large vision-language models (LVLMs) have significantly advanced numerous fields. In this work, we explore how to harness their potential to address 3D scene understanding tasks, u…
Sparse-to-Dense: A Free Lunch for Lossless Acceleration of Video Understanding in LLMs
Xuan Zhang, Cunxiao Du, Sicheng Yu +4
Due to the auto-regressive nature of current video large language models (Video-LLMs), the inference latency increases as the input sequence length grows, posing challenges for the…
Reverse Modeling in Large Language Models
Sicheng Yu, Yuanchen Xu, Cunxiao Du +5
Humans are accustomed to reading and writing in a forward manner, and this natural bias extends to text understanding in auto-regressive large language models (LLMs). This paper in…
Frame-Voyager: Learning to Query Frames for Video Large Language Models
Sicheng Yu, Chengkai Jin, Huanyu Wang +9
Video Large Language Models (Video-LLMs) have made remarkable progress in video understanding tasks. However, they are constrained by the maximum length of input tokens, making it…
OVFoodSeg: Elevating Open-Vocabulary Food Image Segmentation via Image-Informed Textual Representation
Xiongwei Wu, Sicheng Yu, Ee-Peng Lim +1
In the realm of food computing, segmenting ingredients from images poses substantial challenges due to the large intra-class variance among the same ingredients, the emergence of n…
GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding
Cunxiao Du, Jing Jiang, Xu Yuanchen +8
Speculative decoding is a relatively new decoding framework that leverages small and efficient draft models to reduce the latency of LLMs. In this study, we introduce GliDe and CaP…