6 papers
Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models
Yolo Y. Tang, Jing Bi, Pinxin Liu +24
Video understanding represents the most challenging frontier in computer vision, requiring models to reason about complex spatiotemporal relationships, long-term dependencies, and…
Harnessing the Computation Redundancy in ViTs to Boost Adversarial Transferability
Jiani Liu, Zhiyuan Wang, Zeliang Zhang +4
Vision Transformers (ViTs) have demonstrated impressive performance across a range of applications, including many safety-critical tasks. However, their unique architectural proper…
CalibQuant: 1-Bit KV Cache Quantization for Multimodal LLMs
Insu Han, Zeliang Zhang, Zhiyuan Wang +8
Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance across diverse applications. However, their computational overhead during deployment remains a cri…
Generative AI for Cel-Animation: A Survey
Yolo Y. Tang, Junjia Guo, Pinxin Liu +14
Traditional Celluloid (Cel) Animation production pipeline encompasses multiple essential steps, including storyboarding, layout design, keyframe animation, inbetweening, and colori…
Scalable CP Decomposition for Tensor Learning using GPU Tensor Cores
Zeliang Zhang, Zhuo Liu, Susan Liang +4
CP decomposition is a powerful tool for data science, especially gene analysis, deep learning, and quantum computation. However, the application of tensor decomposition is largely…
Diversifying the High-level Features for better Adversarial Transferability
Zhiyuan Wang, Zeliang Zhang, Siyuan Liang +1
Given the great threat of adversarial attacks against Deep Neural Networks (DNNs), numerous works have been proposed to boost transferability to attack real-world applications. How…