activity
20172026
most citedDAMO-YOLO : A Report on Real-Time Object Detection Design

122 citations · 145 across the 11 of their papers we have counts for

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8 papers · 1 filter

cs.CV2026

ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding

Hangjie Yuan, Yichen Qian, Zhiwei Tang +21

Multimodal large language models (MLLMs) hold immense potential to revolutionize clinical practice, yet deploying them in the medical domain is fundamentally a vision-centric chall…

cs.CV2025

CoT-lized Diffusion: Let's Reinforce T2I Generation Step-by-step

Zheyuan Liu, Munan Ning, Qihui Zhang +8

Current text-to-image (T2I) generation models struggle to align spatial composition with the input text, especially in complex scenes. Even layout-based approaches yield suboptimal…

cs.CV2023★ 4 cited

Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks

Weihua Chen, Xianzhe Xu, Jian Jia +5

Human-centric visual tasks have attracted increasing research attention due to their widespread applications. In this paper, we aim to learn a general human representation from mas…

cs.CV2022★ 122 cited

DAMO-YOLO : A Report on Real-Time Object Detection Design

Xianzhe Xu, Yiqi Jiang, Weihua Chen +3

In this report, we present a fast and accurate object detection method dubbed DAMO-YOLO, which achieves higher performance than the state-of-the-art YOLO series. DAMO-YOLO is exten…

cs.CV2021★ 5 cited

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

Zhaoyuan Yin, Pichao Wang, Fan Wang +4

Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations. Most existing methods are bottom-up ap…

cs.CV2021★ 1 cited

2nd Place Solution to Google Landmark Retrieval 2021

Zhang Yuqi, Xu Xianzhe, Chen Weihua +4

This paper presents the 2nd place solution to the Google Landmark Retrieval 2021 Competition on Kaggle. The solution is based on a baseline with training tricks from person re-iden…

cs.CV2021★ 1 cited

An Empirical Study of Vehicle Re-Identification on the AI City Challenge

Hao Luo, Weihua Chen, Xianzhe Xu +7

This paper introduces our solution for the Track2 in AI City Challenge 2021 (AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synt…

cs.CV2021★ 2 cited

City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones

Chong Liu, Yuqi Zhang, Hao Luo +6

Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many advanced inferences and predictions. This paper describes our solution to the Track 3…