activity
20172025
most citedCIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection

220 citations · 1.2k across the 63 of their papers we have counts for

collaborators
Showing 2020Show all

11 papers · 1 filter

cs.CV2020★ 9 cited

Heuristic Domain Adaptation

Shuhao Cui, Xuan Jin, Shuhui Wang +2

In visual domain adaptation (DA), separating the domain-specific characteristics from the domain-invariant representations is an ill-posed problem. Existing methods apply different…

cs.CV2020

Semantic Editing On Segmentation Map Via Multi-Expansion Loss

Jianfeng He, Xuchao Zhang, Shuo Lei +4

Semantic editing on segmentation map has been proposed as an intermediate interface for image generation, because it provides flexible and strong assistance in various image genera…

cs.CV2020

Label Decoupling Framework for Salient Object Detection

Jun Wei, Shuhui Wang, Zhe Wu +3

To get more accurate saliency maps, recent methods mainly focus on aggregating multi-level features from fully convolutional network (FCN) and introducing edge information as auxil…

cs.CV2020★ 25 cited

Corner Proposal Network for Anchor-free, Two-stage Object Detection

Kaiwen Duan, Lingxi Xie, Honggang Qi +3

The goal of object detection is to determine the class and location of objects in an image. This paper proposes a novel anchor-free, two-stage framework which first extracts a numb…

cs.LG2020★ 4 cited

Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction

Zhiyong Yang, Qianqian Xu, Xiaochun Cao +1

As an effective learning paradigm against insufficient training samples, Multi-Task Learning (MTL) encourages knowledge sharing across multiple related tasks so as to improve the o…

cs.CV2020★ 11 cited

Parsing-based View-aware Embedding Network for Vehicle Re-Identification

Dechao Meng, Liang Li, Xuejing Liu +6

Vehicle Re-Identification is to find images of the same vehicle from various views in the cross-camera scenario. The main challenges of this task are the large intra-instance dista…