activity
20162023
most citedHierarchical Neural Architecture Search for Deep Stereo Matching

230 citations · 1.1k across the 84 of their papers we have counts for

collaborators
Showing 2023Show all

13 papers · 1 filter

cs.CV2023★ 2 cited

RPEFlow: Multimodal Fusion of RGB-PointCloud-Event for Joint Optical Flow and Scene Flow Estimation

Zhexiong Wan, Yuxin Mao, Jing Zhang +1

Recently, the RGB images and point clouds fusion methods have been proposed to jointly estimate 2D optical flow and 3D scene flow. However, as both conventional RGB cameras and LiD…

cs.CV2023

Transferable Attack for Semantic Segmentation

Mengqi He, Jing Zhang, Zhaoyuan Yang +3

We analysis performance of semantic segmentation models wrt. adversarial attacks, and observe that the adversarial examples generated from a source model fail to attack the target…

cs.CV2023

Unsupervised 3D Pose Estimation with Non-Rigid Structure-from-Motion Modeling

Haorui Ji, Hui Deng, Yuchao Dai +1

Most of the previous 3D human pose estimation work relied on the powerful memory capability of the network to obtain suitable 2D-3D mappings from the training data. Few works have…

cs.CV2023★ 2 cited

Digging into Depth Priors for Outdoor Neural Radiance Fields

Chen Wang, Jiadai Sun, Lina Liu +5

Neural Radiance Fields (NeRF) have demonstrated impressive performance in vision and graphics tasks, such as novel view synthesis and immersive reality. However, the shape-radiance…

cs.CV2023

Digging Into Uncertainty-based Pseudo-label for Robust Stereo Matching

Zhelun Shen, Xibin Song, Yuchao Dai +3

Due to the domain differences and unbalanced disparity distribution across multiple datasets, current stereo matching approaches are commonly limited to a specific dataset and gene…

cs.CV2023

Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation

Mochu Xiang, Jing Zhang, Nick Barnes +1

Effectively measuring and modeling the reliability of a trained model is essential to the real-world deployment of monocular depth estimation (MDE) models. However, the intrinsic i…

cs.CV2023★ 4 cited

Joint Salient Object Detection and Camouflaged Object Detection via Uncertainty-aware Learning

Aixuan Li, Jing Zhang, Yunqiu Lv +4

Salient objects attract human attention and usually stand out clearly from their surroundings. In contrast, camouflaged objects share similar colors or textures with the environmen…

cs.CV2023★ 1 cited

Weakly-supervised Contrastive Learning for Unsupervised Object Discovery

Yunqiu Lv, Jing Zhang, Nick Barnes +1

Unsupervised object discovery (UOD) refers to the task of discriminating the whole region of objects from the background within a scene without relying on labeled datasets, which b…

cs.CV2023★ 9 cited

Contrastive Conditional Latent Diffusion for Audio-visual Segmentation

Yuxin Mao, Jing Zhang, Mochu Xiang +4

We propose a contrastive conditional latent diffusion model for audio-visual segmentation (AVS) to thoroughly investigate the impact of audio, where the correlation between audio a…

cs.CV2023★ 2 cited

Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detection

Aixuan Li, Yuxin Mao, Jing Zhang +1

In this paper, we present a weakly-supervised RGB-D salient object detection model via scribble supervision. Specifically, as a multimodal learning task, we focus on effective mult…

cs.CV2023

The Second Monocular Depth Estimation Challenge

Jaime Spencer, C. Stella Qian, Michaela Trescakova +40

This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC). This edition was open to methods using any form of supervision, includin…

cs.CV2023

Fine-grained Audible Video Description

Xuyang Shen, Dong Li, Jinxing Zhou +9

We explore a new task for audio-visual-language modeling called fine-grained audible video description (FAVD). It aims to provide detailed textual descriptions for the given audibl…

cs.CV2023★ 22 cited

Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring

Hongguang Zhang, Limeng Zhang, Yuchao Dai +2

Contemporary deep learning multi-scale deblurring models suffer from many issues: 1) They perform poorly on non-uniformly blurred images/videos; 2) Simply increasing the model dept…