2 citations · 3 across the 9 of their papers we have counts for
9 papers
Ovis-Image Technical Report
Guo-Hua Wang, Liangfu Cao, Tianyu Cui +8
We introduce , a 7B text-to-image model specifically optimized for high-quality text rendering, designed to operate efficiently under stringent computational c…
Hierarchical Semantic Tree Anchoring for CLIP-Based Class-Incremental Learning
Tao Hu, Lan Li, Zhen-Hao Xie +1
Class-Incremental Learning (CIL) enables models to learn new classes continually while preserving past knowledge. Recently, vision-language models like CLIP offer transferable feat…
BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning
Lan Li, Tao Hu, Da-Wei Zhou +3
Class-Incremental Learning (CIL) aims to continually learn new categories without forgetting previously acquired knowledge. Vision-language models such as CLIP offer strong transfe…
Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts
Lan Li, Da-Wei Zhou, Han-Jia Ye +1
Domain-Incremental Learning (DIL) focuses on continual learning in non-stationary environments, requiring models to adjust to evolving domains while preserving historical knowledge…
UniCA: Unified Covariate Adaptation for Time Series Foundation Model
Lu Han, Yu Liu, Lan Li +9
Time Series Foundation Models (TSFMs) have achieved remarkable success through large-scale pretraining. However, their design primarily targets real-valued series, limiting their a…
Visualizing, Rethinking, and Mining the Loss Landscape of Deep Neural Networks
Yichu Xu, Xin-Chun Li, Lan Li +1
The loss landscape of deep neural networks (DNNs) is commonly considered complex and wildly fluctuated. However, an interesting observation is that the loss surfaces plotted along…
Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks
Xin-Chun Li, Jin-Lin Tang, Bo Zhang +2
Exploring the loss landscape offers insights into the inherent principles of deep neural networks (DNNs). Recent work suggests an additional asymmetry of the valley beyond the flat…
Twice Class Bias Correction for Imbalanced Semi-Supervised Learning
Lan Li, Bowen Tao, Lu Han +2
Differing from traditional semi-supervised learning, class-imbalanced semi-supervised learning presents two distinct challenges: (1) The imbalanced distribution of training samples…
CLAF: Contrastive Learning with Augmented Features for Imbalanced Semi-Supervised Learning
Bowen Tao, Lan Li, Xin-Chun Li +1
Due to the advantages of leveraging unlabeled data and learning meaningful representations, semi-supervised learning and contrastive learning have been progressively combined to ac…