Author name cluster

Qi Ming

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

AAAI Conference 2026 Conference Paper

Hilbert Curve-Encoded Rotation-Equivariant Oriented Object Detector with Locality-Preserving Spatial Mapping

Qi Ming
Liuqian Wang
Juan Fang
Xudong Zhao
Yucheng Xu
Ziyi Teng
Yue Zhou
Xiaoxi Hu

Arbitrary-Oriented Object Detection (AOOD) has found broad applications in embodied intelligence, autonomous driving, and satellite remote sensing. However, current AOOD frameworks face challenges in ineffective feature extraction and orientation regression inaccuracy. Inspired by Hilbert curve's intrinsic locality-preserving property, we propose a flexible Hilbert curve-Encoded Rotation-Equivariant Oriented Object Detector (HERO-Det). Our innovations include: (i) a novel Hilbert curve traversal convolution paradigm with a dimensionality reduction scheme, which employs locality-preserving spatial filling curves for feature transformation, (ii) a Hilbert pyramid transformer enabling hierarchical construction of multi-scale feature sequences through space-folding operations, as well as (iii) an orientation-adaptive prediction head that decouples rotation-equivariant regression features from invariant classification cues to resolve orientation regression dilemmas in two-stage detectors. Extensive experiments show HERO-Det achieves state-of-the-art performance on AOOD benchmarks, with mAP of 79.56%, 90.64%, 90.10%, and 80.47% on DOTA, HRSC2016, SSDD, and HRSID, respectively. Performance gains in cross-task validation further demonstrate the versatility of our method to diverse vision tasks, such as medical image segmentation and 3D object detection.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Learning Better UAV-Based Cross-View Object Geo-Localization from Multi-Modal Prompts: MoP-UAV Benchmark and MoPT Framework

Xiaohan Zhang
Zhangkai Shen
Si-Yuan Cao
Xiaokai Bai
Yiming Li
Zheheng Han
Zhe Wu
Qi Ming

We present MoP-UAV, a new benchmark for UAV-based cross-view object geo-localization guided by multi-modal prompts. MoP-UAV supports fine-grained object-level cross-view localization under diverse prompt modalities, including natural language, bounding boxes, and click points. It offers potential for incorporating large foundation models like large language models (LLMs) and promotes the building of more flexible and intelligent UAV agents. Based on the benchmark, we propose MoPT, a multi-modal-prompt-guided tansformer that embeds prompts as token sequences and extract object location from UAV and satellite features via cross-attention. To enhance semantic consistency and performance, we further adopt a cross-view contrastive loss and propose a RefCOCOg-based pre-training strategy. Extensive experiments show that MoPT achieves robust localization under arbitrary prompt combinations. Notably, multi-modal-prompt training significantly boosts unimodal-prompt inference performance, highlighting the generalization benefits of multi-modal learning. MoPT trained with multi-modal prompts outperforms prior unimodal prompt works under the same setting.

PDF Details DOI

AAAI Conference 2021 Conference Paper

Dynamic Anchor Learning for Arbitrary-Oriented Object Detection

Qi Ming
Zhiqiang Zhou
Lingjuan Miao
Hongwei Zhang
Linhao Li

Arbitrary-oriented objects widely appear in natural scenes, aerial photographs, remote sensing images, etc. , and thus arbitrary-oriented object detection has received considerable attention. Many current rotation detectors use plenty of anchors with different orientations to achieve spatial alignment with ground truth boxes. Intersection-over-Union (IoU) is then applied to sample the positive and negative candidates for training. However, we observe that the selected positive anchors cannot always ensure accurate detections after regression, while some negative samples can achieve accurate localization. It indicates that the quality assessment of anchors through IoU is not appropriate, and this further leads to inconsistency between classification confidence and localization accuracy. In this paper, we propose a dynamic anchor learning (DAL) method, which utilizes the newly defined matching degree to comprehensively evaluate the localization potential of the anchors and carries out a more efficient label assignment process. In this way, the detector can dynamically select high-quality anchors to achieve accurate object detection, and the divergence between classification and regression will be alleviated. With the newly introduced DAL, we can achieve superior detection performance for arbitrary-oriented objects with only a few horizontal preset anchors. Experimental results on three remote sensing datasets HRSC2016, DOTA, UCAS-AOD as well as a scene text dataset ICDAR 2015 show that our method achieves substantial improvement compared with the baseline model. Besides, our approach is also universal for object detection using horizontal bound box. The code and models are available at https: //github. com/ming71/DAL.

PDF Details

NeurIPS Conference 2021 Conference Paper

Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence

Xue Yang
Xiaojiang Yang
Jirui Yang
Qi Ming
Wentao Wang
Qi Tian
Junchi Yan

Existing rotated object detectors are mostly inherited from the horizontal detection paradigm, as the latter has evolved into a well-developed area. However, these detectors are difficult to perform prominently in high-precision detection due to the limitation of current regression loss design, especially for objects with large aspect ratios. Taking the perspective that horizontal detection is a special case for rotated object detection, in this paper, we are motivated to change the design of rotation regression loss from induction paradigm to deduction methodology, in terms of the relation between rotation and horizontal detection. We show that one essential challenge is how to modulate the coupled parameters in the rotation regression loss, as such the estimated parameters can influence to each other during the dynamic joint optimization, in an adaptive and synergetic way. Specifically, we first convert the rotated bounding box into a 2-D Gaussian distribution, and then calculate the Kullback-Leibler Divergence (KLD) between the Gaussian distributions as the regression loss. By analyzing the gradient of each parameter, we show that KLD (and its derivatives) can dynamically adjust the parameter gradients according to the characteristics of the object. For instance, it will adjust the importance (gradient weight) of the angle parameter according to the aspect ratio. This mechanism can be vital for high-precision detection as a slight angle error would cause a serious accuracy drop for large aspect ratios objects. More importantly, we have proved that KLD is scale invariant. We further show that the KLD loss can be degenerated into the popular Ln-norm loss for horizontal detection. Experimental results on seven datasets using different detectors show its consistent superiority, and codes are available at https: //github. com/yangxue0827/RotationDetection.

PDF Details

ICML Conference 2021 Conference Paper

Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss

Xue Yang 0005
Junchi Yan
Qi Ming
Wentao Wang 0009
Xiaopeng Zhang 0008
Qi Tian 0001

Boundary discontinuity and its inconsistency to the final detection metric have been the bottleneck for rotating detection regression loss design. In this paper, we propose a novel regression loss based on Gaussian Wasserstein distance as a fundamental approach to solve the problem. Specifically, the rotated bounding box is converted to a 2-D Gaussian distribution, which enables to approximate the indifferentiable rotational IoU induced loss by the Gaussian Wasserstein distance (GWD) which can be learned efficiently by gradient back-propagation. GWD can still be informative for learning even there is no overlapping between two rotating bounding boxes which is often the case for small object detection. Thanks to its three unique properties, GWD can also elegantly solve the boundary discontinuity and square-like problem regardless how the bounding box is defined. Experiments on five datasets using different detectors show the effectiveness of our approach, and codes are available at https: //github. com/yangxue0827/RotationDetection.

Details