4 citations · 9 across the 13 of their papers we have counts for
10 papers · 1 filter
MobileSAM2: Lightweight Segment Anything for Spatial Intelligence
Kai Jiang, Jiaxing Huang, Jingyi Zhang +5
The recent large video foundation model, SAM2, enables segment anything in both images and videos, serving as a powerful base model for various applications. However, many of such…
MM-DeepResearch: A Simple and Effective Multimodal Agentic Search Baseline
Huanjin Yao, Qixiang Yin, Min Yang +5
We aim to develop a multimodal research agent capable of explicit reasoning and planning, multi-tool invocation, and cross-modal information synthesis, enabling it to conduct deep…
A Survey on Agentic Multimodal Large Language Models
Huanjin Yao, Ruifei Zhang, Jiaxing Huang +8
With the recent emergence of revolutionary autonomous agentic systems, research community is witnessing a significant shift from traditional static, passive, and domain-specific AI…
R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPO
Huanjin Yao, Qixiang Yin, Jingyi Zhang +8
In this work, we aim to incentivize the reasoning ability of Multimodal Large Language Models (MLLMs) via reinforcement learning (RL) and develop an effective approach that mitigat…
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
Huanjin Yao, Jiaxing Huang, Wenhao Wu +8
In this work, we aim to develop an MLLM that understands and solves questions by learning to create each intermediate step of the reasoning involved till the final answer. To this…
Open-Vocabulary Object Detection via Language Hierarchy
Jiaxing Huang, Jingyi Zhang, Kai Jiang +1
Recent studies on generalizable object detection have attracted increasing attention with additional weak supervision from large-scale datasets with image-level labels. However, we…
Historical Test-time Prompt Tuning for Vision Foundation Models
Jingyi Zhang, Jiaxing Huang, Xiaoqin Zhang +2
Test-time prompt tuning, which learns prompts online with unlabelled test samples during the inference stage, has demonstrated great potential by learning effective prompts on-the-…
A Survey on Evaluation of Multimodal Large Language Models
Jiaxing Huang, Jingyi Zhang
Multimodal Large Language Models (MLLMs) mimic human perception and reasoning system by integrating powerful Large Language Models (LLMs) with various modality encoders (e.g., visi…
Learning to Prompt Segment Anything Models
Jiaxing Huang, Kai Jiang, Jingyi Zhang +4
Segment Anything Models (SAMs) like SEEM and SAM have demonstrated great potential in learning to segment anything. The core design of SAMs lies with Promptable Segmentation, which…
Visual Instruction Tuning towards General-Purpose Multimodal Model: A Survey
Jiaxing Huang, Jingyi Zhang, Kai Jiang +2
Traditional computer vision generally solves each single task independently by a dedicated model with the task instruction implicitly designed in the model architecture, arising tw…