2 citations · 2 across the 10 of their papers we have counts for
12 papers
IVT-Guard: All-in-One Reasoning Model for AI-Generated Content Detection
Hongwei Niu, Yunpeng Luo, Hanjun Li +6
The rapid proliferation of highly realistic AI-Generated Content (AIGC) necessitates robust and interpretable detection mechanisms. However, existing detectors are predominantly co…
D2Pruner: Debiased Importance and Structural Diversity for MLLM Token Pruning
Evelyn Zhang, Fufu Yu, Aoqi Wu +5
Processing long visual token sequences poses a significant computational burden on Multimodal Large Language Models (MLLMs). While token pruning offers a path to acceleration, we f…
Towards Rationale-Answer Alignment of LVLMs via Self-Rationale Calibration
Yuanchen Wu, Ke Yan, Shouhong Ding +2
Large Vision-Language Models (LVLMs) have manifested strong visual question answering capability. However, they still struggle with aligning the rationale and the generated answer,…
VISA: Group-wise Visual Token Selection and Aggregation via Graph Summarization for Efficient MLLMs Inference
Pengfei Jiang, Hanjun Li, Linglan Zhao +4
In this study, we introduce a novel method called group-wise \textbf{VI}sual token \textbf{S}election and \textbf{A}ggregation (VISA) to address the issue of inefficient inference…
AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Models
Ziyin Zhou, Yunpeng Luo, Yuanchen Wu +7
The rapid development of AI-generated content (AIGC) technology has led to the misuse of highly realistic AI-generated images (AIGI) in spreading misinformation, posing a threat to…
Exploring the Collaborative Advantage of Low-level Information on Generalizable AI-Generated Image Detection
Ziyin Zhou, Ke Sun, Zhongxi Chen +5
Existing state-of-the-art AI-Generated image detection methods mostly consider extracting low-level information from RGB images to help improve the generalization of AI-Generated i…
SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained Models
Linglan Zhao, Xuerui Zhang, Ke Yan +2
Continual learning aims to incrementally acquire new concepts in data streams while resisting forgetting previous knowledge. With the rise of powerful pre-trained models (PTMs), th…
Fuse Before Transfer: Knowledge Fusion for Heterogeneous Distillation
Guopeng Li, Qiang Wang, Ke Yan +3
Most knowledge distillation (KD) methodologies predominantly focus on teacher-student pairs with similar architectures, such as both being convolutional neural networks (CNNs). How…
Dual Relation Mining Network for Zero-Shot Learning
Jinwei Han, Yingguo Gao, Zhiwen Lin +4
Zero-shot learning (ZSL) aims to recognize novel classes through transferring shared semantic knowledge (e.g., attributes) from seen classes to unseen classes. Recently, attention-…
Anchor-based Robust Finetuning of Vision-Language Models
Jinwei Han, Zhiwen Lin, Zhongyisun Sun +5
We aim at finetuning a vision-language model without hurting its out-of-distribution (OOD) generalization. We address two types of OOD generalization, i.e., i) domain shift such as…
Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models
Didi Zhu, Zhongyi Sun, Zexi Li +5
Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large language models (MLLMs), where improving performance on unseen tasks often leads to a sig…
Combining Past, Present and Future: A Self-Supervised Approach for Class Incremental Learning
Xiaoshuang Chen, Zhongyi Sun, Ke Yan +2
Class Incremental Learning (CIL) aims to handle the scenario where data of novel classes occur continuously and sequentially. The model should recognize the sequential novel classe…