activity
20122023
most citedDeep High-Resolution Representation Learning for Visual Recognition

351 citations · 2k across the 89 of their papers we have counts for

collaborators

91 papers

cs.CV2023

Likelihood-Based Text-to-Image Evaluation with Patch-Level Perceptual and Semantic Credit Assignment

Qi Chen, Chaorui Deng, Zixiong Huang +3

Text-to-image synthesis has made encouraging progress and attracted lots of public attention recently. However, popular evaluation metrics in this area, like the Inception Score an…

cs.CV2023★ 1 cited

Nav: Action-Aware Zero-Shot Robot Navigation by Exploiting Vision-and-Language Ability of Foundation Models

Peihao Chen, Xinyu Sun, Hongyan Zhi +5

We study the task of zero-shot vision-and-language navigation (ZS-VLN), a practical yet challenging problem in which an agent learns to navigate following a path described by langu…

cs.CV2023★ 1 cited

Cross-Ray Neural Radiance Fields for Novel-view Synthesis from Unconstrained Image Collections

Yifan Yang, Shuhai Zhang, Zixiong Huang +2

Neural Radiance Fields (NeRF) is a revolutionary approach for rendering scenes by sampling a single ray per pixel and it has demonstrated impressive capabilities in novel-view synt…

cs.CV2023

Learning Vision-and-Language Navigation from YouTube Videos

Kunyang Lin, Peihao Chen, Diwei Huang +3

Vision-and-language navigation (VLN) requires an embodied agent to navigate in realistic 3D environments using natural language instructions. Existing VLN methods suffer from train…

cs.CV2023★ 2 cited

CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation

Lizhao Liu, Zhuangwei Zhuang, Shangxin Huang +5

We study the task of weakly-supervised point cloud semantic segmentation with sparse annotations (e.g., less than 0.1% points are labeled), aiming to reduce the expensive cost of d…

cs.LG2023★ 4 cited

Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score

Shuhai Zhang, Feng Liu, Jiahao Yang +4

Adversarial detection aims to determine whether a given sample is an adversarial one based on the discrepancy between natural and adversarial distributions. Unfortunately, estimati…