5 citations · 19 across the 13 of their papers we have counts for
16 papers
Understanding and Mitigating Token-Pruning-Induced Vulnerabilities in VLMs
Shuailong Wang, Xinyu Lyu, Shengming Yuan +3
Token-Pruning accelerates Vision-Language Models by removing redundant visual tokens, yet its safety implications remain underexplored. In this work, we present the first comprehen…
FlexAC: Towards Flexible Control of Associative Reasoning in Multimodal Large Language Models
Shengming Yuan, Xinyu Lyu, Shuailong Wang +3
Multimodal large language models (MLLMs) face an inherent trade-off between faithfulness and creativity, as different tasks require varying degrees of associative reasoning. Howeve…
SafePTR: Token-Level Jailbreak Defense in Multimodal LLMs via Prune-then-Restore Mechanism
Beitao Chen, Xinyu Lyu, Lianli Gao +2
By incorporating visual inputs, Multimodal Large Language Models (MLLMs) extend LLMs to support visual reasoning. However, this integration also introduces new vulnerabilities, mak…
Attention Hijackers: Detect and Disentangle Attention Hijacking in LVLMs for Hallucination Mitigation
Beitao Chen, Xinyu Lyu, Lianli Gao +2
Despite their success, Large Vision-Language Models (LVLMs) remain vulnerable to hallucinations. While existing studies attribute the cause of hallucinations to insufficient visual…
Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization
Xinyu Lyu, Beitao Chen, Lianli Gao +2
Although Large Visual Language Models (LVLMs) have demonstrated exceptional abilities in understanding multimodal data, they invariably suffer from hallucinations, leading to a dis…
Text-Video Retrieval with Global-Local Semantic Consistent Learning
Haonan Zhang, Pengpeng Zeng, Lianli Gao +4
Adapting large-scale image-text pre-training models, e.g., CLIP, to the video domain represents the current state-of-the-art for text-video retrieval. The primary approaches involv…
On the robustness of double-word addition algorithms
Yuanyuan Yang, XinYu Lyu, Sida He +3
We demonstrate that, even when there are moderate overlaps in the inputs of sloppy or accurate double-word addition algorithms in the QD library, these algorithms still guarantee e…
ALF: Adaptive Label Finetuning for Scene Graph Generation
Qishen Chen, Jianzhi Liu, Xinyu Lyu +3
Scene Graph Generation (SGG) endeavors to predict the relationships between subjects and objects in a given image. Nevertheless, the long-tail distribution of relations often leads…
Local-Global Information Interaction Debiasing for Dynamic Scene Graph Generation
Xinyu Lyu, Jingwei Liu, Yuyu Guo +1
The task of dynamic scene graph generation (DynSGG) aims to generate scene graphs for given videos, which involves modeling the spatial-temporal information in the video. However,…
Informative Scene Graph Generation via Debiasing
Lianli Gao, Xinyu Lyu, Yuyu Guo +5
Scene graph generation aims to detect visual relationship triplets, (subject, predicate, object). Due to biases in data, current models tend to predict common predicates, e.g. "on"…
Generalized Unbiased Scene Graph Generation
Xinyu Lyu, Lianli Gao, Junlin Xie +4
Existing Unbiased Scene Graph Generation (USGG) methods only focus on addressing the predicate-level imbalance that high-frequency classes dominate predictions of rare ones, while…
Prototype-based Embedding Network for Scene Graph Generation
Chaofan Zheng, Xinyu Lyu, Lianli Gao +2
Current Scene Graph Generation (SGG) methods explore contextual information to predict relationships among entity pairs. However, due to the diverse visual appearance of numerous p…
Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation
Chaofan Zheng, Lianli Gao, Xinyu Lyu +3
The current studies of Scene Graph Generation (SGG) focus on solving the long-tailed problem for generating unbiased scene graphs. However, most de-biasing methods overemphasize th…
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation
Xinyu Lyu, Lianli Gao, Pengpeng Zeng +2
The performance of current Scene Graph Generation (SGG) models is severely hampered by hard-to-distinguish predicates, e.g., woman-on/standing on/walking on-beach. As general SGG m…
Learning To Generate Scene Graph from Head to Tail
Chaofan Zheng, Xinyu Lyu, Yuyu Guo +3
Scene Graph Generation (SGG) represents objects and their interactions with a graph structure. Recently, many works are devoted to solving the imbalanced problem in SGG. However, u…
Fine-Grained Predicates Learning for Scene Graph Generation
Xinyu Lyu, Lianli Gao, Yuyu Guo +4
The performance of current Scene Graph Generation models is severely hampered by some hard-to-distinguish predicates, e.g., "woman-on/standing on/walking on-beach" or "woman-near/l…