91 citations · 231 across the 17 of their papers we have counts for
17 papers
G2PDiffusion: Cross-Species Genotype-to-Phenotype Prediction via Evolutionary Diffusion
Mengdi Liu, Zhangyang Gao, Hong Chang +3
Understanding how genes influence phenotype across species is a fundamental challenge in genetic engineering, which will facilitate advances in various fields such as crop breeding…
UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language Models
Jiachen Liang, Ruibing Hou, Minyang Hu +3
Pre-trained vision-language models (e.g., CLIP) have shown powerful zero-shot transfer capabilities. But they still struggle with domain shifts and typically require labeled data t…
HERM: Benchmarking and Enhancing Multimodal LLMs for Human-Centric Understanding
Keliang Li, Zaifei Yang, Jiahe Zhao +5
The significant advancements in visual understanding and instruction following from Multimodal Large Language Models (MLLMs) have opened up more possibilities for broader applicati…
Generalized Semi-Supervised Learning via Self-Supervised Feature Adaptation
Jiachen Liang, Ruibing Hou, Hong Chang +3
Traditional semi-supervised learning (SSL) assumes that the feature distributions of labeled and unlabeled data are consistent which rarely holds in realistic scenarios. In this pa…
MGPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation
Mingshuang Luo, Ruibing Hou, Zhuo Li +4
This paper presents MGPT, an advanced ultimodal, ultitask framework for otion comprehension and generation. MGPT operates on three funda…
Clothes-Changing Person Re-Identification with Feasibility-Aware Intermediary Matching
Jiahe Zhao, Ruibing Hou, Hong Chang +4
Current clothes-changing person re-identification (re-id) approaches usually perform retrieval based on clothes-irrelevant features, while neglecting the potential of clothes-relev…
Task Attribute Distance for Few-Shot Learning: Theoretical Analysis and Applications
Minyang Hu, Hong Chang, Zong Guo +3
Few-shot learning (FSL) aims to learn novel tasks with very few labeled samples by leveraging experience from \emph{related} training tasks. In this paper, we try to understand FSL…
Bias Propagation in Federated Learning
Hongyan Chang, Reza Shokri
We show that participating in federated learning can be detrimental to group fairness. In fact, the bias of a few parties against under-represented groups (identified by sensitive…
Dual Compensation Residual Networks for Class Imbalanced Learning
Ruibing Hou, Hong Chang, Bingpeng Ma +2
Learning generalizable representation and classifier for class-imbalanced data is challenging for data-driven deep models. Most studies attempt to re-balance the data distribution,…
On The Impact of Machine Learning Randomness on Group Fairness
Prakhar Ganesh, Hongyan Chang, Martin Strobel +1
Statistical measures for group fairness in machine learning reflect the gap in performance of algorithms across different groups. These measures, however, exhibit a high variance b…
Diversity-Measurable Anomaly Detection
Wenrui Liu, Hong Chang, Bingpeng Ma +2
Reconstruction-based anomaly detection models achieve their purpose by suppressing the generalization ability for anomaly. However, diverse normal patterns are consequently not wel…
Clothes-Changing Person Re-identification with RGB Modality Only
Xinqian Gu, Hong Chang, Bingpeng Ma +3
The key to address clothes-changing person re-identification (re-id) is to extract clothes-irrelevant features, e.g., face, hairstyle, body shape, and gait. Most current works main…
Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework
Botao Ye, Hong Chang, Bingpeng Ma +2
The current popular two-stream, two-stage tracking framework extracts the template and the search region features separately and then performs relation modeling, thus the extracted…
On the Privacy Risks of Algorithmic Fairness
Hongyan Chang, Reza Shokri
Algorithmic fairness and privacy are essential pillars of trustworthy machine learning. Fair machine learning aims at minimizing discrimination against protected groups by, for exa…
On Adversarial Bias and the Robustness of Fair Machine Learning
Hongyan Chang, Ta Duy Nguyen, Sasi Kumar Murakonda +2
Optimizing prediction accuracy can come at the expense of fairness. Towards minimizing discrimination against a group, fair machine learning algorithms strive to equalize the behav…
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer
Hongyan Chang, Virat Shejwalkar, Reza Shokri +1
Collaborative (federated) learning enables multiple parties to train a model without sharing their private data, but through repeated sharing of the parameters of their local model…
Deeply Coupled Auto-encoder Networks for Cross-view Classification
Wen Wang, Zhen Cui, Hong Chang +2
The comparison of heterogeneous samples extensively exists in many applications, especially in the task of image classification. In this paper, we propose a simple but effective co…