7 citations · 13 across the 37 of their papers we have counts for
12 papers · 1 filter
Sketch-in-Latents: Eliciting Unified Reasoning in MLLMs
Jintao Tong, Jiaqi Gu, Yujing Lou +5
While Multimodal Large Language Models (MLLMs) excel at visual understanding tasks through text reasoning, they often fall short in scenarios requiring visual imagination. Unlike c…
Decoupling Template Bias in CLIP: Harnessing Empty Prompts for Enhanced Few-Shot Learning
Zhenyu Zhang, Guangyao Chen, Yixiong Zou +2
The Contrastive Language-Image Pre-Training (CLIP) model excels in few-shot learning by aligning visual and textual representations. Our study shows that template-sample similarity…
Start Small, Think Big: Curriculum-based Relative Policy Optimization for Visual Grounding
Qingyang Yan, Guangyao Chen, Yixiong Zou
Chain-of-Thought (CoT) prompting has recently shown significant promise across various NLP and computer vision tasks by explicitly generating intermediate reasoning steps. However,…
Quantifying Distributional Invariance in Causal Subgraph for IRM-Free Graph Generalization
Yang Qiu, Yixiong Zou, Jun Wang +3
Out-of-distribution generalization under distributional shifts remains a critical challenge for graph neural networks. Existing methods generally adopt the Invariant Risk Minimizat…
HiDe: Rethinking The Zoom-IN method in High Resolution MLLMs via Hierarchical Decoupling
Xianjie Liu, Yiman Hu, Yixiong Zou +3
Multimodal Large Language Models (MLLMs) have made significant strides in visual understanding tasks. However, their performance on high-resolution images remains suboptimal. While…
Revisiting Pool-based Prompt Learning for Few-shot Class-incremental Learning
Yongwei Jiang, Yixiong Zou, Yuhua Li +1
Few-Shot Class-Incremental Learning (FSCIL) faces dual challenges of data scarcity and incremental learning in real-world scenarios. While pool-based prompting methods have demonst…