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Paper Citation Record · LEDGER

Imbalance Problems in Object Detection: A Review

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.

pith.paper-citation-record.v1
1909.00169 v3

Coverage vector

measured 100 of 180 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T06:04:41.383653Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

100 of 180 outbound references displayed

  • verified exact8
  • verified fuzzy0
  • unresolved92
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 38b2d716-3d91-4c11-94b0-057720165e36 · outbound

This paper cites A closer look at faster r-cnn for vehicle detection,.

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

This paper cites Fore- ground gating and background refining network for surveillance object detection,.

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

This paper cites Are we ready for au- tonomous driving? the kitti vision benchmark suite,.

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

This paper cites Hybridnet: A fast vehicle detection system for au- tonomous driving,.

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

This paper cites Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection.

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

This paper cites Liver lesion detection from weakly-labeled multi-phase ct volumes with a grouped single shot multibox detector,.

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

This paper cites Bb8: A scalable, accurate, robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth,.

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

This paper cites Ssd- 6d: Making rgb-based 3d detection and 6d pose estimation great again,.

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

This paper cites Real-Time Seamless Single Shot 6D Object Pose Prediction,.

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

This paper cites Bop: Benchmark for 6d object pose estimation,.

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

This paper cites Vision-based Robotic Grasping From Object Localization, Object Pose Estimation to Grasp Estimation for Parallel Grippers: A Review.

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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Observation ab5a53eb-2867-4391-ad61-b798797a7e77 · outbound

This paper cites Cosmo: Contextualized scene modeling with boltzmann machines,.

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

This paper cites Object detection with discriminatively trained part- based models,.

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

This paper cites Imagenet classifi- cation with deep convolutional neural networks,.

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

This paper cites YOLO9000: Better, faster, stronger,.

Imbalance Problems in Object Detection: A Review YOLO9000: Better, faster, stronger,

Reference 15

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Observation 6c2c6c58-86f9-4678-bd32-2a15a8c3d723 · outbound

This paper cites Rich feature hierarchies for accurate object detection and semantic segmen- tation,.

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

This paper cites Fast R-CNN,.

Imbalance Problems in Object Detection: A Review Fast R-CNN,

Reference 17

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Observation 44071085-1d40-47ae-8257-022d4ed4b922 · outbound

This paper cites R-FCN: Object detection via region-based fully convolutional networks,.

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

This paper cites SSD: single shot multibox detector,.

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

This paper cites You only look once: Unified, real-time object detection,.

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

This paper cites Faster R-CNN: Towards real-time object detection with region proposal networks,.

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

This paper cites Focal loss for dense object detection,.

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

This paper cites Cornernet: Detecting objects as paired keypoints,.

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

This paper cites Training region- based object detectors with online hard example mining,.

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

This paper cites Factors in finetun- ing deep model for object detection with long-tail distribution,.

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

This paper cites Feature pyramid networks for object detection,.

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

This paper cites An analysis of scale invariance in object detection - snip,.

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

This paper cites Sniper: Efficient multi-scale training,.

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

This paper cites Libra R-CNN: Towards balanced learning for object detection,.

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

This paper cites Prime Sample Attention in Object Detection.

Imbalance Problems in Object Detection: A Review Prime Sample Attention in Object Detection

Reference 30

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Observation 6bdbb176-e53f-4a4b-a784-be6c699d12f3 · outbound

This paper cites Deep Learning for Generic Object Detection: A Survey.

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

This paper cites Object Detection in 20 Years: A Survey.

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

This paper cites Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks.

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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Observation 34f3ce13-66c6-428e-b746-139572d96150 · outbound

This paper cites On-road vehicle detection: a review,.

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

This paper cites Pedestrian detec- tion: An evaluation of the state of the art,.

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

This paper cites A survey on face detection in the wild,.

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

This paper cites Text detection and recognition in imagery: A survey,.

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

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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

This paper cites A survey on deep learning in medical image analysis,.

Imbalance Problems in Object Detection: A Review A survey on deep learning in medical image analysis,

Reference 39

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source=pdf_text observed=2026-08-14T06:04:41.167549Z digest=sha256:fea4d3880a12a19d84087f541666fa999905032a23477b6911f039f21d85bd59

Observation a13dc5a0-3e1b-43e7-b39c-05ffea33b5d0 · outbound

This paper cites Learning from imbalanced data: open challenges and future directions,.

Imbalance Problems in Object Detection: A Review Learning from imbalanced data: open challenges and future directions,

Reference 40

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source=pdf_text observed=2026-08-14T06:04:41.170985Z digest=sha256:b14cd7d99516aa79f73ea793085f7c330e05b60a28d874d23890f1d8142f6f7b

Observation f26a16d3-c97c-4074-9f2c-f8f9e3dde808 · outbound

This paper cites A survey on addressing high-class imbalance in big data,.

Imbalance Problems in Object Detection: A Review A survey on addressing high-class imbalance in big data,

Reference 41

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source=pdf_text observed=2026-08-14T06:04:41.174144Z digest=sha256:31b85959759f79448eb463060966cc0a60f5f547180a417644fccbf57e8fceff

Observation 3ee1b756-5551-4fe6-b17f-3ccd74b46151 · outbound

This paper cites Fernández, S.

Imbalance Problems in Object Detection: A Review Fernández, S

Reference 42

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source=pdf_text observed=2026-08-14T06:04:41.177249Z digest=sha256:3292e2ce0fdbda49d4485d11f685fd4afa94de78cff75743387a6d0849973351

Observation 7cec087c-545c-4a2d-9459-7c0d5b5b1d5f · outbound

This paper cites Survey on deep learning with class imbalance,.

Imbalance Problems in Object Detection: A Review Survey on deep learning with class imbalance,

Reference 43

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source=pdf_text observed=2026-08-14T06:04:41.180451Z digest=sha256:139c7d143fc3f3e3872504e34cf78eb2217e618d44ed6e798d2fce06f9e6ccee

Observation c622a246-c9e2-490f-9aaf-88c51427e3b1 · outbound

This paper cites Selective search for object recognition,.

Imbalance Problems in Object Detection: A Review Selective search for object recognition,

Reference 44

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source=pdf_text observed=2026-08-14T06:04:41.183380Z digest=sha256:71d48ee7858eb8d4b37ad80e98fdab4d37ff409065994e7420e7276ee493b3f0

Observation b80cc4d2-6a43-459d-920d-a8f421d046f5 · outbound

This paper cites Edge boxes: Locating object propos- als from edges,.

Imbalance Problems in Object Detection: A Review Edge boxes: Locating object propos- als from edges,

Reference 45

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source=pdf_text observed=2026-08-14T06:04:41.186183Z digest=sha256:bad282f20f8e6cdff7b0753bd3c3b4b078f0e9c1cc473206e277f512449e4f90

Observation 78142c98-973a-40fa-97fe-3c35d2700d46 · outbound

This paper cites DSSD : Deconvolutional Single Shot Detector.

Imbalance Problems in Object Detection: A Review DSSD : Deconvolutional Single Shot Detector

Reference 46

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source=pdf_text observed=2026-08-14T06:04:41.189096Z digest=sha256:1e73eca43c2542c9c82c55505ba2b5fdc1e31d3946ff21601437c7f01f532542

Observation 6fe2f438-e671-4dd6-9118-d87a2284ab23 · outbound

This paper cites YOLOv3: An Incremental Improvement.

Imbalance Problems in Object Detection: A Review YOLOv3: An Incremental Improvement

Reference 47

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source=pdf_text observed=2026-08-14T06:04:41.192704Z digest=sha256:c8aed959ff231976e21a89b726b43ffa6304649649232ad187e0971f6f7ba8e1

Observation 7f53bdb0-4b5e-43ec-bcc7-eefe95b723c7 · outbound

This paper cites Centernet: Keypoint triplets for object detection,.

Imbalance Problems in Object Detection: A Review Centernet: Keypoint triplets for object detection,

Reference 48

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source=pdf_text observed=2026-08-14T06:04:41.196808Z digest=sha256:50a3865058013145291e79c0df38d9c166c37cdf86963c8e533c76e65665d1ef

Observation 819788aa-6c07-4eb5-9531-86110c2e63fd · outbound

This paper cites Bottom-up object detection by grouping extreme and center points,.

Imbalance Problems in Object Detection: A Review Bottom-up object detection by grouping extreme and center points,

Reference 49

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source=pdf_text observed=2026-08-14T06:04:41.200290Z digest=sha256:2e768428e975f52e66bfa92f1428199db1fdf3bfa81352f543d635cd5b340fa7

Observation 5840a99c-77b8-4c31-a227-8f893de6c2ee · outbound

This paper cites Associative embedding: End- to-end learning for joint detection and grouping,.

Imbalance Problems in Object Detection: A Review Associative embedding: End- to-end learning for joint detection and grouping,

Reference 50

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source=pdf_text observed=2026-08-14T06:04:41.203730Z digest=sha256:78cec6f1953be2bcb29c2e9cd5e445733bb2e18765dbc8287743965f4ea3bbd7

Observation e7aa1fd5-65b7-4266-8ef3-2375da6a1580 · outbound

This paper cites The pascal visual object classes (voc) challenge,.

Imbalance Problems in Object Detection: A Review The pascal visual object classes (voc) challenge,

Reference 51

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source=pdf_text observed=2026-08-14T06:04:41.207129Z digest=sha256:e151f2aee4688361f26892475485509c7b1da59ef42e2c0d797c0d60aa6e7348

Observation 910c8fee-53f7-4fc3-b105-fede04fad095 · outbound

This paper cites Ima- geNet: A Large-Scale Hierarchical Image Database,.

Imbalance Problems in Object Detection: A Review Ima- geNet: A Large-Scale Hierarchical Image Database,

Reference 52

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source=pdf_text observed=2026-08-14T06:04:41.210438Z digest=sha256:80f9dfb505c4ab66e5d4d7018b5ec54245f22e07e724e27c780951efb7bcbf57

Observation cd2e4d0d-e1f5-4a35-9faf-d26b30a119c4 · outbound

This paper cites Bounding box regression with uncertainty for accurate object detection,.

Imbalance Problems in Object Detection: A Review Bounding box regression with uncertainty for accurate object detection,

Reference 53

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source=pdf_text observed=2026-08-14T06:04:41.213700Z digest=sha256:dfdd59f9dd47829ae6cf5134adb0261fec20d34bee613ace53a06ae3780578cb

Observation c9928d2b-8e63-4efb-a2ac-2f1e43df1d17 · outbound

This paper cites Is Heuristic Sampling Necessary in Training Deep Object Detectors?.

Imbalance Problems in Object Detection: A Review Is Heuristic Sampling Necessary in Training Deep Object Detectors?

Reference 54

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T06:04:41.217213Z digest=sha256:29163e032605e75277bb070f55661094af5e0e9bfe09d14b15bf9b4e80f5de3f

Observation c058ddd1-2aa3-4cb7-a8a0-3b7c48b35268 · outbound

This paper cites Human face detection in visual scenes,.

Imbalance Problems in Object Detection: A Review Human face detection in visual scenes,

Reference 55

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source=pdf_text observed=2026-08-14T06:04:41.220816Z digest=sha256:3dc956c8a871a313246276723fbc36f5f01f336380e4fd1527a7badfd2a76d72

Observation 7049eb43-693a-4fbb-96de-01756c3eb269 · outbound

This paper cites Ron: Reverse connection with objectness prior networks for object detection,.

Imbalance Problems in Object Detection: A Review Ron: Reverse connection with objectness prior networks for object detection,

Reference 56

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source=pdf_text observed=2026-08-14T06:04:41.224443Z digest=sha256:6e137a573461054d34d55e402e7aec40c55a3b2ed63e672268fadb3ee6a17f46

Observation 72d82095-3484-4da0-8625-5b8daa8ae920 · outbound

This paper cites Single-shot refinement neural network for object detection,.

Imbalance Problems in Object Detection: A Review Single-shot refinement neural network for object detection,

Reference 57

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source=pdf_text observed=2026-08-14T06:04:41.227996Z digest=sha256:6eb3b63c598431425a05edc4df4ac2c9622629595381ee649779a64ada756548

Observation d9971513-0759-4627-83b3-16ebac1fba0c · outbound

This paper cites Enriched feature guided refinement network for object detection,.

Imbalance Problems in Object Detection: A Review Enriched feature guided refinement network for object detection,

Reference 58

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source=pdf_text observed=2026-08-14T06:04:41.231569Z digest=sha256:802684bc433d82bd5651c4d62eee090d58bd57c578f097e96f1dfa3772f8b213

Observation c19ebace-014e-4c4e-b788-3af95d623f0a · outbound

This paper cites Gradient harmonized single-stage detector,.

Imbalance Problems in Object Detection: A Review Gradient harmonized single-stage detector,

Reference 59

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source=pdf_text observed=2026-08-14T06:04:41.235047Z digest=sha256:b984a484b17c420f3c7d1937a7d13ae7b6b07f7cabac87400185f8ab3cadbfb8

Observation 536f25ac-92b7-4fb9-9dc9-ce3f349ea28f · outbound

This paper cites Residual Objectness for Imbalance Reduction.

Imbalance Problems in Object Detection: A Review Residual Objectness for Imbalance Reduction

Reference 60

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source=pdf_text observed=2026-08-14T06:04:41.238467Z digest=sha256:4e163323ea898de67821e404c4cda60cf2afec5021a913b153aa976cb4227feb

Observation f4676cae-d3b3-4069-a7df-5a7d501137f0 · outbound

This paper cites Towards accurate one-stage object detection with ap-loss,.

Imbalance Problems in Object Detection: A Review Towards accurate one-stage object detection with ap-loss,

Reference 61

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source=pdf_text observed=2026-08-14T06:04:41.242520Z digest=sha256:78aeb8e3eb7c29f1f09c06b66e43dd18bf416b60c75b243ee504818ec7e3d47c

Observation 367a0880-6aca-49f0-9d8e-f08943a18d35 · outbound

This paper cites DR Loss: Improving Object Detection by Distributional Ranking.

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T06:04:41.246105Z digest=sha256:d1512ae70ef8b851b68d2e710711ac9b1170a88495ebd3541d61658c3143baa7

Observation 328164b4-fdc1-4fbe-9225-147bd21d635b · outbound

This paper cites A-fast-rcnn: Hard pos- itive generation via adversary for object detection,.

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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source=pdf_text observed=2026-08-14T06:04:41.249845Z digest=sha256:707a72eb2a4a2d48ac2f70428b565ad0b9b19ec696431631c58c6d3b9ce1fc76

Observation feb68a83-ba65-4014-8cd9-d5ecb1835b78 · outbound

This paper cites Learning to generate synthetic data via compositing,.

Imbalance Problems in Object Detection: A Review Learning to generate synthetic data via compositing,

Reference 64

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source=pdf_text observed=2026-08-14T06:04:41.253822Z digest=sha256:a359efc5b987c5d4389d5fd4788cd63c6dd6a09d22e79eb849789718d31cd8c8

Observation bfa61a81-4c41-4f42-95ab-eb12ecec12ce · outbound

This paper cites Data Augmentation for Object Detection via Progressive and Selective Instance-Switching.

Imbalance Problems in Object Detection: A Review Data Augmentation for Object Detection via Progressive and Selective Instance-Switching

Reference 65

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T06:04:41.257618Z digest=sha256:441589784dd0165872f97ff8a568b488b1d2abd2f4461d72b256e8ff910f216c

Observation 0dcad1fb-00d5-453b-961b-97a716112340 · outbound

This paper cites Generating posi- tive bounding boxes for balanced training of object detectors,.

Imbalance Problems in Object Detection: A Review Generating posi- tive bounding boxes for balanced training of object detectors,

Reference 66

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source=pdf_text observed=2026-08-14T06:04:41.261370Z digest=sha256:826383f24b7fc8cf5648392b30bc987a6f460fd212459f6fa41f1a1e44aff6c0

Observation b8c34c17-0f94-403f-a3a2-36ea68f34793 · outbound

This paper cites Region proposal by guided anchoring,.

Imbalance Problems in Object Detection: A Review Region proposal by guided anchoring,

Reference 67

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source=pdf_text observed=2026-08-14T06:04:41.265125Z digest=sha256:5bdf3448c5353d5e3c158a8d6ef760de08ea353f5a0d2d8be6d4506b7c38c202

Observation da180bdd-e594-4958-b3f0-bd5ccdf54b43 · outbound

This paper cites Freeanchor: Learning to match anchors for visual object detection,.

Imbalance Problems in Object Detection: A Review Freeanchor: Learning to match anchors for visual object detection,

Reference 68

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source=pdf_text observed=2026-08-14T06:04:41.268521Z digest=sha256:6086d0bb37087adb828411672db0d164cfc0009ed574de03eaf05cc84dfd61fb

Observation 30aca8c3-ed75-4098-86d2-e9504276750e · outbound

This paper cites Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers,.

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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source=pdf_text observed=2026-08-14T06:04:41.272063Z digest=sha256:8f135d8426b5d594453645274f6c0f10c54cfa1560241a07dded1c3e31a7486c

Observation 05cc2cae-7b4c-44e1-941a-eee60ed99feb · outbound

This paper cites A unified multi- scale deep convolutional neural network for fast object detec- tion,.

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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source=pdf_text observed=2026-08-14T06:04:41.275967Z digest=sha256:61b473c4bf69adf593eb71a944abe6ff101ca609a24d413f43115fc6b6b3c3a9

Observation 79ec6624-9738-4345-b495-00f137f6c892 · outbound

This paper cites Scale-aware fast r-cnn for pedestrian detection,.

Imbalance Problems in Object Detection: A Review Scale-aware fast r-cnn for pedestrian detection,

Reference 71

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source=pdf_text observed=2026-08-14T06:04:41.281339Z digest=sha256:b7e03746bab8083f71f1cafc199d60fb7ed6cdf3b5de5cb3e8e1ab554c54c14c

Observation fa1f5613-502c-4850-b271-4e755da8047f · outbound

This paper cites Efficient featurized image pyramid network for single shot detector,.

Imbalance Problems in Object Detection: A Review Efficient featurized image pyramid network for single shot detector,

Reference 72

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source=pdf_text observed=2026-08-14T06:04:41.287382Z digest=sha256:a48ca3fe9b728d12654ceed159a202894607c8f347212f17fa6b460d6ec03ec9

Observation f4073d65-c839-4927-a440-44b435f864f0 · outbound

This paper cites Better to follow, follow to be better: Towards precise supervision of feature super- resolution for small object detection,.

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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source=pdf_text observed=2026-08-14T06:04:41.290506Z digest=sha256:275ff702de33ecf5f3968d9a5363c42f3afc1de0ec09518ef42241c0a8316edb

Observation 0a91cb54-bb59-4abb-9874-27b6f18678ed · outbound

This paper cites Scale-aware trident net- works for object detection,.

Imbalance Problems in Object Detection: A Review Scale-aware trident net- works for object detection,

Reference 74

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source=pdf_text observed=2026-08-14T06:04:41.293330Z digest=sha256:9cc9162fadead7d0e93822c8a92df69ab1b37e5d4bf3105df565a6a4b9f75174

Observation b25d28b3-19b0-47ca-83e4-c6a5a02007b3 · outbound

This paper cites Path aggregation network for instance segmentation,.

Imbalance Problems in Object Detection: A Review Path aggregation network for instance segmentation,

Reference 75

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source=pdf_text observed=2026-08-14T06:04:41.296387Z digest=sha256:2f9b81064c1f971a3854e7247d59c64bfa6d0eb9ec23c846a8bc7b4fe2aeb76c

Observation 79dbb9ad-1f12-45be-9016-f047a286a5dc · outbound

This paper cites Scale-transferrable object detection,.

Imbalance Problems in Object Detection: A Review Scale-transferrable object detection,

Reference 76

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source=pdf_text observed=2026-08-14T06:04:41.299358Z digest=sha256:3ad8a105b66e921c3daf699b4d45fd9f8020b9fc9ded1b39301b8b697bae68a7

Observation 5510f108-b786-44ff-80fd-5421288d6b98 · outbound

This paper cites Parallel feature pyramid network for object detection,.

Imbalance Problems in Object Detection: A Review Parallel feature pyramid network for object detection,

Reference 77

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source=pdf_text observed=2026-08-14T06:04:41.302349Z digest=sha256:2354bd9e8dba6613f78bb9c1963518c3b89b92772bec785d0e39e7dcca74e770

Observation b7988a78-babd-47ff-9e18-e645c04f0057 · outbound

This paper cites Deep feature pyramid reconfiguration for object detection,.

Imbalance Problems in Object Detection: A Review Deep feature pyramid reconfiguration for object detection,

Reference 78

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source=pdf_text observed=2026-08-14T06:04:41.305202Z digest=sha256:8dd5281d6ad94ffa84788be473771617ba89c596f9afdb3103d5fff7f36097a2

Observation ea440d94-ff54-45a5-a607-3dc89ec04608 · outbound

This paper cites Zoom out-and-in network with map attention decision for region proposal and object detection,.

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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source=pdf_text observed=2026-08-14T06:04:41.308153Z digest=sha256:2e9a5f9dc90d8577455ca0964cea393cb944d32e112ff8403af851701b42371b

Observation 6b60cf39-9eac-40c7-a71d-303769aaffa6 · outbound

This paper cites M2det: A single-shot object detector based on multi-level feature pyramid network,.

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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source=pdf_text observed=2026-08-14T06:04:41.311249Z digest=sha256:f1de1524564e1f3a69974128b4404300d77ebd52c6df38078134120134e7b327

Observation 95d66d8c-328c-4dec-aadc-1a1d683c291a · outbound

This paper cites NAS-FPN: learning scalable feature pyramid architecture for object detection,.

Imbalance Problems in Object Detection: A Review NAS-FPN: learning scalable feature pyramid architecture for object detection,

Reference 81

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source=pdf_text observed=2026-08-14T06:04:41.314206Z digest=sha256:a07feeb409390385d9b2c05c66e99c7ec6655dbeee8ac68fa380e6d4d8b3b376

Observation 5d7c5c66-cb09-4fdb-9b32-68f5fecee50a · outbound

This paper cites Auto-fpn: Automatic network architecture adaptation for object detection beyond classification,.

Imbalance Problems in Object Detection: A Review Auto-fpn: Automatic network architecture adaptation for object detection beyond classification,

Reference 82

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source=pdf_text observed=2026-08-14T06:04:41.317572Z digest=sha256:e4b7de6c9e943258c22247efcf74b02aad28cc8ea0dd82e1f528bbb1412f7a4a

Observation b07c6287-cebd-49a0-b92a-48f1620fb5a6 · outbound

This paper cites Unitbox: An advanced object detection network,.

Imbalance Problems in Object Detection: A Review Unitbox: An advanced object detection network,

Reference 83

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source=pdf_text observed=2026-08-14T06:04:41.321156Z digest=sha256:420107071987e9e9c413bdc3b5a0dc6450d640c7fb09db0a5c95a430abaed201

Observation 74e30a1c-50ec-4334-983f-6cfa5bc8c6c7 · outbound

This paper cites Improving object local- ization with fitness nms and bounded iou loss,.

Imbalance Problems in Object Detection: A Review Improving object local- ization with fitness nms and bounded iou loss,

Reference 84

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source=pdf_text observed=2026-08-14T06:04:41.324633Z digest=sha256:03c94909ab685b94fbe170efd5eb696fd48fb93707bda6e75fa30c95da63cb9c

Observation b8806803-03dd-450e-811d-b53e9ac2301a · outbound

This paper cites Generalized intersection over union: A metric and a loss for bounding box regression,.

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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source=pdf_text observed=2026-08-14T06:04:41.328004Z digest=sha256:be93ed95b4825932f05e5324749c20861eeed1a378894b06cd17ede3cc37cdf4

Observation 0efba9f4-e613-419f-ab6a-de54983fdba7 · outbound

This paper cites Distance-iou loss: Faster and better learning for bounding box regression,.

Imbalance Problems in Object Detection: A Review Distance-iou loss: Faster and better learning for bounding box regression,

Reference 86

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source=pdf_text observed=2026-08-14T06:04:41.331494Z digest=sha256:2f9aff584a06ea45d9ad40d94bea08b50ae26342e1a6a5fa43f2410399c354de

Observation f1fd0056-7f17-4cad-befa-d4db11585174 · outbound

This paper cites Cascade R-CNN: Delving into high quality object detection,.

Imbalance Problems in Object Detection: A Review Cascade R-CNN: Delving into high quality object detection,

Reference 87

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source=pdf_text observed=2026-08-14T06:04:41.334965Z digest=sha256:f98a0ed683286cc57d61dcaafb8fdff17bc30ae7159a5719639708ad5543b37f

Observation 31896a36-9727-4489-9a04-2acccca60faf · outbound

This paper cites Hierarchical shot detector,.

Imbalance Problems in Object Detection: A Review Hierarchical shot detector,

Reference 88

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source=pdf_text observed=2026-08-14T06:04:41.338960Z digest=sha256:d49acc1bd8481523545630b02f3fde182df27e706bf9293a6c82b5dcf66805ca

Observation 0139498b-51a2-4252-8fe7-e5180a37d3c0 · outbound

This paper cites IoU-uniform R-CNN: Breaking Through the Limitations of RPN.

Imbalance Problems in Object Detection: A Review IoU-uniform R-CNN: Breaking Through the Limitations of RPN

Reference 89

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verified exact
local_arxiv, observed 2026-08-14T06:04:41.823812Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-14T06:04:41.342418Z digest=sha256:90323295af5ba2c05645878a16323e04ef821b7bdbe340606995dce91a28e518

Observation 77bf2ce4-d65c-43dd-9d7a-acfb02a12d84 · outbound

This paper cites Microsoft COCO: Common Ob- jects in Context,.

Imbalance Problems in Object Detection: A Review Microsoft COCO: Common Ob- jects in Context,

Reference 90

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source=pdf_text observed=2026-08-14T06:04:41.346621Z digest=sha256:a83205d10a8c6012a68bf1aee5b0c0056fb845e4d42e0ef6be30d0249511248e

Observation 54312ff8-2cfb-4c5c-bc24-88fc7ef02d0b · outbound

This paper cites The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale.

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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source=pdf_text observed=2026-08-14T06:04:41.350353Z digest=sha256:f47d4773f3c76b162965bca4f54f843eddca68f5f8b3db4787e0293b8aafddbe

Observation eac7803e-b535-461a-a177-ba5e80d79ae7 · outbound

This paper cites Objects365: A large-scale, high-quality dataset for object detection,.

Imbalance Problems in Object Detection: A Review Objects365: A large-scale, high-quality dataset for object detection,

Reference 92

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source=pdf_text observed=2026-08-14T06:04:41.354179Z digest=sha256:2f01836b6e332cc27dd260c74d568b5380a6f45ca4d8482075e3be4062c3186c

Observation 7d3c7e2c-f2a5-4afd-aa27-be05c4208d51 · outbound

This paper cites Deep residual learning for image recognition,.

Imbalance Problems in Object Detection: A Review Deep residual learning for image recognition,

Reference 93

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source=pdf_text observed=2026-08-14T06:04:41.357805Z digest=sha256:a7851e72e35bc3ebeafb2ba0108f00d4098a061b14d12674f9b4fad4bae4bcdf

Observation eb85623a-b7d4-4770-964b-c2c8c3fc7fac · outbound

This paper cites Example-based learning for view- based human face detection,.

Imbalance Problems in Object Detection: A Review Example-based learning for view- based human face detection,

Reference 94

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source=pdf_text observed=2026-08-14T06:04:41.361556Z digest=sha256:087d71665a8c8ba6baf21134ca62e83da0fcbbba36a7dba89c61003923e5f849

Observation 265c8c81-3f93-4dac-b938-f776780ec596 · outbound

This paper cites Rapid object detection using a boosted cascade of simple features,.

Imbalance Problems in Object Detection: A Review Rapid object detection using a boosted cascade of simple features,

Reference 95

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source=pdf_text observed=2026-08-14T06:04:41.365284Z digest=sha256:b1405aeba8acdf91907f5b266838121390436751159337888dd5ca353923c4f0

Observation 4ae7e75d-6dd3-4d65-895c-e18118ee3134 · outbound

This paper cites Histograms of oriented gradients for human detection,.

Imbalance Problems in Object Detection: A Review Histograms of oriented gradients for human detection,

Reference 96

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source=pdf_text observed=2026-08-14T06:04:41.369705Z digest=sha256:d47357b65c787dcd014ba042c8f46be62b5a30c8671a3ec0ecfb6de519cd5cb7

Observation ee1a2b6b-28a3-4422-b0b4-f46d8e56f4fa · outbound

This paper cites Training deep neural networks via direct loss minimization,.

Imbalance Problems in Object Detection: A Review Training deep neural networks via direct loss minimization,

Reference 97

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source=pdf_text observed=2026-08-14T06:04:41.373086Z digest=sha256:9a75a78541f5ed5fce3481e4c8d3b6d40bb65b08babaeb44bf7ab59888246004

Observation 892d17a8-2f9a-486c-a3b2-bca8aef4c223 · outbound

This paper cites End-to-end training of object class detectors for mean average precision,.

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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source=pdf_text observed=2026-08-14T06:04:41.376686Z digest=sha256:4740e99e1ccc630105ef6d89222330b5558a5d67139c107d443ddc1e99415836

Observation 897ccec7-4f74-4e04-a17a-8b0547831a15 · outbound

This paper cites Cut, paste and learn: Surprisingly easy synthesis for instance detection,.

Imbalance Problems in Object Detection: A Review Cut, paste and learn: Surprisingly easy synthesis for instance detection,

Reference 99

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source=pdf_text observed=2026-08-14T06:04:41.380119Z digest=sha256:16153aba745ab17486c43c175b26cfadf956faf6e2b0fcd62593fa715ff5515f

Observation adf651fa-2935-4e8e-a7e4-c2414cb9ec1f · outbound

This paper cites Modeling visual context is key to augmenting object detection datasets,.

Imbalance Problems in Object Detection: A Review Modeling visual context is key to augmenting object detection datasets,

Reference 100

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Pith citing papers

No inbound Pith citation observations are available.