7 citations · 28 across the 19 of their papers we have counts for
20 papers
From Inertia to Objectivity: Improving Deep Research Agents with Noise Isolation
Xiangxin Zhang, Zhanwei Zhang, Zhihang Fu +2
Web search agents powered by Large Language Models (LLMs) show strong promise, but deep research tasks expose a recurring failure mode: once an agent has produced a query, plan, or…
Interact-RAG: Reason and Interact with the Corpus, Beyond Black-Box Retrieval
Yulong Hui, Chao Chen, Zhihang Fu +3
Retrieval-Augmented Generation (RAG) has significantly enhanced LLMs by incorporating external information. However, prevailing agentic RAG approaches are constrained by a critical…
Controlling Thinking Speed in Reasoning Models
Zhengkai Lin, Zhihang Fu, Ze Chen +6
Human cognition is theorized to operate in two modes: fast, intuitive System 1 thinking and slow, deliberate System 2 thinking. While current Large Reasoning Models (LRMs) excel at…
Human-centered Interactive Learning via MLLMs for Text-to-Image Person Re-identification
Yang Qin, Chao Chen, Zhihang Fu +3
Despite remarkable advancements in text-to-image person re-identification (TIReID) facilitated by the breakthrough of cross-modal embedding models, existing methods often struggle…
ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL
Yang Qin, Chao Chen, Zhihang Fu +4
Despite the significant advancements in Text-to-SQL (Text2SQL) facilitated by large language models (LLMs), the latest state-of-the-art techniques are still trapped in the in-conte…
Delving into the Reversal Curse: How Far Can Large Language Models Generalize?
Zhengkai Lin, Zhihang Fu, Kai Liu +6
While large language models (LLMs) showcase unprecedented capabilities, they also exhibit certain inherent limitations when facing seemingly trivial tasks. A prime example is the r…
Category-Extensible Out-of-Distribution Detection via Hierarchical Context Descriptions
Kai Liu, Zhihang Fu, Chao Chen +5
The key to OOD detection has two aspects: generalized feature representation and precise category description. Recently, vision-language models such as CLIP provide significant adv…
Structure-aware Domain Knowledge Injection for Large Language Models
Kai Liu, Ze Chen, Zhihang Fu +6
This paper introduces a pioneering methodology, termed StructTuning, to efficiently transform foundation Large Language Models (LLMs) into domain specialists. It significantly redu…
Enhancing LLM's Cognition via Structurization
Kai Liu, Zhihang Fu, Chao Chen +6
When reading long-form text, human cognition is complex and structurized. While large language models (LLMs) process input contexts through a causal and sequential perspective, thi…
Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution
Kai Liu, Zhihang Fu, Sheng Jin +6
Detecting and rejecting unknown out-of-distribution (OOD) samples is critical for deployed neural networks to void unreliable predictions. In real-world scenarios, however, the eff…
ESOD: Efficient Small Object Detection on High-Resolution Images
Kai Liu, Zhihang Fu, Sheng Jin +5
Enlarging input images is a straightforward and effective approach to promote small object detection. However, simple image enlargement is significantly expensive on both computati…
ROPO: Robust Preference Optimization for Large Language Models
Xize Liang, Chao Chen, Shuang Qiu +6
Preference alignment is pivotal for empowering large language models (LLMs) to generate helpful and harmless responses. However, the performance of preference alignment is highly s…
Optimal Parameter and Neuron Pruning for Out-of-Distribution Detection
Chao Chen, Zhihang Fu, Kai Liu +3
For a machine learning model deployed in real world scenarios, the ability of detecting out-of-distribution (OOD) samples is indispensable and challenging. Most existing OOD detect…
INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection
Chao Chen, Kai Liu, Ze Chen +5
Knowledge hallucination have raised widespread concerns for the security and reliability of deployed LLMs. Previous efforts in detecting hallucinations have been employed at logit-…
Uncertainty-aware Unsupervised Multi-Object Tracking
Kai Liu, Sheng Jin, Zhihang Fu +3
Without manually annotated identities, unsupervised multi-object trackers are inferior to learning reliable feature embeddings. It causes the similarity-based inter-frame associati…
Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection
Ze Chen, Zhihang Fu, Jianqiang Huang +5
Weakly supervised object detection (WSOD), which is an effective way to train an object detection model using only image-level annotations, has attracted considerable attention fro…
Dynamic Supervisor for Cross-dataset Object Detection
Ze Chen, Zhihang Fu, Jianqiang Huang +6
The application of cross-dataset training in object detection tasks is complicated because the inconsistency in the category range across datasets transforms fully supervised learn…
SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection
Ze Chen, Zhihang Fu, Rongxin Jiang +2
Based on the framework of multiple instance learning (MIL), tremendous works have promoted the advances of weakly supervised object detection (WSOD). However, most MIL-based method…
HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation
Chao Chen, Zhihang Fu, Zhihong Chen +4
Minimizing the discrepancy of feature distributions between different domains is one of the most promising directions in unsupervised domain adaptation. From the perspective of dis…
Towards Self-similarity Consistency and Feature Discrimination for Unsupervised Domain Adaptation
Chao Chen, Zhihang Fu, Zhihong Chen +3
Recent advances in unsupervised domain adaptation mainly focus on learning shared representations by global distribution alignment without considering class information across doma…