most citedDINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

777 citations · 1.1k across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2023★ 6 cited

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…

cs.CV2023★ 5 cited

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…

cs.CV2023★ 259 cited

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…

cs.CV2022★ 3 cited

DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding

Shilong Liu, Yaoyuan Liang, Feng Li +5

In this paper, we study the problem of visual grounding by considering both phrase extraction and grounding (PEG). In contrast to the previous phrase-known-at-test setting, PEG req…

cs.CV2022★ 777 cited

DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

Hao Zhang, Feng Li, Shilong Liu +5

We present DINO (\textbf{D}ETR with \textbf{I}mproved de\textbf{N}oising anch\textbf{O}r boxes), a state-of-the-art end-to-end object detector. % in this paper. DINO improves over…