170 citations · 596 across the 62 of their papers we have counts for
13 papers · 1 filter
UniChest: Conquer-and-Divide Pre-training for Multi-Source Chest X-Ray Classification
Tianjie Dai, Ruipeng Zhang, Feng Hong +3
Vision-Language Pre-training (VLP) that utilizes the multi-modal information to promote the training efficiency and effectiveness, has achieved great success in vision recognition…
Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation
Xu Chen, Zida Cheng, Jiangchao Yao +5
Cross-domain CTR (CDCTR) prediction is an important research topic that studies how to leverage meaningful data from a related domain to help CTR prediction in target domain. Most…
Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation
Fei Zhang, Tianfei Zhou, Boyang Li +6
This paper studies the problem of weakly open-vocabulary semantic segmentation (WOVSS), which learns to segment objects of arbitrary classes using mere image-text pairs. Existing w…
Combating Representation Learning Disparity with Geometric Harmonization
Zhihan Zhou, Jiangchao Yao, Feng Hong +3
Self-supervised learning (SSL) as an effective paradigm of representation learning has achieved tremendous success on various curated datasets in diverse scenarios. Nevertheless, w…
Redundancy-Adaptive Multimodal Learning for Imperfect Data
Mengxi Chen, Jiangchao Yao, Linyu Xing +3
Multimodal models trained on complete modality data often exhibit a substantial decrease in performance when faced with imperfect data containing corruptions or missing modalities.…
UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training
Jiayu Lei, Lisong Dai, Haoyun Jiang +9
Magnetic resonance imaging~(MRI) have played a crucial role in brain disease diagnosis, with which a range of computer-aided artificial intelligence methods have been proposed. How…
Bag of Tricks for Long-Tailed Multi-Label Classification on Chest X-Rays
Feng Hong, Tianjie Dai, Jiangchao Yao +2
Clinical classification of chest radiography is particularly challenging for standard machine learning algorithms due to its inherent long-tailed and multi-label nature. However, f…
Balanced Destruction-Reconstruction Dynamics for Memory-replay Class Incremental Learning
Yuhang Zhou, Jiangchao Yao, Feng Hong +2
Class incremental learning (CIL) aims to incrementally update a trained model with the new classes of samples (plasticity) while retaining previously learned ability (stability). T…
Class-Balancing Diffusion Models
Yiming Qin, Huangjie Zheng, Jiangchao Yao +2
Diffusion-based models have shown the merits of generating high-quality visual data while preserving better diversity in recent studies. However, such observation is only justified…
Zero-shot Composed Text-Image Retrieval
Yikun Liu, Jiangchao Yao, Ya Zhang +2
In this paper, we consider the problem of composed image retrieval (CIR), it aims to train a model that can fuse multi-modal information, e.g., text and images, to accurately retri…
Towards Efficient Task-Driven Model Reprogramming with Foundation Models
Shoukai Xu, Jiangchao Yao, Ran Luo +5
Vision foundation models exhibit impressive power, benefiting from the extremely large model capacity and broad training data. However, in practice, downstream scenarios may only s…
Latent Class-Conditional Noise Model
Jiangchao Yao, Bo Han, Zhihan Zhou +2
Learning with noisy labels has become imperative in the Big Data era, which saves expensive human labors on accurate annotations. Previous noise-transition-based methods have achie…
Long-Tailed Partial Label Learning via Dynamic Rebalancing
Feng Hong, Jiangchao Yao, Zhihan Zhou +2
Real-world data usually couples the label ambiguity and heavy imbalance, challenging the algorithmic robustness of partial label learning (PLL) and long-tailed learning (LT). The s…