Showing 1-5 of 5 results
- Sort
Filter Results
Show
Sort By
Results
Dynamic Correlation Learning and Regularization for Multi-Label Confidence Calibration
Tianshui Chen;Weihang Wang;Tao Pu;Jinghui Qin;Zhijing Yang;Jie Liu;Liang Lin
Modern visual recognition models often display overconfidence due to their reliance on complex deep neural networks and one-hot target supervision, resulting in unreliable confidence scores that necessitate calibration. While current confidence calibration techniques primarily address single-label scenarios, there is a lack of focus on more practical and generalizable multi-label contexts. This pa...Show More
Category-Adaptive Label Discovery and Noise Rejection for Multi-Label Recognition With Partial Positive Labels
Tao Pu;Qianru Lao;Hefeng Wu;Tianshui Chen;Ling Tian;Jie Liu;Liang Lin
As a cost-effective alternative to standard multi-label learning, the multi-label image recognition with partial positive labels (MLR-PPL) task attracts increasing attention, in which merely a portion of positive labels are given while the rest of positive labels and all negative labels are missing. To facilitate this task, we propose a novel framework that leverages semantic correlation among dif...Show More
Spatial–Temporal Knowledge-Embedded Transformer for Video Scene Graph Generation
Video scene graph generation (VidSGG) aims to identify objects in visual scenes and infer their relationships for a given video. It requires not only a comprehensive understanding of each object scattered on the whole scene but also a deep dive into their temporal motions and interactions. Inherently, object pairs and their relationships enjoy spatial co-occurrence correlations within each image a...Show More
Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning
Tianshui Chen;Tao Pu;Hefeng Wu;Yuan Xie;Lingbo Liu;Liang Lin
Facial expression recognition (FER) has received significant attention in the past decade with witnessed progress, but data inconsistencies among different FER datasets greatly hinder the generalization ability of the models learned on one dataset to another. Recently, a series of cross-domain FER algorithms (CD-FERs) have been extensively developed to address this issue. Although each declares to...Show More
AU-Expression Knowledge Constrained Representation Learning for Facial Expression Recognition
2021 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2021 | Conference Paper |
Cited by: Papers (16)
Recognizing human emotion/expressions automatically is quite an expected ability for intelligent robotics, as it can promote better communication and cooperation with humans. Current deep-learning-based algorithms may achieve impressive performance in some lab-controlled environments, but they always fail to recognize the expressions accurately for the uncontrolled in-the-wild situation. Fortunate...Show More
IEEE Account
Purchase Details
Profile Information
Need Help?
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Contact Us
- Help
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Sitemap
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.