activity
20142024
most citedCompressing Deep Convolutional Networks using Vector Quantization

1k citations · 1.2k across the 45 of their papers we have counts for

collaborators

49 papers

cs.CV20241 cited

Open-Vocabulary Remote Sensing Image Semantic Segmentation

Qinglong Cao, Yuntian Chen, Chao Ma +1

Open-vocabulary image semantic segmentation (OVS) seeks to segment images into semantic regions across an open set of categories. Existing OVS methods commonly depend on foundation…

cs.CV20241 cited

LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation

Henghui Ding, Lingyi Hong, Chang Liu +30

Despite the promising performance of current video segmentation models on existing benchmarks, these models still struggle with complex scenes. In this paper, we introduce the 6th…

cs.CV2024

POA: Pre-training Once for Models of All Sizes

Yingying Zhang, Xin Guo, Jiangwei Lao +7

Large-scale self-supervised pre-training has paved the way for one foundation model to handle many different vision tasks. Most pre-training methodologies train a single model of a…

eess.IV20242 cited

Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring

Chengxu Liu, Xuan Wang, Xiangyu Xu +4

Eliminating image blur produced by various kinds of motion has been a challenging problem. Dominant approaches rely heavily on model capacity to remove blurring by reconstructing r…

cs.CV2024

No More Ambiguity in 360° Room Layout via Bi-Layout Estimation

Yu-Ju Tsai, Jin-Cheng Jhang, Jingjing Zheng +5

Inherent ambiguity in layout annotations poses significant challenges to developing accurate 360° room layout estimation models. To address this issue, we propose a novel Bi-Layout…

cs.CV2024

Spatial-Temporal Multi-level Association for Video Object Segmentation

Deshui Miao, Xin Li, Zhenyu He +2

Existing semi-supervised video object segmentation methods either focus on temporal feature matching or spatial-temporal feature modeling. However, they do not address the issues o…