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20162026
most citedYour Negative May not Be True Negative: Boosting Image-Text Matching with False Negative Elimination

41 citations · 134 across the 27 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.MM2024★ 4 cited

MM-Forecast: A Multimodal Approach to Temporal Event Forecasting with Large Language Models

Haoxuan Li, Zhengmao Yang, Yunshan Ma +3

We study an emerging and intriguing problem of multimodal temporal event forecasting with large language models. Compared to using text or graph modalities, the investigation of ut…

cs.CL2024★ 33 cited

GalleryGPT: Analyzing Paintings with Large Multimodal Models

Yi Bin, Wenhao Shi, Yujuan Ding +5

Artwork analysis is important and fundamental skill for art appreciation, which could enrich personal aesthetic sensibility and facilitate the critical thinking ability. Understand…

cs.MM2024★ 2 cited

Leveraging Weak Cross-Modal Guidance for Coherence Modelling via Iterative Learning

Yi Bin, Junrong Liao, Yujuan Ding +4

Cross-modal coherence modeling is essential for intelligent systems to help them organize and structure information, thereby understanding and creating content of the physical worl…

cs.CV2024

Exploring Deeper! Segment Anything Model with Depth Perception for Camouflaged Object Detection

Zhenni Yu, Xiaoqin Zhang, Li Zhao +2

This paper introduces a new Segment Anything Model with Depth Perception (DSAM) for Camouflaged Object Detection (COD). DSAM exploits the zero-shot capability of SAM to realize pre…

cs.CV2024

MAMA: Meta-optimized Angular Margin Contrastive Framework for Video-Language Representation Learning

Thong Nguyen, Yi Bin, Xiaobao Wu +6

Data quality stands at the forefront of deciding the effectiveness of video-language representation learning. However, video-text pairs in previous data typically do not align perf…

cs.CL2024

Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models

Wenhao Shi, Zhiqiang Hu, Yi Bin +5

Large language models (LLMs) have demonstrated impressive reasoning capabilities, particularly in textual mathematical problem-solving. However, existing open-source image instruct…

cs.CL2024★ 1 cited

Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives

Thong Nguyen, Yi Bin, Junbin Xiao +6

Humans use multiple senses to comprehend the environment. Vision and language are two of the most vital senses since they allow us to easily communicate our thoughts and perceive t…