259 citations · 415 across the 14 of their papers we have counts for
14 papers
LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents
Shilong Liu, Hao Cheng, Haotian Liu +10
LLaVA-Plus is a general-purpose multimodal assistant that expands the capabilities of large multimodal models. It maintains a skill repository of pre-trained vision and vision-lang…
Neural Interactive Keypoint Detection
Jie Yang, Ailing Zeng, Feng Li +3
This work proposes an end-to-end neural interactive keypoint detection framework named Click-Pose, which can significantly reduce more than 10 times labeling costs of 2D keypoint a…
detrex: Benchmarking Detection Transformers
Tianhe Ren, Shilong Liu, Feng Li +13
The DEtection TRansformer (DETR) algorithm has received considerable attention in the research community and is gradually emerging as a mainstream approach for object detection and…
A Strong and Reproducible Object Detector with Only Public Datasets
Tianhe Ren, Jianwei Yang, Shilong Liu +6
This work presents Focal-Stable-DINO, a strong and reproducible object detection model which achieves 64.6 AP on COCO val2017 and 64.8 AP on COCO test-dev using only 700M parameter…
Detection Transformer with Stable Matching
Shilong Liu, Tianhe Ren, Jiayu Chen +8
This paper is concerned with the matching stability problem across different decoder layers in DEtection TRansformers (DETR). We point out that the unstable matching in DETR is cau…
A Simple Framework for Open-Vocabulary Segmentation and Detection
Hao Zhang, Feng Li, Xueyan Zou +5
We present OpenSeeD, a simple Open-vocabulary Segmentation and Detection framework that jointly learns from different segmentation and detection datasets. To bridge the gap of voca…
MP-Former: Mask-Piloted Transformer for Image Segmentation
Hao Zhang, Feng Li, Huaizhe Xu +4
We present a mask-piloted Transformer which improves masked-attention in Mask2Former for image segmentation. The improvement is based on our observation that Mask2Former suffers fr…
Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR
Feng Li, Ailing Zeng, Shilong Liu +4
Recent DEtection TRansformer-based (DETR) models have obtained remarkable performance. Its success cannot be achieved without the re-introduction of multi-scale feature fusion in t…
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
Shilong Liu, Zhaoyang Zeng, Tianhe Ren +9
In this paper, we present an open-set object detector, called Grounding DINO, by marrying Transformer-based detector DINO with grounded pre-training, which can detect arbitrary obj…
Introducing Depth into Transformer-based 3D Object Detection
Hao Zhang, Hongyang Li, Ailing Zeng +4
In this paper, we present DAT, a Depth-Aware Transformer framework designed for camera-based 3D detection. Our model is based on observing two major issues in existing methods: lar…
A Unified Mutual Supervision Framework for Referring Expression Segmentation and Generation
Shijia Huang, Feng Li, Hao Zhang +3
Reference Expression Segmentation (RES) and Reference Expression Generation (REG) are mutually inverse tasks that can be naturally jointly trained. Though recent work has explored…
Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation
Feng Li, Hao Zhang, Huaizhe xu +4
In this paper we present Mask DINO, a unified object detection and segmentation framework. Mask DINO extends DINO (DETR with Improved Denoising Anchor Boxes) by adding a mask predi…
Vision-Language Intelligence: Tasks, Representation Learning, and Large Models
Feng Li, Hao Zhang, Yi-Fan Zhang +5
This paper presents a comprehensive survey of vision-language (VL) intelligence from the perspective of time. This survey is inspired by the remarkable progress in both computer vi…
DN-DETR: Accelerate DETR Training by Introducing Query DeNoising
Feng Li, Hao Zhang, Shilong Liu +3
We present in this paper a novel denoising training method to speedup DETR (DEtection TRansformer) training and offer a deepened understanding of the slow convergence issue of DETR…