12 citations · 12 across the 7 of their papers we have counts for
6 papers · 1 filter
Improving Multi-modal Large Language Model through Boosting Vision Capabilities
Yanpeng Sun, Huaxin Zhang, Qiang Chen +5
We focus on improving the visual understanding capability for boosting the vision-language models. We propose \textbf{Arcana}, a multiModal language model, which introduces two cru…
FullAnno: A Data Engine for Enhancing Image Comprehension of MLLMs
Jing Hao, Yuxiang Zhao, Song Chen +6
Multimodal Large Language Models (MLLMs) have shown promise in a broad range of vision-language tasks with their strong reasoning and generalization capabilities. However, they hea…
OVLW-DETR: Open-Vocabulary Light-Weighted Detection Transformer
Yu Wang, Xiangbo Su, Qiang Chen +6
Open-vocabulary object detection focusing on detecting novel categories guided by natural language. In this report, we propose Open-Vocabulary Light-Weighted Detection Transformer…
LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection
Qiang Chen, Xiangbo Su, Xinyu Zhang +12
In this paper, we present a light-weight detection transformer, LW-DETR, which outperforms YOLOs for real-time object detection. The architecture is a simple stack of a ViT encoder…
VRP-SAM: SAM with Visual Reference Prompt
Yanpeng Sun, Jiahui Chen, Shan Zhang +7
In this paper, we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmenta…
MS-DETR: Efficient DETR Training with Mixed Supervision
Chuyang Zhao, Yifan Sun, Wenhao Wang +4
DETR accomplishes end-to-end object detection through iteratively generating multiple object candidates based on image features and promoting one candidate for each ground-truth ob…