21 citations · 31 across the 7 of their papers we have counts for
9 papers
M-VQA: A Benchmark for Multimodal, Multi-Entity, Multi-Hop Visual Question Answering
Jiatong Ma, Longteng Guo, Yuchen Liu +4
We present M-VQA, a novel knowledge-based Visual Question Answering (VQA) benchmark, to enhance the evaluation of multimodal large language models (MLLMs) in fine-grained multi…
Qianfan-VL: Domain-Enhanced Universal Vision-Language Models
Daxiang Dong, Mingming Zheng, Dong Xu +32
We present Qianfan-VL, a series of multimodal large language models ranging from 3B to 70B parameters, achieving state-of-the-art performance through innovative domain enhancement…
LaVi: Efficient Large Vision-Language Models via Internal Feature Modulation
Tongtian Yue, Longteng Guo, Yepeng Tang +4
Despite the impressive advancements of Large Vision-Language Models (LVLMs), existing approaches suffer from a fundamental bottleneck: inefficient visual-language integration. Curr…
Image Difference Grounding with Natural Language
Wenxuan Wang, Zijia Zhao, Yisi Zhang +4
Visual grounding (VG) typically focuses on locating regions of interest within an image using natural language, and most existing VG methods are limited to single-image interpretat…
OneDiff: A Generalist Model for Image Difference Captioning
Erdong Hu, Longteng Guo, Tongtian Yue +3
In computer vision, Image Difference Captioning (IDC) is crucial for accurately describing variations between closely related images. Traditional IDC methods often rely on speciali…
VL-Mamba: Exploring State Space Models for Multimodal Learning
Yanyuan Qiao, Zheng Yu, Longteng Guo +5
Multimodal large language models (MLLMs) have attracted widespread interest and have rich applications. However, the inherent attention mechanism in its Transformer structure requi…