1 citations · 1 across the 8 of their papers we have counts for
8 papers
Enhancing Few-Shot Out-of-Distribution Detection via the Refinement of Foreground and Background
Tianyu Li, Zongqian Wu, Songyue Cai +2
CLIP-based foreground-background (FG-BG) decomposition methods have demonstrated remarkable effectiveness in improving few-shot out-of-distribution (OOD) detection performance. How…
Background Prompt for Few-Shot Out-of-Distribution Detection
Songyue Cai, Zongqian Wu, Yujie Mo +4
Existing foreground-background (FG-BG) decomposition methods for the few-shot out-of-distribution (FS-OOD) detection often suffer from low robustness due to over-reliance on the lo…
Mitigating Strategy-Selection Bias in Reasoning for More Effective Test-Time Scaling
Zongqian Wu, Baoduo Xu, Tianyu Li +3
Test-time scaling (TTS) has been shown to improve the performance of large language models (LLMs) by sampling and aggregating diverse reasoning paths. However, existing research ha…
Is Depth All You Need? An Exploration of Iterative Reasoning in LLMs
Zongqian Wu, Tianyu Li, Baoduo Xu +4
Deep iterative chain-of-thought (CoT) reasoning enables LLMs to tackle complex tasks by progressively activating relevant pre-trained knowledge. However, it faces challenges in ens…
Rethinking Chain-of-Thought from the Perspective of Self-Training
Zongqian Wu, Baoduo Xu, Ruochen Cui +3
Chain-of-thought (CoT) reasoning has emerged as an effective approach for activating latent capabilities in LLMs. Interestingly, we observe that both CoT reasoning and self-trainin…
Noisy Node Classification by Bi-level Optimization based Multi-teacher Distillation
Yujing Liu, Zongqian Wu, Zhengyu Lu +4
Previous graph neural networks (GNNs) usually assume that the graph data is with clean labels for representation learning, but it is not true in real applications. In this paper, w…
MMGPL: Multimodal Medical Data Analysis with Graph Prompt Learning
Liang Peng, Songyue Cai, Zongqian Wu +3
Prompt learning has demonstrated impressive efficacy in the fine-tuning of multimodal large models to a wide range of downstream tasks. Nonetheless, applying existing prompt learni…
Adaptive Multi-Modality Prompt Learning
Zongqian Wu, Yujing Liu, Mengmeng Zhan +3
Although current prompt learning methods have successfully been designed to effectively reuse the large pre-trained models without fine-tuning their large number of parameters, the…