11 citations · 15 across the 15 of their papers we have counts for
6 papers · 1 filter
Improving Visual Discriminability of CLIP for Training-Free Open-Vocabulary Semantic Segmentation
Jinxin Zhou, Jiachen Jiang, Zhihui Zhu
Extending CLIP models to semantic segmentation remains challenging due to the misalignment between their image-level pre-training objectives and the pixel-level visual understandin…
From Emergence to Control: Probing and Modulating Self-Reflection in Language Models
Xudong Zhu, Jiachen Jiang, Mohammad Mahdi Khalili +1
Self-reflection -- the ability of a large language model (LLM) to revisit, evaluate, and revise its own reasoning -- has recently emerged as a powerful behavior enabled by reinforc…
Understanding Task Vectors in In-Context Learning: Emergence, Functionality, and Limitations
Yuxin Dong, Jiachen Jiang, Zhihui Zhu +1
Task vectors offer a compelling mechanism for accelerating inference in in-context learning (ICL) by distilling task-specific information into a single, reusable representation. De…
Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models
Jiachen Jiang, Jinxin Zhou, Bo Peng +2
Achieving better alignment between vision embeddings and Large Language Models (LLMs) is crucial for enhancing the abilities of Multimodal LLMs (MLLMs), particularly for recent mod…
From Compression to Expression: A Layerwise Analysis of In-Context Learning
Jiachen Jiang, Yuxin Dong, Jinxin Zhou +1
In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks without weight updates by learning from demonstration sequences. While ICL shows strong empiric…
Learning Where and What to Restore for Composite Image Restoration
Jiachen Jiang, Tianyu Ding, Ke Zhang +5
Real-world degraded images often contain multiple co-occurring degradation types, making composite image restoration fundamentally different from the single-degradation setting ass…