Search Results for author:
Found 6 papers, 5 papers with code
Generalizing Person Re-Identification by Camera-Aware Invariance Learning and Cross-Domain Mixup
Despite the impressive performance under the single-domain setup, current fully-supervised models for person re-identification (re-ID) degrade significantly when deployed to an unseen domain.
The Devil Is in the Details: Window-based Attention for Image Compression
Inspired by recent progresses of Vision Transformer (ViT) and Swin Transformer, we found that combining the local-aware attention mechanism with the global-related feature learning could meet the expectation in image compression.
Ranked #1 on
Image Compression
on kodak
Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation
To address this problem, it is a good choice to learn to segment with weak supervision from bounding boxes.
Weakly-supervised Learning
Weakly supervised Semantic Segmentation
+1
Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning
In this paper, we propose a method for air quality measurement based on double-channel convolutional neural network ensemble learning to solve the problem of feature extraction for different parts of environmental images.
CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation with only image-level labels saves large human effort to annotate pixel-level labels.
Mask-Guided Contrastive Attention Model for Person Re-Identification
We may be the first one to successfully introduce the binary mask into person ReID task and the first one to propose region-level contrastive learning.
