3 citations · 5 across the 7 of their papers we have counts for
7 papers
MoE3D: A Mixture-of-Experts Module for 3D Reconstruction
Zichen Wang, Ang Cao, Liam J. Wang +1
We propose a simple yet effective approach to enhance the performance of feed-forward 3D reconstruction models. Existing methods often struggle near depth discontinuities, where st…
PKI: Prior Knowledge-Infused Neural Network for Few-Shot Class-Incremental Learning
Kexin Baoa, Fanzhao Lin, Zichen Wang +3
Few-shot class-incremental learning (FSCIL) aims to continually adapt a model on a limited number of new-class examples, facing two well-known challenges: catastrophic forgetting a…
DreamSwapV: Mask-guided Subject Swapping for Any Customized Video Editing
Weitao Wang, Zichen Wang, Hongdeng Shen +6
With the rapid progress of video generation, demand for customized video editing is surging, where subject swapping constitutes a key component yet remains under-explored. Prevaili…
Toward Rich Video Human-Motion2D Generation
Ruihao Xi, Xuekuan Wang, Yongcheng Li +5
Generating realistic and controllable human motions, particularly those involving rich multi-character interactions, remains a significant challenge due to data scarcity and the co…
Real-Time Human Action Recognition on Embedded Platforms
Ruiqi Wang, Zichen Wang, Peiqi Gao +7
With advancements in computer vision and deep learning, video-based human action recognition (HAR) has become practical. However, due to the complexity of the computation pipeline,…
Fine-Grained Prototypes Distillation for Few-Shot Object Detection
Zichen Wang, Bo Yang, Haonan Yue +1
Few-shot object detection (FSOD) aims at extending a generic detector for novel object detection with only a few training examples. It attracts great concerns recently due to the p…
UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World Understanding
Bowen Shi, Peisen Zhao, Zichen Wang +8
Vision-language foundation models, represented by Contrastive Language-Image Pre-training (CLIP), have gained increasing attention for jointly understanding both vision and textual…