1 citations · 1 across the 17 of their papers we have counts for
17 papers
MulVec: Fine-Grained Role-Aware Matching for Training-Free Zero-Shot Composed Image Retrieval
Zihao Zhang, Dayan Wu, Xinze Liu +6
Training-free zero-shot composed image retrieval finds a target image in a gallery from a reference image and a text edit without learning from task-specific image triplets. Existi…
AdaptiveEmbed: Sample-Adaptive Multi-Vector Representation for Multimodal Retrieval
Xinze Liu, Lei Yang, Dayan Wu +7
Multi-vector representations have emerged as an effective paradigm for multimodal retrieval, representing each sample with multiple complementary embeddings to capture fine-grained…
FOVEA: Focused On-Demand Visual Evidence Adaptation for Cache-Friendly Multimodal Speculative Decoding
Hengjie Zhu, Dayan Wu, Zihao Zhang +6
Multimodal speculative decoding accelerates vision-language models by allowing a lightweight draft model to propose candidate tokens for parallel verification by a larger target mo…
Hallucinations Leave a Grounding Signature:Verifier-Guided Decoding for Selective Object Correction
Lei Yang, Xinze Liu, Dayan Wu +7
Large vision-language models (LVLMs) often hallucinate objects that are absent from an image. Despite recent progress, existing mitigation methods still lack reliable object-level…
Learning to Solve, Forgetting to Retain: Correct-Set Turnover in RLVR
Chuanyu Qin, Chenxu Yang, Qingyi Si +3
Reinforcement learning with verifiable rewards (RLVR) improves the ability of large language model, yet headline accuracy gains often conceal a hidden cost: previously solved probl…
Online Self-Calibration Against Hallucination in Vision-Language Models
Minghui Chen, Chenxu Yang, Hengjie Zhu +3
Large Vision-Language Models (LVLMs) often suffer from hallucinations, generating descriptions that include visual details absent from the input image. Recent preference alignment…
Learning from the Near Future: Temporal Self-Distillation for RLVR
Chuanyu Qin, Chenxu Yang, Qingyi Si +6
Reinforcement learning with verifiable rewards (RLVR) is a core post-training recipe for reasoning models, yet pure on-policy learning can be inefficient when useful trajectories a…
EasyVideoR1: Easier RL for Video Understanding
Chuanyu Qin, Chenxu Yang, Qingyi Si +6
Reinforcement learning from verifiable rewards (RLVR) has demonstrated remarkable effectiveness in improving the reasoning capabilities of large language models. As models evolve i…
Beyond the Covariance Trap: Unlocking Generalization in Same-Subject Knowledge Editing for Large Language Models
Xiyu Liu, Qingyi Si, Zhengxiao Liu +3
While locate-then-edit knowledge editing efficiently updates knowledge encoded within Large Language Models (LLMs), a critical generalization failure mode emerges in the practical…
A Closer Look into LLMs for Table Understanding
Jia Wang, Chuanyu Qin, Mingyu Zheng +3
Despite the success of Large Language Models (LLMs) in table understanding, their internal mechanisms remain unclear. In this paper, we conduct an empirical study on 16 LLMs, cover…
Exposing Cross-Modal Consistency for Fake News Detection in Short-Form Videos
Chong Tian, Yu Wang, Chenxu Yang +5
Short-form video platforms are major channels for news but also fertile ground for multimodal misinformation where each modality appears plausible alone yet cross-modal relationshi…
Breaking the Trade-Off Between Faithfulness and Expressiveness for Large Language Models
Chenxu Yang, Qingyi Si, Zheng Lin
Grounding responses in external knowledge represents an effective strategy for mitigating hallucinations in Large Language Models (LLMs). However, current LLMs struggle to seamless…
Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering
Peize Li, Qingyi Si, Peng Fu +2
Retrieval-based multi-image question answering (QA) task involves retrieving multiple question-related images and synthesizing these images to generate an answer. Conventional "ret…
Towards Flexible Evaluation for Generative Visual Question Answering
Huishan Ji, Qingyi Si, Zheng Lin +1
Throughout rapid development of multimodal large language models, a crucial ingredient is a fair and accurate evaluation of their multimodal comprehension abilities. Although Visua…
Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early Pruning
Naibin Gu, Peng Fu, Xiyu Liu +3
Parameter-efficient fine-tuning (PEFT) has emerged as the predominant technique for fine-tuning in the era of large language models. However, existing PEFT methods still have inade…
Are Large Language Models Table-based Fact-Checkers?
Hanwen Zhang, Qingyi Si, Peng Fu +2
Table-based Fact Verification (TFV) aims to extract the entailment relation between statements and structured tables. Existing TFV methods based on small-scaled models suffer from…
Object Attribute Matters in Visual Question Answering
Peize Li, Qingyi Si, Peng Fu +2
Visual question answering is a multimodal task that requires the joint comprehension of visual and textual information. However, integrating visual and textual semantics solely thr…