Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T06:04:41.383653Z
Paper Citation Record · LEDGER
As of 15 August 2026, this Paper Citation Record lists 100 of 180 outbound references and 0 inbound Pith citation observations for arXiv:1909.00169.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T06:04:41.383653Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
100 of 180 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 38b2d716-3d91-4c11-94b0-057720165e36 · outbound
Imbalance Problems in Object Detection: A Review A closer look at faster r-cnn for vehicle detection,
Reference 1
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Observation aa7fd586-4e83-4b0e-8ec5-74fe60dbaeb2 · outbound
Imbalance Problems in Object Detection: A Review Fore- ground gating and background refining network for surveillance object detection,
Reference 2
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Observation 570470e0-dff0-4118-a836-dd8093bef3eb · outbound
Imbalance Problems in Object Detection: A Review Are we ready for au- tonomous driving? the kitti vision benchmark suite,
Reference 3
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Observation 42007d3a-3001-4177-919b-4e52b7af2302 · outbound
Imbalance Problems in Object Detection: A Review Hybridnet: A fast vehicle detection system for au- tonomous driving,
Reference 4
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Observation 114aa44d-9c44-494e-bf60-7aa7218f453a · outbound
Imbalance Problems in Object Detection: A Review Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
Reference 5
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Observation a2e1c156-9ea4-4b86-95a9-b706836ec428 · outbound
Imbalance Problems in Object Detection: A Review Liver lesion detection from weakly-labeled multi-phase ct volumes with a grouped single shot multibox detector,
Reference 6
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Observation e7f2f0dc-3043-44d1-9882-cdb0de948f81 · outbound
Imbalance Problems in Object Detection: A Review Bb8: A scalable, accurate, robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth,
Reference 7
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Observation b9bc8117-9ea6-4c43-a91d-5e9f8733e342 · outbound
Imbalance Problems in Object Detection: A Review Ssd- 6d: Making rgb-based 3d detection and 6d pose estimation great again,
Reference 8
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Observation c997a4b6-4f7c-4cc8-a9ce-87a96ff21499 · outbound
Imbalance Problems in Object Detection: A Review Real-Time Seamless Single Shot 6D Object Pose Prediction,
Reference 9
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Observation 8500e614-ef43-4951-b19b-c0df5878c336 · outbound
Imbalance Problems in Object Detection: A Review Bop: Benchmark for 6d object pose estimation,
Reference 10
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Observation f6c8ab50-b06a-4aca-8e81-3dac4948ebdc · outbound
Imbalance Problems in Object Detection: A Review Vision-based Robotic Grasping From Object Localization, Object Pose Estimation to Grasp Estimation for Parallel Grippers: A Review
Reference 11
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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation ab5a53eb-2867-4391-ad61-b798797a7e77 · outbound
Imbalance Problems in Object Detection: A Review Cosmo: Contextualized scene modeling with boltzmann machines,
Reference 12
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Observation a17da334-3913-4b57-bd47-3baefa268ccb · outbound
Imbalance Problems in Object Detection: A Review Object detection with discriminatively trained part- based models,
Reference 13
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Observation 33de661e-8bd3-4b2c-81cc-1c29960f22cf · outbound
Imbalance Problems in Object Detection: A Review Imagenet classifi- cation with deep convolutional neural networks,
Reference 14
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Observation cc3f6050-258a-452d-b6e9-13ff4e413aec · outbound
Imbalance Problems in Object Detection: A Review YOLO9000: Better, faster, stronger,
Reference 15
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Observation 6c2c6c58-86f9-4678-bd32-2a15a8c3d723 · outbound
Imbalance Problems in Object Detection: A Review Rich feature hierarchies for accurate object detection and semantic segmen- tation,
Reference 16
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Observation 7c3866b9-90aa-4698-87de-647f093af2e7 · outbound
Imbalance Problems in Object Detection: A Review Fast R-CNN,
Reference 17
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Observation 44071085-1d40-47ae-8257-022d4ed4b922 · outbound
Imbalance Problems in Object Detection: A Review R-FCN: Object detection via region-based fully convolutional networks,
Reference 18
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Observation c25768f2-6205-4708-9679-8bd573402ebc · outbound
Imbalance Problems in Object Detection: A Review SSD: single shot multibox detector,
Reference 19
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Observation 17070827-3cdb-4e24-a5df-5c7be7e630f2 · outbound
Imbalance Problems in Object Detection: A Review You only look once: Unified, real-time object detection,
Reference 20
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Observation ccbc51fb-7376-4628-af3b-5285e5d39816 · outbound
Imbalance Problems in Object Detection: A Review Faster R-CNN: Towards real-time object detection with region proposal networks,
Reference 21
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Observation 9f83ff57-fafa-447f-8d55-4d5114b33218 · outbound
Imbalance Problems in Object Detection: A Review Focal loss for dense object detection,
Reference 22
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Observation 4dadf3f2-5af0-46e5-9a1b-c29bb110f2ec · outbound
Imbalance Problems in Object Detection: A Review Cornernet: Detecting objects as paired keypoints,
Reference 23
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Observation a4799d81-e0ac-49ac-886c-b9439682d58c · outbound
Imbalance Problems in Object Detection: A Review Training region- based object detectors with online hard example mining,
Reference 24
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Observation 69cdb62c-daec-4b35-a30b-012926ecb989 · outbound
Imbalance Problems in Object Detection: A Review Factors in finetun- ing deep model for object detection with long-tail distribution,
Reference 25
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Observation 3edc3e22-6d39-4c65-9766-ccc9660194a5 · outbound
Imbalance Problems in Object Detection: A Review Feature pyramid networks for object detection,
Reference 26
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Observation 065798e1-59ab-4136-a98f-769b1f16e0fe · outbound
Imbalance Problems in Object Detection: A Review An analysis of scale invariance in object detection - snip,
Reference 27
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Observation 545185e0-0024-4dec-87cd-30a06a686555 · outbound
Imbalance Problems in Object Detection: A Review Sniper: Efficient multi-scale training,
Reference 28
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Observation 006ac5c3-9296-44be-9133-e4827cff16fb · outbound
Imbalance Problems in Object Detection: A Review Libra R-CNN: Towards balanced learning for object detection,
Reference 29
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Observation 72f1d4a6-f4d7-4c2b-8569-e3ee902002bd · outbound
Imbalance Problems in Object Detection: A Review Prime Sample Attention in Object Detection
Reference 30
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 6bdbb176-e53f-4a4b-a784-be6c699d12f3 · outbound
Imbalance Problems in Object Detection: A Review Deep Learning for Generic Object Detection: A Survey
Reference 31
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Observation b337a9e2-f4a0-44dc-bf7a-7be91844775c · outbound
Imbalance Problems in Object Detection: A Review Object Detection in 20 Years: A Survey
Reference 32
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Observation ce05c904-baeb-41f1-8259-1604fa470f12 · outbound
Imbalance Problems in Object Detection: A Review Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
Reference 33
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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 34f3ce13-66c6-428e-b746-139572d96150 · outbound
Imbalance Problems in Object Detection: A Review On-road vehicle detection: a review,
Reference 34
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Observation c1d48bc9-527d-417a-8afc-27d06e16d295 · outbound
Imbalance Problems in Object Detection: A Review Pedestrian detec- tion: An evaluation of the state of the art,
Reference 35
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Observation 3789c15b-44b2-48e8-a79e-b42748fe68d6 · outbound
Imbalance Problems in Object Detection: A Review A survey on face detection in the wild,
Reference 36
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Observation 775f9d15-4098-4d4e-be05-9008a97f1018 · outbound
Imbalance Problems in Object Detection: A Review Text detection and recognition in imagery: A survey,
Reference 37
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Observation 8ca146f1-26ca-4ab5-9e19-da3f59b97f76 · outbound
Imbalance Problems in Object Detection: A Review Text detection, tracking and recognition in video: A comprehensive survey,
Reference 38
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Observation bf6b0d78-c4be-4061-bbf6-3bbe37be6b90 · outbound
Imbalance Problems in Object Detection: A Review A survey on deep learning in medical image analysis,
Reference 39
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Observation a13dc5a0-3e1b-43e7-b39c-05ffea33b5d0 · outbound
Imbalance Problems in Object Detection: A Review Learning from imbalanced data: open challenges and future directions,
Reference 40
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Observation f26a16d3-c97c-4074-9f2c-f8f9e3dde808 · outbound
Imbalance Problems in Object Detection: A Review A survey on addressing high-class imbalance in big data,
Reference 41
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Observation 3ee1b756-5551-4fe6-b17f-3ccd74b46151 · outbound
Imbalance Problems in Object Detection: A Review Fernández, S
Reference 42
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Observation 7cec087c-545c-4a2d-9459-7c0d5b5b1d5f · outbound
Imbalance Problems in Object Detection: A Review Survey on deep learning with class imbalance,
Reference 43
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Observation c622a246-c9e2-490f-9aaf-88c51427e3b1 · outbound
Imbalance Problems in Object Detection: A Review Selective search for object recognition,
Reference 44
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Observation b80cc4d2-6a43-459d-920d-a8f421d046f5 · outbound
Imbalance Problems in Object Detection: A Review Edge boxes: Locating object propos- als from edges,
Reference 45
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Observation 78142c98-973a-40fa-97fe-3c35d2700d46 · outbound
Imbalance Problems in Object Detection: A Review DSSD : Deconvolutional Single Shot Detector
Reference 46
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Observation 6fe2f438-e671-4dd6-9118-d87a2284ab23 · outbound
Imbalance Problems in Object Detection: A Review YOLOv3: An Incremental Improvement
Reference 47
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Observation 7f53bdb0-4b5e-43ec-bcc7-eefe95b723c7 · outbound
Imbalance Problems in Object Detection: A Review Centernet: Keypoint triplets for object detection,
Reference 48
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Observation 819788aa-6c07-4eb5-9531-86110c2e63fd · outbound
Imbalance Problems in Object Detection: A Review Bottom-up object detection by grouping extreme and center points,
Reference 49
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Observation 5840a99c-77b8-4c31-a227-8f893de6c2ee · outbound
Imbalance Problems in Object Detection: A Review Associative embedding: End- to-end learning for joint detection and grouping,
Reference 50
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Observation e7aa1fd5-65b7-4266-8ef3-2375da6a1580 · outbound
Imbalance Problems in Object Detection: A Review The pascal visual object classes (voc) challenge,
Reference 51
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Observation 910c8fee-53f7-4fc3-b105-fede04fad095 · outbound
Imbalance Problems in Object Detection: A Review Ima- geNet: A Large-Scale Hierarchical Image Database,
Reference 52
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Observation cd2e4d0d-e1f5-4a35-9faf-d26b30a119c4 · outbound
Imbalance Problems in Object Detection: A Review Bounding box regression with uncertainty for accurate object detection,
Reference 53
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Observation c9928d2b-8e63-4efb-a2ac-2f1e43df1d17 · outbound
Imbalance Problems in Object Detection: A Review Is Heuristic Sampling Necessary in Training Deep Object Detectors?
Reference 54
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation c058ddd1-2aa3-4cb7-a8a0-3b7c48b35268 · outbound
Imbalance Problems in Object Detection: A Review Human face detection in visual scenes,
Reference 55
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Observation 7049eb43-693a-4fbb-96de-01756c3eb269 · outbound
Imbalance Problems in Object Detection: A Review Ron: Reverse connection with objectness prior networks for object detection,
Reference 56
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Observation 72d82095-3484-4da0-8625-5b8daa8ae920 · outbound
Imbalance Problems in Object Detection: A Review Single-shot refinement neural network for object detection,
Reference 57
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Observation d9971513-0759-4627-83b3-16ebac1fba0c · outbound
Imbalance Problems in Object Detection: A Review Enriched feature guided refinement network for object detection,
Reference 58
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Observation c19ebace-014e-4c4e-b788-3af95d623f0a · outbound
Imbalance Problems in Object Detection: A Review Gradient harmonized single-stage detector,
Reference 59
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Observation 536f25ac-92b7-4fb9-9dc9-ce3f349ea28f · outbound
Imbalance Problems in Object Detection: A Review Residual Objectness for Imbalance Reduction
Reference 60
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Observation f4676cae-d3b3-4069-a7df-5a7d501137f0 · outbound
Imbalance Problems in Object Detection: A Review Towards accurate one-stage object detection with ap-loss,
Reference 61
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Observation 367a0880-6aca-49f0-9d8e-f08943a18d35 · outbound
Imbalance Problems in Object Detection: A Review DR Loss: Improving Object Detection by Distributional Ranking
Reference 62
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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 328164b4-fdc1-4fbe-9225-147bd21d635b · outbound
Imbalance Problems in Object Detection: A Review A-fast-rcnn: Hard pos- itive generation via adversary for object detection,
Reference 63
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Observation feb68a83-ba65-4014-8cd9-d5ecb1835b78 · outbound
Imbalance Problems in Object Detection: A Review Learning to generate synthetic data via compositing,
Reference 64
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Observation bfa61a81-4c41-4f42-95ab-eb12ecec12ce · outbound
Imbalance Problems in Object Detection: A Review Data Augmentation for Object Detection via Progressive and Selective Instance-Switching
Reference 65
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Observation 0dcad1fb-00d5-453b-961b-97a716112340 · outbound
Imbalance Problems in Object Detection: A Review Generating posi- tive bounding boxes for balanced training of object detectors,
Reference 66
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Observation b8c34c17-0f94-403f-a3a2-36ea68f34793 · outbound
Imbalance Problems in Object Detection: A Review Region proposal by guided anchoring,
Reference 67
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Observation da180bdd-e594-4958-b3f0-bd5ccdf54b43 · outbound
Imbalance Problems in Object Detection: A Review Freeanchor: Learning to match anchors for visual object detection,
Reference 68
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Observation 30aca8c3-ed75-4098-86d2-e9504276750e · outbound
Imbalance Problems in Object Detection: A Review Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers,
Reference 69
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Observation 05cc2cae-7b4c-44e1-941a-eee60ed99feb · outbound
Imbalance Problems in Object Detection: A Review A unified multi- scale deep convolutional neural network for fast object detec- tion,
Reference 70
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Observation 79ec6624-9738-4345-b495-00f137f6c892 · outbound
Imbalance Problems in Object Detection: A Review Scale-aware fast r-cnn for pedestrian detection,
Reference 71
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Observation fa1f5613-502c-4850-b271-4e755da8047f · outbound
Imbalance Problems in Object Detection: A Review Efficient featurized image pyramid network for single shot detector,
Reference 72
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Observation f4073d65-c839-4927-a440-44b435f864f0 · outbound
Imbalance Problems in Object Detection: A Review Better to follow, follow to be better: Towards precise supervision of feature super- resolution for small object detection,
Reference 73
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Observation 0a91cb54-bb59-4abb-9874-27b6f18678ed · outbound
Imbalance Problems in Object Detection: A Review Scale-aware trident net- works for object detection,
Reference 74
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Observation b25d28b3-19b0-47ca-83e4-c6a5a02007b3 · outbound
Imbalance Problems in Object Detection: A Review Path aggregation network for instance segmentation,
Reference 75
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Observation 79dbb9ad-1f12-45be-9016-f047a286a5dc · outbound
Imbalance Problems in Object Detection: A Review Scale-transferrable object detection,
Reference 76
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Observation 5510f108-b786-44ff-80fd-5421288d6b98 · outbound
Imbalance Problems in Object Detection: A Review Parallel feature pyramid network for object detection,
Reference 77
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Observation b7988a78-babd-47ff-9e18-e645c04f0057 · outbound
Imbalance Problems in Object Detection: A Review Deep feature pyramid reconfiguration for object detection,
Reference 78
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Observation ea440d94-ff54-45a5-a607-3dc89ec04608 · outbound
Imbalance Problems in Object Detection: A Review Zoom out-and-in network with map attention decision for region proposal and object detection,
Reference 79
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Observation 6b60cf39-9eac-40c7-a71d-303769aaffa6 · outbound
Imbalance Problems in Object Detection: A Review M2det: A single-shot object detector based on multi-level feature pyramid network,
Reference 80
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Observation 95d66d8c-328c-4dec-aadc-1a1d683c291a · outbound
Imbalance Problems in Object Detection: A Review NAS-FPN: learning scalable feature pyramid architecture for object detection,
Reference 81
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Observation 5d7c5c66-cb09-4fdb-9b32-68f5fecee50a · outbound
Imbalance Problems in Object Detection: A Review Auto-fpn: Automatic network architecture adaptation for object detection beyond classification,
Reference 82
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Observation b07c6287-cebd-49a0-b92a-48f1620fb5a6 · outbound
Imbalance Problems in Object Detection: A Review Unitbox: An advanced object detection network,
Reference 83
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Observation 74e30a1c-50ec-4334-983f-6cfa5bc8c6c7 · outbound
Imbalance Problems in Object Detection: A Review Improving object local- ization with fitness nms and bounded iou loss,
Reference 84
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Observation b8806803-03dd-450e-811d-b53e9ac2301a · outbound
Imbalance Problems in Object Detection: A Review Generalized intersection over union: A metric and a loss for bounding box regression,
Reference 85
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Observation 0efba9f4-e613-419f-ab6a-de54983fdba7 · outbound
Imbalance Problems in Object Detection: A Review Distance-iou loss: Faster and better learning for bounding box regression,
Reference 86
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Observation f1fd0056-7f17-4cad-befa-d4db11585174 · outbound
Imbalance Problems in Object Detection: A Review Cascade R-CNN: Delving into high quality object detection,
Reference 87
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Observation 31896a36-9727-4489-9a04-2acccca60faf · outbound
Imbalance Problems in Object Detection: A Review Hierarchical shot detector,
Reference 88
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Observation 0139498b-51a2-4252-8fe7-e5180a37d3c0 · outbound
Imbalance Problems in Object Detection: A Review IoU-uniform R-CNN: Breaking Through the Limitations of RPN
Reference 89
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Observation 77bf2ce4-d65c-43dd-9d7a-acfb02a12d84 · outbound
Imbalance Problems in Object Detection: A Review Microsoft COCO: Common Ob- jects in Context,
Reference 90
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Observation 54312ff8-2cfb-4c5c-bc24-88fc7ef02d0b · outbound
Imbalance Problems in Object Detection: A Review The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale
Reference 91
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Observation eac7803e-b535-461a-a177-ba5e80d79ae7 · outbound
Imbalance Problems in Object Detection: A Review Objects365: A large-scale, high-quality dataset for object detection,
Reference 92
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Observation 7d3c7e2c-f2a5-4afd-aa27-be05c4208d51 · outbound
Imbalance Problems in Object Detection: A Review Deep residual learning for image recognition,
Reference 93
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Observation eb85623a-b7d4-4770-964b-c2c8c3fc7fac · outbound
Imbalance Problems in Object Detection: A Review Example-based learning for view- based human face detection,
Reference 94
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Observation 265c8c81-3f93-4dac-b938-f776780ec596 · outbound
Imbalance Problems in Object Detection: A Review Rapid object detection using a boosted cascade of simple features,
Reference 95
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Observation 4ae7e75d-6dd3-4d65-895c-e18118ee3134 · outbound
Imbalance Problems in Object Detection: A Review Histograms of oriented gradients for human detection,
Reference 96
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Observation ee1a2b6b-28a3-4422-b0b4-f46d8e56f4fa · outbound
Imbalance Problems in Object Detection: A Review Training deep neural networks via direct loss minimization,
Reference 97
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Observation 892d17a8-2f9a-486c-a3b2-bca8aef4c223 · outbound
Imbalance Problems in Object Detection: A Review End-to-end training of object class detectors for mean average precision,
Reference 98
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Observation 897ccec7-4f74-4e04-a17a-8b0547831a15 · outbound
Imbalance Problems in Object Detection: A Review Cut, paste and learn: Surprisingly easy synthesis for instance detection,
Reference 99
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Observation adf651fa-2935-4e8e-a7e4-c2414cb9ec1f · outbound
Imbalance Problems in Object Detection: A Review Modeling visual context is key to augmenting object detection datasets,
Reference 100
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No inbound Pith citation observations are available.