13 citations · 16 across the 7 of their papers we have counts for
8 papers
Beyond UV Mapping: Mesh Texture Compression via Surface-Aligned Texture Fields
Jianqiang Wang, Junhui Hou, Siyu Ren +2
Mesh texture compression typically relies on 2D UV atlases, whose chart discontinuities and mapping overhead can limit coding efficiency. To tackle this challenge, we introduce Tex…
ABC: Adaptive BayesNet Structure Learning for Computational Scalable Multi-task Image Compression
Yufeng Zhang, Wenrui Dai, Hang Yu +4
Neural Image Compression (NIC) has revolutionized image compression with its superior rate-distortion performance and multi-task capabilities, supporting both human visual percepti…
Rodimus*: Breaking the Accuracy-Efficiency Trade-Off with Efficient Attentions
Zhihao He, Hang Yu, Zi Gong +3
Recent advancements in Transformer-based large language models (LLMs) have set new standards in natural language processing. However, the classical softmax attention incurs signifi…
DAC: 2D-3D Retrieval with Noisy Labels via Divide-and-Conquer Alignment and Correction
Chaofan Gan, Yuanpeng Tu, Yuxi Li +1
With the recent burst of 2D and 3D data, cross-modal retrieval has attracted increasing attention recently. However, manual labeling by non-experts will inevitably introduce corrup…
NeCGS: Neural Compression for 3D Geometry Sets
Siyu Ren, Junhui Hou, Weiyao Lin +1
We present NeCGS, the first neural compression paradigm, which can compress a geometry set encompassing thousands of detailed and diverse 3D mesh models by up to 900 times with hig…
Collaborative Weakly Supervised Video Correlation Learning for Procedure-Aware Instructional Video Analysis
Tianyao He, Huabin Liu, Yuxi Li +4
Video Correlation Learning (VCL), which aims to analyze the relationships between videos, has been widely studied and applied in various general video tasks. However, applying VCL…
Density Matters: Improved Core-set for Active Domain Adaptive Segmentation
Shizhan Liu, Zhengkai Jiang, Yuxi Li +3
Active domain adaptation has emerged as a solution to balance the expensive annotation cost and the performance of trained models in semantic segmentation. However, existing works…
BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis
Zelin Ni, Hang Yu, Shizhan Liu +2
Bases have become an integral part of modern deep learning-based models for time series forecasting due to their ability to act as feature extractors or future references. To be ef…