382 citations · 399 across the 12 of their papers we have counts for
6 papers · 1 filter
ClawMachine: Learning to Fetch Visual Tokens for Referential Comprehension
Tianren Ma, Lingxi Xie, Yunjie Tian +2
Aligning vision and language concepts at a finer level remains an essential topic of multimodal large language models (MLLMs), particularly for tasks such as referring and groundin…
Artemis: Towards Referential Understanding in Complex Videos
Jihao Qiu, Yuan Zhang, Xi Tang +6
Videos carry rich visual information including object description, action, interaction, etc., but the existing multimodal large language models (MLLMs) fell short in referential un…
Building Vision Models upon Heat Conduction
Zhaozhi Wang, Yue Liu, Yunjie Tian +3
Visual representation models leveraging attention mechanisms are challenged by significant computational overhead, particularly when pursuing large receptive fields. In this study,…
Virtual Classification: Modulating Domain-Specific Knowledge for Multidomain Crowd Counting
Mingyue Guo, Binghui Chen, Zhaoyi Yan +2
Multidomain crowd counting aims to learn a general model for multiple diverse datasets. However, deep networks prefer modeling distributions of the dominant domains instead of all…
ChatterBox: Multi-round Multimodal Referring and Grounding
Yunjie Tian, Tianren Ma, Lingxi Xie +6
In this study, we establish a baseline for a new task named multimodal multi-round referring and grounding (MRG), opening up a promising direction for instance-level multimodal dia…
VMamba: Visual State Space Model
Yue Liu, Yunjie Tian, Yuzhong Zhao +6
Designing computationally efficient network architectures remains an ongoing necessity in computer vision. In this paper, we adapt Mamba, a state-space language model, into VMamba,…