10 citations · 10 across the 20 of their papers we have counts for
20 papers
Teaching MLLMs to Say No: Generalized Referring Expression Comprehension via Refusal Calibrated GRPO
Xuzheng Yang, Jun Ling, Tao Huang +2
We tackle the challenging yet underexplored task of Generalized Referring Expression Comprehension (GREC), which requires a model to localize the object described by a textual expr…
TF-SNO: Time-Frequency Gated Spectral Neural Operators for Learning Non-Stationary Partial Differential Equations
Yitian Zhou, Chaoning Zhang, Zhenzhen Huang +8
Non-stationary partial differential equations (PDEs) arise throughout scientific computing, where the dominant frequency content and energy distribution can drift over time. While…
Autoregression-Free Neural Operators for Time-Dependent PDEs
Jiaquan Zhang, Caiyan Qin, Haoyu Bian +7
Neural operators learn mappings from function-dependent inputs to solutions, providing an effective framework for solving partial differential equations (PDEs). For time-dependent…
LEGO: LoRA-Enabled Generator-Oriented Framework for Synthetic Image Detection
Yutong Xiao, Ran Ran, Jiwei Wei +4
The rapid advancement of generative technologies has made synthetic images nearly indistinguishable from real ones, thereby creating an urgent need for robust detectors to counter…
Frequency-Aware Semantic Fusion with Gated Injection for AI-generated Image Detection
Shuchang Zhou, Shangkun Wu, Jiwei Wei +4
AI-generated images are becoming increasingly realistic and diverse, posing significant challenges for generalizable detection. While Vision Foundation Models (VFMs) provide rich s…
From Similarity to Structure: Training-free LLM Context Compression with Hybrid Graph Priors
Yitian Zhou, Chaoning Zhang, Jiaquan Zhang +6
Long-context large language models remain computationally expensive to run and often fail to reliably process very long inputs, which makes context compression an important compone…
Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs
Xudong Wang, Chaoning Zhang, Qigan Sun +7
Retrieval-Augmented Generation (RAG) mitigates hallucination in large language models (LLMs) by incorporating external knowledge during generation. However, the effectiveness of RA…
Relaxing Anchor-Frame Dominance for Mitigating Hallucinations in Video Large Language Models
Zijian Liu, Sihan Cao, Pengcheng Zheng +5
Recent Video Large Language Models (Video-LLMs) have demonstrated strong capability in video understanding, yet they still suffer from hallucinations. Existing mitigation methods t…
Immunizing 3D Gaussian Generative Models Against Unauthorized Fine-Tuning via Attribute-Space Traps
Jianwei Zhang, Sihan Cao, Chaoning Zhang +7
Recent large-scale generative models enable high-quality 3D synthesis. However, the public accessibility of pre-trained weights introduces a critical vulnerability. Adversaries can…
RCP: Representation Consistency Pruner for Mitigating Distribution Shift in Large Vision-Language Models
Jianwei Zhang, Chaoning Zhang, Sihan Cao +7
Large Vision-Language Models (LVLMs) suffer from prohibitive inference costs due to the massive number of visual tokens processed by the language decoder. Existing pruning methods…
Topology-Aware Layer Pruning for Large Vision-Language Models
Pengcheng Zheng, Chaoning Zhang, Ya Wen +10
Large Language Models (LLMs) have demonstrated strong capabilities in natural language understanding and reasoning, while recent extensions that incorporate visual inputs enable th…
TDA-RC: Task-Driven Alignment for Knowledge-Based Reasoning Chains in Large Language Models
Jiaquan Zhang, Qigan Sun, Chaoning Zhang +11
Enhancing the reasoning capability of large language models (LLMs) remains a core challenge in natural language processing. The Chain-of-Thought (CoT) paradigm dominates practical…
Language-Guided Token Compression with Reinforcement Learning in Large Vision-Language Models
Sihan Cao, Jianwei Zhang, Pengcheng Zheng +7
Large Vision-Language Models (LVLMs) incur substantial inference costs due to the processing of a vast number of visual tokens. Existing methods typically struggle to model progres…
Optimizing Soft Prompt Tuning via Structural Evolution
Zhenzhen Huang, Chaoning Zhang, Haoyu Bian +8
Soft prompt tuning leverages continuous embeddings to capture task-specific information in large pre-trained language models (LLMs), achieving competitive performance in few-shot s…
Geometric Neural Operators via Lie Group-Constrained Latent Dynamics
Jiaquan Zhang, Fachrina Dewi Puspitasari, Songbo Zhang +7
Neural operators offer an effective framework for learning solutions of partial differential equations for many physical systems in a resolution-invariant and data-driven manner. E…
Rethinking Input Domains in Physics-Informed Neural Networks via Geometric Compactification Mappings
Zhenzhen Huang, Haoyu Bian, Jiaquan Zhang +6
Several complex physical systems are governed by multi-scale partial differential equations (PDEs) that exhibit both smooth low-frequency components and localized high-frequency st…
LLaVA-FA: Learning Fourier Approximation for Compressing Large Multimodal Models
Pengcheng Zheng, Chaoning Zhang, Jiarong Mo +8
Large multimodal models (LMMs) have achieved impressive performance on various vision-language tasks, but their substantial computational and memory costs hinder their practical de…
Fast SAM2 with Text-Driven Token Pruning
Avilasha Mandal, Chaoning Zhang, Fachrina Dewi Puspitasari +6
Segment Anything Model 2 (SAM2), a vision foundation model has significantly advanced in prompt-driven video object segmentation, yet their practical deployment remains limited by…
GraphMind: Theorem Selection and Conclusion Generation Framework with Dynamic GNN for LLM Reasoning
Yutong Li, Yitian Zhou, Xudong Wang +2
Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, including multi-step reasoning such as mathematical proving…
Sora as a World Model? A Complete Survey on Text-to-Video Generation
Fachrina Dewi Puspitasari, Chaoning Zhang, Joseph Cho +13
The evolution of video generation from text, from animating MNIST to simulating the world with Sora, has progressed at a breakneck speed. Here, we systematically discuss how far te…