3 citations · 6 across the 6 of their papers we have counts for
7 papers
Regularizing modality contribution drift in multimodal continual learning
Zhen Zhang, Jielei Chu, Bin Liu +5
Multimodal continual learning (MMCL) aims to acquire new knowledge from multimodal data while retaining previously learned knowledge. Existing MMCL methods primarily mitigate forge…
Causal Disentanglement-Inspired Degradation Representation Learning for Full-Reference Image Quality Assessment
Zhen Zhang, Jielei Chu, Tian Zhang +5
Existing deep network-based full-reference image quality assessment (FR-IQA) models typically work by performing pairwise comparisons of deep features from the reference and distor…
Tackling Small Sample Survival Analysis via Transfer Learning: A Study of Colorectal Cancer Prognosis
Yonghao Zhao, Changtao Li, Chi Shu +7
Survival prognosis is crucial for medical informatics. Practitioners often confront small-sized clinical data, especially cancer patient cases, which can be insufficient to induce…
Self-Supervised Learning for Glass Composition Screening
Meijing Chen, Bin Liu, Ying Liu +1
Glass composition screening is essential for advancing new glass materials, yet the inherent complexity of multicomponent systems presents significant challenges. Current supervise…
Dynamic Backtracking in GFlowNets: Enhancing Decision Steps with Reward-Dependent Adjustment Mechanisms
Shuai Guo, Jielei Chu, Lin Ma +2
Generative Flow Networks (GFlowNets or GFNs) are probabilistic models predicated on Markov flows, and they employ specific amortization algorithms to learn stochastic policies that…
Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook
Xingchen Zou, Yibo Yan, Xixuan Hao +8
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources…
Small Object Detection by DETR via Information Augmentation and Adaptive Feature Fusion
Ji Huang, Hui Wang
The main challenge for small object detection algorithms is to ensure accuracy while pursuing real-time performance. The RT-DETR model performs well in real-time object detection,…