{"list":"other","total":54,"rows":[{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1506.01497","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","created":"2015-06-04","cited_by_count":9639},{"id":"2005.12872","title":"End-to-End Object Detection with Transformers","created":"2020-05-26","cited_by_count":7414},{"id":"1703.06870","title":"Mask R-CNN","created":"2017-03-20","cited_by_count":7277},{"id":"1612.03144","title":"Feature Pyramid Networks for Object Detection","created":"2016-12-09","cited_by_count":5036},{"id":"1506.02640","title":"You Only Look Once: Unified, Real-Time Object Detection","created":"2015-06-08","cited_by_count":4852},{"id":"2010.04159","title":"Deformable DETR: Deformable Transformers for End-to-End Object Detection","created":"2020-10-08","cited_by_count":3422},{"id":"1512.02325","title":"SSD: Single Shot MultiBox Detector","created":"2015-12-08","cited_by_count":3130},{"id":"1504.08083","title":"Fast R-CNN","created":"2015-04-30","cited_by_count":2933},{"id":"1804.02767","title":"YOLOv3: An Incremental Improvement","created":"2018-04-08","cited_by_count":2659},{"id":"1311.2901","title":"Visualizing and Understanding Convolutional Networks","created":"2013-11-12","cited_by_count":2290},{"id":"1311.2524","title":"Rich feature hierarchies for accurate object detection and semantic segmentation","created":"2013-11-11","cited_by_count":2162},{"id":"2004.10934","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","created":"2020-04-23","cited_by_count":1630},{"id":"1612.08242","title":"YOLO9000: Better, Faster, Stronger","created":"2016-12-25","cited_by_count":1431},{"id":"1908.07919","title":"Deep High-Resolution Representation Learning for Visual Recognition","created":"2019-08-20","cited_by_count":1420},{"id":"1908.03195","title":"LVIS: A Dataset for Large Vocabulary Instance Segmentation","created":"2019-08-08","cited_by_count":1217},{"id":"2207.02696","title":"YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors","created":"2022-07-06","cited_by_count":982},{"id":"1406.4729","title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","created":"2014-06-18","cited_by_count":720},{"id":"2011.12450","title":"Sparse R-CNN: End-to-End Object Detection with Learnable Proposals","created":"2020-11-25","cited_by_count":495},{"id":"2006.11392","title":"PraNet: Parallel Reverse Attention Network for Polyp Segmentation","created":"2020-06-13","cited_by_count":460},{"id":"1605.06409","title":"R-FCN: Object Detection via Region-based Fully Convolutional Networks","created":"2016-05-20","cited_by_count":414},{"id":"2211.09788","title":"DiffusionDet: Diffusion Model for Object Detection","created":"2022-11-17","cited_by_count":354},{"id":"2105.09451","title":"Anabranch Network for Camouflaged Object Segmentation","created":"2021-05-20","cited_by_count":227},{"id":"2102.10274","title":"Concealed Object Detection","created":"2021-02-20","cited_by_count":212},{"id":"2103.04011","title":"Simultaneously Localize, Segment and Rank the Camouflaged Objects","created":"2021-03-06","cited_by_count":198},{"id":"2104.10475","title":"Camouflaged Object Segmentation with Distraction Mining","created":"2021-04-21","cited_by_count":151},{"id":"1809.08545","title":"Bounding Box Regression with Uncertainty for Accurate Object Detection","created":"2018-09-23","cited_by_count":143},{"id":"2008.08115","title":"TIDE: A General Toolbox for Identifying Object Detection Errors","created":"2020-08-18","cited_by_count":114},{"id":"2104.02628","title":"Uncertainty-aware Joint Salient Object and Camouflaged Object Detection","created":"2021-04-06","cited_by_count":114},{"id":"2104.02613","title":"Mutual Graph Learning for Camouflaged Object Detection","created":"2021-04-03","cited_by_count":103},{"id":"2305.11003","title":"Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping","created":"2023-05-18","cited_by_count":92},{"id":"2105.12555","title":"Context-aware Cross-level Fusion Network for Camouflaged Object Detection","created":"2021-05-26","cited_by_count":86},{"id":"2103.17202","title":"GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection","created":"2021-03-31","cited_by_count":66},{"id":"2003.01279","title":"Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems","created":"2020-03-03","cited_by_count":65},{"id":"2205.12853","title":"Deep Gradient Learning for Efficient Camouflaged Object Detection","created":"2022-05-25","cited_by_count":65},{"id":"2203.07363","title":"Implicit Motion Handling for Video Camouflaged Object Detection","created":"2022-03-14","cited_by_count":60},{"id":"2106.07873","title":"Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images","created":"2021-06-15","cited_by_count":58},{"id":"2308.03166","title":"Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects","created":"2023-08-06","cited_by_count":56},{"id":"1505.01749","title":"Object detection via a multi-region & semantic segmentation-aware CNN model","created":"2015-05-07","cited_by_count":47},{"id":"2009.09869","title":"FakeTagger: Robust Safeguards against DeepFake Dissemination via Provenance Tracking","created":"2020-09-21","cited_by_count":46},{"id":"2111.03216","title":"Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network","created":"2021-11-05","cited_by_count":42},{"id":"2102.02996","title":"Deep Texture-Aware Features for Camouflaged Object Detection","created":"2021-02-05","cited_by_count":36},{"id":"2203.15880","title":"Proactive Image Manipulation Detection","created":"2022-03-29","cited_by_count":35},{"id":"2207.13362","title":"Camouflaged Object Detection via Context-aware Cross-level Fusion","created":"2022-07-27","cited_by_count":32},{"id":"1711.07264","title":"Light-Head R-CNN: In Defense of Two-Stage Object Detector","created":"2017-11-20","cited_by_count":30},{"id":"2303.16976","title":"MaLP: Manipulation Localization Using a Proactive Scheme","created":"2023-03-29","cited_by_count":26},{"id":"1711.07201","title":"End-to-end Trained CNN Encode-Decoder Networks for Image Steganography","created":"2017-11-20","cited_by_count":18},{"id":"2110.15606","title":"Improving Camouflaged Object Detection with the Uncertainty of Pseudo-edge Labels","created":"2021-10-29","cited_by_count":16},{"id":"1812.00440","title":"Pedestrian Detection with Autoregressive Network Phases","created":"2018-12-02","cited_by_count":12},{"id":"2006.12247","title":"OGAN: Disrupting Deepfakes with an Adversarial Attack that Survives Training","created":"2020-06-17","cited_by_count":8},{"id":"2106.06485","title":"Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization","created":"2021-06-11","cited_by_count":8},{"id":"1906.05388","title":"Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors","created":"2019-06-12","cited_by_count":2},{"id":"2010.09870","title":"DeepApple: Deep Learning-based Apple Detection using a Suppression Mask R-CNN","created":"2020-10-19","cited_by_count":2}],"next_cursor":null}