6 papers · 1 filter
Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning
Hao Sun, Zi-Jun Ding, Da-Wei Zhou
Class-Incremental Learning (CIL) enables models to continuously integrate new knowledge while mitigating catastrophic forgetting. Driven by the remarkable generalization of CLIP, l…
Power Reinforcement Post-Training of Text-to-Image Models with Super-Linear Advantage Shaping
Haoyuan Sun, Jing Wang, Yuxin Song +9
Recently, post-training methods based on reinforcement learning, with a particular focus on Group Relative Policy Optimization (GRPO), have emerged as the robust paradigm for furth…
Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning
Zhen-Hao Xie, Yan Wang, Hao Sun +3
Class-Incremental Learning (CIL) requires a learning system to learn new classes while retaining previously learned knowledge. However, in real-world scenarios such as autonomous d…
One Framework to Rule Them All: Unifying Multimodal Tasks with LLM Neural-Tuning
Hao Sun, Yu Song, Jiaqing Liu +3
Large-scale models have exhibited remarkable capabilities across diverse domains, including automated medical services and intelligent customer support. However, as most large mode…
EPIC: Efficient Prompt Interaction for Text-Image Classification
Xinyao Yu, Hao Sun, Zeyu Ling +5
In recent years, large-scale pre-trained multimodal models (LMMs) generally emerge to integrate the vision and language modalities, achieving considerable success in multimodal tas…
Robust Latent Representation Tuning for Image-text Classification
Hao Sun, Yu Song
Large models have demonstrated exceptional generalization capabilities in computer vision and natural language processing. Recent efforts have focused on enhancing these models wit…