5 papers
VGent: Visual Grounding via Modular Design for Disentangling Reasoning and Prediction
Weitai Kang, Jason Kuen, Mengwei Ren +3
Current visual grounding models are either based on a Multimodal Large Language Model (MLLM) that performs auto-regressive decoding, which is slow and risks hallucinations, or on r…
ExpVG: Investigating the Design Space of Visual Grounding in Multimodal Large Language Model
Weitai Kang, Weiming Zhuang, Zhizhong Li +2
Fine-grained multimodal capability in Multimodal Large Language Models (MLLMs) has emerged as a critical research direction, particularly for tackling the visual grounding (VG) pro…
GuirlVG: Incentivize GUI Visual Grounding via Empirical Exploration on Reinforcement Learning
Weitai Kang, Bin Lei, Gaowen Liu +2
Graphical user interface visual grounding (GUI-VG), a core capability for GUI agents, has primarily relied on supervised fine-tuning (SFT) of multimodal large language models (MLLM…
3DResT: A Strong Baseline for Semi-Supervised 3D Referring Expression Segmentation
Wenxin Chen, Mengxue Qu, Weitai Kang +3
3D Referring Expression Segmentation (3D-RES) typically requires extensive instance-level annotations, which are time-consuming and costly. Semi-supervised learning (SSL) mitigates…
Interpolating Video-LLMs: Toward Longer-sequence LMMs in a Training-free Manner
Yuzhang Shang, Bingxin Xu, Weitai Kang +7
Advancements in Large Language Models (LLMs) inspire various strategies for integrating video modalities. A key approach is Video-LLMs, which incorporate an optimizable interface l…