Detection Transformer

Also known as DETR

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2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Latest papers 53

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Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes
Jun 3, 2026Renjie Liang, Zhengkang Fan, Jinqian Pan +4Medical Image Analysis Detection Transformer
Department of Health Outcomes and Biomedical Informatics, University of Florida, 2004 Mowry Road, Gainesville, 32611, FL, USA · Department of Biostatistics and Health Data Science, Indiana University School of Medicine, 340 West 10th Street, Indianapolis, 46202, IN, USA · Center of Biomedical Informatics, 1101 W 10th St, Indianapolis, Indianapolis, 46202, IN, USA +1
EIVE: End-to-End Instance-Specific Visual Explanations for Detection Transformers
Jun 1, 2026Jianlin Xiang, Yanshan Li, Linhui DaiTransformer Interpretability Feature Attribution
Institute of Intelligent Information Processing, Shenzhen University; the Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University; and the Shenzhen Key Laboratory of Modern Communications and Information Processing, Shenzhen University, Shenzhen, China
GIRL-DETR: Gradient-Isolated Reinforcement Learning for Video Moment Retrieval
May 30, 2026Shihang Zhang, Mingjin Kuai, Ye Wei +2Temporal Video Grounding Reinforcement Learning
Shihang Zhang and Ye Wei are with the College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China · Mingjin Kuai, Zhen Zhang, and Wei Ji are with the School of Intelligence Science and Technology, Nanjing University, Suzhou 215163, China
TinyFormer: Preserving Tiny Objects in YOLO-DETR Hybrid Real-time Detectors
May 24, 2026Jun-Wei Hsieh, Meng-Yu Kao, Ghufron Wahyu Kurniawan +1Hybrid CNN-Transformer Architectures Small Object Detection
College of Artificial Intelligence, National Yang Ming Chiao Tung University, Taiwan · 2Mitsubishi Electric Research Laboratories
MDS-DETR: DETR with Masked Duplicate Suppressor
May 22, 2026Chanho Lee, Seunghee Koh, Yunho Jeon +1Detection Transformer Object Detection
Samsung Research, Seoul 06765, South Korea · Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, South Korea · Department of Artificial Intelligence Software, Hanbat National University, Daejeon 34158, South Korea
Improved Vision-to-Chart Buoy Association with Learned World-to-Image Projection
May 21, 2026Borja Carrillo-PerezDetection Transformer Multimodal Sensor Fusion
Arquimea Research Center
Pattern-Enhanced RT-DETR for Multi-Class Battery Detection
May 13, 2026Xu Zhong, Enyuan HuEfficient Object Detection Detection Transformer
Independent Researcher · Chemistry Division Brookhaven National Laboratory NY, USA
WD-FQDet: Multispectral Detection Transformer via Wavelet Decomposition and Frequency-aware Query Learning
May 13, 2026Chunjin Yang, Xiwei Zhang, Yiming Xiao +1Multispectral Object Detection Frequency-Domain Feature Learning
University of Electronic Science and Technology of China,Chengdu, Sichuan 611731, China
Increasing the Efficiency of DETR for Maritime High-Resolution Images
May 11, 2026Tinsae Yehuala, Hao Cheng, Ville LehtolaDetection Transformer Object Detection
Dept. of Earth Observation Science, ITC Faculty, University of Twente, Enschede, The Netherlands.
Towards Open World Sound Event Detection
May 5, 2026P. H. Hai, L. T. Minh, L. H. SonContinual Learning Detection Transformer
VNU University of Engineering and Technology, Hanoi, 100000, Vietnam · Artificial Intelligence Research Center, VNU Information Technology Institute, Hanoi, 100000, Vietnam
Benchmarking ResNet Backbones in RT-DETR: Impact of Depth and Regularization under environmental conditions
May 1, 2026Pamela Barboza, Víctor Castelli, Belén Pereira +3Neural Network Generalization Detection Transformer
1, Robotics and Artificial Intelligence Laboratory, Technological University of Uruguay, Rivera, Uruguay
ViCrop-Det: Spatial Attention Entropy Guided Cropping for Training-Free Small-Object Detection
Apr 29, 2026Hui Wang, Hongze Li, Wei Chen +1Small Object Detection Detection Transformer
School of Computer Science and Technology, Huazhong University of Science and Technology 2School of Software Engineering, Huazhong University of Science and Technology Wuhan 430074, China
Evaluation of Convolutional and Transformer-Based Detectors for Weed Detection in Tomato Plantations
Apr 29, 2026Alcides Toledo Espinosa, Gerardo Antonio Álvarez Hernández, Ángel Eduardo Zamora-Suárez +2Agricultural Image Analysis Weed Detection
Instituto Politécnico Nacional · CIDETEC-IPN · Mexico City, Mexico +1
Caries DETR: Tooth Structure-aware Prior and Lesion-aware Dynamic Loss Refinement for DETR Based Caries Detection
Apr 26, 2026Xuefen Liu, Xinquan Yang, Mianjie Zheng +5Medical Imaging Medical Image Analysis
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China. · School of Artificial Intelligence, Shenzhen University, Shenzhen, China. · Department of Stomatology, Shenzhen University General Hospital, Shenzhen, China.
VFM $^{4}$ SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection
Apr 23, 2026Yupeng Zhang, Ruize Han, Ningnan Guo +3Detection Transformer Cross-Domain Generalization
College of Intelligence and Computing, Tianjin University, Tianjin 300350, China · Faculty of Computer Science and Artificial Intelligence, Shenzhen University of Advanced Technology, Shenzhen 518107, Guangdong, China
UHR-DETR: Efficient End-to-End Small Object Detection for Ultra-High-Resolution Remote Sensing Imagery
Apr 23, 2026Jingfang Li, Haoran Zhu, Wen Yang +4Remote Sensing Small Object Detection Small Object Detection
School of Electronic Information, Wuhan University, Wuhan, 430072 China
Learning Where to Embed: Noise-Aware Positional Embedding for Query Retrieval in Small-Object Detection
Apr 16, 2026Yangchen Zeng, Zhenyu Yu, Dongming Jiang +5Small Object Detection Efficient Object Detection
Southeast University · Fudan University · The University of Texas at Dallas +6
FSDETR: Frequency-Spatial Feature Enhancement for Small Object Detection
Apr 16, 2026Jianchao Huang, Fengming Zhang, Haibo Zhu +1Frequency-Domain Feature Learning Small Object Detection
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China
DETR-ViP: Detection Transformer with Robust Discriminative Visual Prompts
Apr 16, 2026Bo Qian, Dahu Shi, Xing WeiOpen-Vocabulary Object Detection Detection Transformer
School of Software Engineering, Xi’an Jiaotong University · State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University · Work done during the internship at Hikrobot Co., Ltd. +2
LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole-Slide Images
Jan 6, 2026Matěj Pekár, Vít Musil, Rudolf Nenutil +2Computational Pathology Image Segmentation
Masaryk University, Faculty of Informatics, Botanická 68a, Brno, 602 00, Czech Republic · Masaryk Memorial Cancer Institute, Žlutý kopec 7, Brno, 656 53, Czech Republic · BBMRI-ERIC, Neue Stiftingtalstraße 2/B/6, Graz, 8010, Austria
DETRPose: Real-Time End-to-End Multi-Person Pose Estimation via Modified Transformer Decoder and Novel Denoising Keypoints
Jun 16, 2025Sebastian Janampa, Marios PattichisDetection Transformer Efficient ViTs
The University of New Mexico
Exploiting Stability-Plasticity Asymmetry in Pretrained Detectors for Incremental Object Detection
Apr 14, 2025Songze Li, Qixing Xu, Tonghua Su +3Class-Incremental Learning Detection Transformer
Harbin Institute of Technology, Harbin, China · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China · Chongqing Research Institute of HIT, Chongqing, China +2
OrientedFormer: An End-to-End Transformer-Based Oriented Object Detector in Remote Sensing Images
Sep 29, 2024Zeyu Ding, Yong Zhou, Jiaqi Zhao +4Remote Sensing Image Understanding Detection Transformer
School of Computer Science and Technology, China University of Mining and Technology, and with Mine Digitization Engineering Research Center of the Ministry of Education, and also with Innovation Research Center of Disaster Intelligent Prevention and Emergency Rescue, China University of Mining and Technology, Xuzhou 221116, China · School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada

Jun 3, 2026, cs.CVJ/K move · Enter open · S save

Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes

Renjie Liang, Zhengkang Fan, Jinqian Pan, Chenkun Sun+3

Department of Health Outcomes and Biomedical Informatics, University of Florida, 2004 Mowry Road, Gainesville, 32611, FL, USA · Department of Biostatistics and Health Data Science, Indiana University School of Medicine, 340 West 10th Street, Indianapolis, 46202, IN, USA · Center of Biomedical Informatics, 1101 W 10th St, Indianapolis, Indianapolis, 46202, IN, USA+1

Medical Image Analysis Detection Transformer 3D Object Detection Computed Tomography

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Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level. We reformulate kidney CT characterization as a per-lesion set-prediction task: one model emits a variable number of lesions per kidney, each with four clinical attributes. We curated 2,619 CT volumes from 788 patients at one academic medical center, with multi-granularity side- and per-lesion labels, and used KiTS23 (489 cases) for zero-shot external validation. We propose \textbf{LesionDETR}, a DETR-style architecture with size-distance Hungarian matching and a hierarchical loss that aggregates per-slot outputs to side-level objectives. Across four input representations and six encoder initializations, two design choices dominate: a segmentation mask as an input channel, and same-domain abdominal pretraining (SuPreM); generic large-corpus pretraining is no better than random initialization. LesionDETR reaches bilateral side-level abnormality AUC

0.799 \pm 0.009

on UF-Health and

0.817 \pm 0.072

on KiTS23. A count-conditioned variant reaches per-lesion mAP

0.190 \pm 0.083

on cystic lesions; rare solid-lesion AP stays at the noise floor, pointing to targeted data collection, not architecture, as the next bottleneck. The framework yields verified per-lesion predictions for downstream structured report generation.

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