4 citations · 8 across the 6 of their papers we have counts for
6 papers
Spatial Preference Rewarding for MLLMs Spatial Understanding
Han Qiu, Peng Gao, Lewei Lu +3
Multimodal large language models~(MLLMs) have demonstrated promising spatial understanding capabilities, such as referencing and grounding object descriptions. Despite their succes…
Multimodal 3D Reasoning Segmentation with Complex Scenes
Xueying Jiang, Lewei Lu, Ling Shao +1
The recent development in multimodal learning has greatly advanced the research in 3D scene understanding in various real-world tasks such as embodied AI. However, most existing st…
Masked AutoDecoder is Effective Multi-Task Vision Generalist
Han Qiu, Jiaxing Huang, Peng Gao +3
Inspired by the success of general-purpose models in NLP, recent studies attempt to unify different vision tasks in the same sequence format and employ autoregressive Transformers…
Weakly Supervised Monocular 3D Detection with a Single-View Image
Xueying Jiang, Sheng Jin, Lewei Lu +2
Monocular 3D detection (M3D) aims for precise 3D object localization from a single-view image which usually involves labor-intensive annotation of 3D detection boxes. Weakly superv…
LLMs Meet VLMs: Boost Open Vocabulary Object Detection with Fine-grained Descriptors
Sheng Jin, Xueying Jiang, Jiaxing Huang +2
Inspired by the outstanding zero-shot capability of vision language models (VLMs) in image classification tasks, open-vocabulary object detection has attracted increasing interest…
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…