activity
20212025
most citedMulti-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos

46 citations · 50 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2025

Robust Multimodal Large Language Models Against Modality Conflict

Zongmeng Zhang, Wengang Zhou, Jie Zhao +1

Despite the impressive capabilities of multimodal large language models (MLLMs) in vision-language tasks, they are prone to hallucinations in real-world scenarios. This paper inves…

cs.CV2025

Mitigating Hallucination in VideoLLMs via Temporal-Aware Activation Engineering

Jianfeng Cai, Wengang Zhou, Zongmeng Zhang +3

Multimodal large language models (MLLMs) have achieved remarkable progress in video understanding.However, hallucination, where the model generates plausible yet incorrect outputs,…

cs.IR2024

BoolQuestions: Does Dense Retrieval Understand Boolean Logic in Language?

Zongmeng Zhang, Jinhua Zhu, Wengang Zhou +3

Dense retrieval, which aims to encode the semantic information of arbitrary text into dense vector representations or embeddings, has emerged as an effective and efficient paradigm…

cs.CL2024★ 1 cited

Trustworthy Alignment of Retrieval-Augmented Large Language Models via Reinforcement Learning

Zongmeng Zhang, Yufeng Shi, Jinhua Zhu +4

Trustworthiness is an essential prerequisite for the real-world application of large language models. In this paper, we focus on the trustworthiness of language models with respect…

cs.IR2023★ 3 cited

Hybrid and Collaborative Passage Reranking

Zongmeng Zhang, Wengang Zhou, Jiaxin Shi +1

In passage retrieval system, the initial passage retrieval results may be unsatisfactory, which can be refined by a reranking scheme. Existing solutions to passage reranking focus…

cs.CV2021★ 46 cited

Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos

Zongmeng Zhang, Xianjing Han, Xuemeng Song +2

This paper focuses on tackling the problem of temporal language localization in videos, which aims to identify the start and end points of a moment described by a natural language…