activity
20202024
most citedMOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models

22 citations · 30 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2024

CamFreeDiff: Camera-free Image to Panorama Generation with Diffusion Model

Xiaoding Yuan, Shitao Tang, Kejie Li +2

This paper introduces Camera-free Diffusion (CamFreeDiff) model for 360-degree image outpainting from a single camera-free image and text description. This method distinguishes its…

cs.CV2023★ 2 cited

Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape

Jiacong Xu, Yi Zhang, Jiawei Peng +17

Accurately estimating the 3D pose and shape is an essential step towards understanding animal behavior, and can potentially benefit many downstream applications, such as wildlife c…

cs.CV2023

Robust Category-Level 3D Pose Estimation from Synthetic Data

Jiahao Yang, Wufei Ma, Angtian Wang +3

Obtaining accurate 3D object poses is vital for numerous computer vision applications, such as 3D reconstruction and scene understanding. However, annotating real-world objects is…

cs.CV2022★ 22 cited

MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models

Chenglin Yang, Siyuan Qiao, Qihang Yu +5

This paper presents MOAT, a family of neural networks that build on top of MObile convolution (i.e., inverted residual blocks) and ATtention. Unlike the current works that stack se…

cs.CV2021★ 2 cited

PartImageNet: A Large, High-Quality Dataset of Parts

Ju He, Shuo Yang, Shaokang Yang +7

It is natural to represent objects in terms of their parts. This has the potential to improve the performance of algorithms for object recognition and segmentation but can also hel…

cs.CV2021★ 3 cited

Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles

Qing Liu, Adam Kortylewski, Zhishuai Zhang +7

Part segmentations provide a rich and detailed part-level description of objects. However, their annotation requires an enormous amount of work, which makes it difficult to apply s…

cs.CV2020★ 1 cited

Robust Instance Segmentation through Reasoning about Multi-Object Occlusion

Xiaoding Yuan, Adam Kortylewski, Yihong Sun +1

Analyzing complex scenes with Deep Neural Networks is a challenging task, particularly when images contain multiple objects that partially occlude each other. Existing approaches t…