activity
20172025
most citedWeakly Supervised Instance Segmentation using Class Peak Response

38 citations · 97 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV2025

CAGS: Open-Vocabulary 3D Scene Understanding with Context-Aware Gaussian Splatting

Wei Sun, Yanzhao Zhou, Jianbin Jiao +1

Open-vocabulary 3D scene understanding is crucial for applications requiring natural language-driven spatial interpretation, such as robotics and augmented reality. While 3D Gaussi…

cs.CV2024

Depth-guided Texture Diffusion for Image Semantic Segmentation

Wei Sun, Yuan Li, Qixiang Ye +2

Depth information provides valuable insights into the 3D structure especially the outline of objects, which can be utilized to improve the semantic segmentation tasks. However, a n…

cs.CV2024

Correspondence-Guided SfM-Free 3D Gaussian Splatting for NVS

Wei Sun, Xiaosong Zhang, Fang Wan +4

Novel View Synthesis (NVS) without Structure-from-Motion (SfM) pre-processed camera poses--referred to as SfM-free methods--is crucial for promoting rapid response capabilities and…

cs.CV2024

Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian

Wei Sun, Qi Zhang, Yanzhao Zhou +3

3D Gaussian splatting has demonstrated impressive performance in real-time novel view synthesis. However, achieving successful reconstruction from RGB images generally requires mul…

cs.CV2024

Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object Detection

Feng Liu, Tengteng Huang, Qianjing Zhang +5

Multi-view 3D object detection systems often struggle with generating precise predictions due to the challenges in estimating depth from images, increasing redundant and incorrect…

cs.CV2019★ 2 cited

Deep Reason: A Strong Baseline for Real-World Visual Reasoning

Chenfei Wu, Yanzhao Zhou, Gen Li +3

This paper presents a strong baseline for real-world visual reasoning (GQA), which achieves 60.93% in GQA 2019 challenge and won the sixth place. GQA is a large dataset with 22M qu…

cs.CV2018★ 38 cited

Weakly Supervised Instance Segmentation using Class Peak Response

Yanzhao Zhou, Yi Zhu, Qixiang Ye +2

Weakly supervised instance segmentation with image-level labels, instead of expensive pixel-level masks, remains unexplored. In this paper, we tackle this challenging problem by ex…

cs.CV2017★ 24 cited

Soft Proposal Networks for Weakly Supervised Object Localization

Yi Zhu, Yanzhao Zhou, Qixiang Ye +2

Weakly supervised object localization remains challenging, where only image labels instead of bounding boxes are available during training. Object proposal is an effective componen…

cs.CV2017★ 33 cited

Oriented Response Networks

Yanzhao Zhou, Qixiang Ye, Qiang Qiu +1

Deep Convolution Neural Networks (DCNNs) are capable of learning unprecedentedly effective image representations. However, their ability in handling significant local and global im…