4 accepted papers
Video understanding has made remarkable progress in recent years, largely driven by advances in deep models and the availability of large-scale annotated datasets. However, the robustness of these models to domain shifts encountered in real-world video applications remains a critical yet underexplor…
With the scale of Large Language Models(LLMs) and the size of the training data continuing to expand, the computational costs required for training or tuning have significantly increased as well. In this work we propose an efficient and effective Large-Scale Data Compression (LSDC) method to substan…
Diffusion models have substantially advanced text-to-image generation, achieving remarkable performance in creating high-quality images from textual prompts. However, they often struggle with accurately generating images representing spatial locations described or implied in the prompts. To address…
In this paper, we propose a novel framework for hand localization and pose estimation from a single depth image. For hand localization, unlike most existing methods that using heuristic strategies, e.g. color segmentation, we propose Hierarchical Hand location Networks (HHLN) to estimate the hand lo…