4 citations · 7 across the 10 of their papers we have counts for
10 papers
ST-WAM: Semantic-Temporal World Action Model for Robust Manipulation under Visual Distribution Shifts
Mingxin Wang, Bin Hu, Bin Qian +12
World Action Models (WAMs) have emerged as a promising paradigm by jointly modeling robot actions and future visual dynamics. However, their reliance on pixel-generative future sup…
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…
PRPO: Perception-Reinforced Policy Optimization via Token-Level Dynamic Advantage Reshaping
Qiming Li, Tianlun Li, Xiaolong Cheng +5
Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective paradigm for improving the reasoning capability of Large Vision-Language Models (LVLMs). However, exis…
ABot-OCR Technical Report
Kaitao Jiang, Ruiyan Gong, Xiaolong Cheng +3
We introduce ABot-OCR, an end-to-end vision-language model that transcribes a page image directly into clean Markdown in a single forward pass. By doing so, our approach completely…
A Survey on Vision Autoregressive Model
Kai Jiang, Jiaxing Huang
Autoregressive models have demonstrated great performance in natural language processing (NLP) with impressive scalability, adaptability and generalizability. Inspired by their not…
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…
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…
DA-BEV: Unsupervised Domain Adaptation for Bird's Eye View Perception
Kai Jiang, Jiaxing Huang, Weiying Xie +3
Camera-only Bird's Eye View (BEV) has demonstrated great potential in environment perception in a 3D space. However, most existing studies were conducted under a supervised setup w…
Domain Adaptation for Large-Vocabulary Object Detectors
Kai Jiang, Jiaxing Huang, Weiying Xie +4
Large-vocabulary object detectors (LVDs) aim to detect objects of many categories, which learn super objectness features and can locate objects accurately while applied to various…
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…