activity
20162023
most citedUC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders

35 citations · 96 across the 20 of their papers we have counts for

collaborators
Showing 2021Show all

5 papers · 1 filter

cs.CV20211 cited

Inferring the Class Conditional Response Map for Weakly Supervised Semantic Segmentation

Weixuan Sun, Jing Zhang, Nick Barnes

Image-level weakly supervised semantic segmentation (WSSS) relies on class activation maps (CAMs) for pseudo labels generation. As CAMs only highlight the most discriminative regio…

cs.LG202110 cited

Dense Uncertainty Estimation

Jing Zhang, Yuchao Dai, Mochu Xiang +7

Deep neural networks can be roughly divided into deterministic neural networks and stochastic neural networks.The former is usually trained to achieve a mapping from input space to…

cs.CV202122 cited

Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion

Shi Qiu, Saeed Anwar, Nick Barnes

Given the prominence of current 3D sensors, a fine-grained analysis on the basic point cloud data is worthy of further investigation. Particularly, real point cloud scenes can intu…

cs.CV2021

Weakly Supervised Video Salient Object Detection

Wangbo Zhao, Jing Zhang, Long Li +3

Significant performance improvement has been achieved for fully-supervised video salient object detection with the pixel-wise labeled training datasets, which are time-consuming an…

cs.CV2021

Recursive Training for Zero-Shot Semantic Segmentation

Ce Wang, Moshiur Farazi, Nick Barnes

General purpose semantic segmentation relies on a backbone CNN network to extract discriminative features that help classify each image pixel into a 'seen' object class (ie., the o…