Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount | Paper Citation Record

Observation 57d30c16-f79a-4081-90ec-8aa97e10b402 · outbound

This paper cites Long-tailed recognition via weight balancing.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Long-tailed recognition via weight balancing

Reference 1

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Observation 349bd434-220a-404c-9905-f0dff1fa163d · outbound

This paper cites Cleaning large-dimensional covariance matrices for correlated samples.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Cleaning large-dimensional covariance matrices for correlated samples

Reference 2

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Observation 648a80cb-874a-483a-9699-7eaac0a002ea · outbound

This paper cites Image-level or object-level? a tale of two resampling strategies for long-tailed detection.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Image-level or object-level? a tale of two resampling strategies for long-tailed detection

Reference 3

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This paper cites MMDetection: Open MMLab Detection Toolbox and Benchmark.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount MMDetection: Open MMLab Detection Toolbox and Benchmark

Reference 4

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Observation 13c2edd2-ef1e-4b28-bd4c-0799d0872b01 · outbound

This paper cites Long-tail Detection with Effective Class-Margins.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Long-tail Detection with Effective Class-Margins

Reference 5

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Observation 90a910d6-c268-4854-b38f-56d4cd6ba669 · outbound

This paper cites Separability and geometry of object manifolds in deep neural networks.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Separability and geometry of object manifolds in deep neural networks

Reference 6

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Observation ae77c63f-7a85-46c0-9503-58204f934436 · outbound

This paper cites Class-balanced loss based on effective number of samples.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Class-balanced loss based on effective number of samples

Reference 7

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Observation b1cac262-6054-49e3-be42-096e316d32d2 · outbound

This paper cites Boosting long-tailed object detection via step-wise learning on smooth-tail data.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Boosting long-tailed object detection via step-wise learning on smooth-tail data

Reference 8

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Observation bc928ffc-d86e-4980-8d21-66221445adf2 · outbound

This paper cites Everingham, S.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Everingham, S

Reference 9

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Observation 839b2177-d1a4-47e2-b2b4-e2c90ef4103f · outbound

This paper cites Exploring classification equilibrium in long-tailed object detection.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Exploring classification equilibrium in long-tailed object detection

Reference 10

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Observation 35ed245e-b2ff-47c3-8ef2-8faff2f95953 · outbound

This paper cites Simple copy-paste is a strong data augmentation method for instance segmentation.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Simple copy-paste is a strong data augmentation method for instance segmentation

Reference 11

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Observation 359a709b-6362-4435-832b-dcf68278e5a9 · outbound

This paper cites Lvis: A dataset for large vocabulary instance segmentation.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Lvis: A dataset for large vocabulary instance segmentation

Reference 12

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Observation fd33c8f5-72ab-4fa6-8adb-7042ff9441c7 · outbound

This paper cites Deep residual learning for image recognition.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Deep residual learning for image recognition

Reference 13

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Observation dde9bb07-d594-4a24-8d57-eb450651bd77 · outbound

This paper cites Droploss for long-tail instance segmentation.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Droploss for long-tail instance segmentation

Reference 14

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This paper cites A survey of deep learning-based object detection.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount A survey of deep learning-based object detection

Reference 15

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Observation ba690839-c53f-48da-a595-d1010e4d4ec2 · outbound

This paper cites Decoupling Representation and Classifier for Long-Tailed Recognition.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Decoupling Representation and Classifier for Long-Tailed Recognition

Reference 16

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Observation 8570a1ce-958e-4b3b-b6f4-991a45183e0a · outbound

This paper cites Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance

Reference 17

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Observation f1942633-86c1-49c6-8b74-6e04e166cce3 · outbound

This paper cites Equalized focal loss for dense long-tailed object detection.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Equalized focal loss for dense long-tailed object detection

Reference 18

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Observation 02c9f4a8-e0de-4130-85ec-a247f516a9b9 · outbound

This paper cites Representations and generalization in artificial and brain neural networks.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Representations and generalization in artificial and brain neural networks

Reference 19

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This paper cites Measuring the information of images (in chinese).

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Measuring the information of images (in chinese)

Reference 20

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Observation 6297d414-33a4-4cc0-80a8-0eea964aefdd · outbound

This paper cites Overcoming classifier imbalance for long-tail object detection with balanced group softmax.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Overcoming classifier imbalance for long-tail object detection with balanced group softmax

Reference 21

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Observation ac00f1d8-52ff-4909-b82b-e749833425fc · outbound

This paper cites Microsoft coco: Common objects in context.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Microsoft coco: Common objects in context

Reference 22

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Observation 613e2d0a-6caf-429b-b5dc-0c9d93cf29f0 · outbound

This paper cites Feature pyramid networks for object detection.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Feature pyramid networks for object detection

Reference 23

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Observation 892bdc61-74f3-49e7-bb6e-224cda4b03e0 · outbound

This paper cites Focal loss for dense object detection.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Focal loss for dense object detection

Reference 24

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This paper cites Deep learning for generic object detection: A survey.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Deep learning for generic object detection: A survey

Reference 25

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This paper cites Delving into semantic scale imbalance.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Delving into semantic scale imbalance

Reference 26

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Observation d23a48de-fe9e-4d20-bf8c-5a41cae98794 · outbound

This paper cites Feature distribution representation learning based on knowledge transfer for long-tailed classification.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Feature distribution representation learning based on knowledge transfer for long-tailed classification

Reference 27

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Observation 534fc117-448a-4069-afed-7d6b4e24a9a7 · outbound

This paper cites Curvature-balanced feature manifold learning for long-tailed classification.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Curvature-balanced feature manifold learning for long-tailed classification

Reference 28

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Observation efb68e23-4532-4188-a3e5-e89f5235abdf · outbound

This paper cites Geometric prior guided feature representation learning for long-tailed classification.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Geometric prior guided feature representation learning for long-tailed classification

Reference 30

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Observation 989a20b1-9fc9-4aed-947e-2f00861af96b · outbound

This paper cites Learning from rich semantics and coarse locations for long-tailed object detection.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Learning from rich semantics and coarse locations for long-tailed object detection

Reference 31

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This paper cites Imbalance problems in object detection: A review.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Imbalance problems in object detection: A review

Reference 32

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This paper cites Balanced classification: A unified framework for long-tailed object detection.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Balanced classification: A unified framework for long-tailed object detection

Reference 33

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Observation fec1f874-75bc-4462-b5e2-e7edf6d9432b · outbound

This paper cites Balanced meta-softmax for long-tailed visual recognition.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Balanced meta-softmax for long-tailed visual recognition

Reference 34

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This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Faster r-cnn: Towards real-time object detection with region proposal networks

Reference 35

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This paper cites Relay backpropagation for effective learning of deep convolutional neural networks.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Relay backpropagation for effective learning of deep convolutional neural networks

Reference 36

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This paper cites Equalization loss for long-tailed object recognition.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Equalization loss for long-tailed object recognition

Reference 37

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This paper cites Equalization loss v2: A new gradient balance approach for long-tailed object detection.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Equalization loss v2: A new gradient balance approach for long-tailed object detection

Reference 38

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Observation f1cbfcfa-8386-4bab-bc7e-407b9f75080c · outbound

This paper cites Long-tailed classification by keeping the good and removing the bad momentum causal effect.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Long-tailed classification by keeping the good and removing the bad momentum causal effect

Reference 39

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This paper cites Rethinking pascal-voc and ms-coco dataset for small object detection.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Rethinking pascal-voc and ms-coco dataset for small object detection

Reference 40

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This paper cites Seesaw loss for long-tailed instance segmentation.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Seesaw loss for long-tailed instance segmentation

Reference 41

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This paper cites The devil is in classification: A simple framework for long-tail instance segmentation.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount The devil is in classification: A simple framework for long-tail instance segmentation

Reference 42

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Observation 459d6f3e-f97e-426e-b6f7-b7d330891ec4 · outbound

This paper cites Adaptive class suppression loss for long-tail object detection.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Adaptive class suppression loss for long-tail object detection

Reference 43

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This paper cites C2am loss: Chasing a better decision boundary for long-tail object detection.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount C2am loss: Chasing a better decision boundary for long-tail object detection

Reference 44

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Observation dfdfe164-9568-4b5c-b8fe-91fcc2a1370b · outbound

This paper cites Cross-batch memory for embedding learning.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Cross-batch memory for embedding learning

Reference 45

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Observation af6e7665-b058-49c7-a2ae-a1c4a0d94c82 · outbound

This paper cites Forest r-cnn: Large-vocabulary long-tailed object detection and instance segmentation.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Forest r-cnn: Large-vocabulary long-tailed object detection and instance segmentation

Reference 46

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Observation d1a23466-8afd-4e44-b219-97e748d8ca50 · outbound

This paper cites Fasa: Feature augmentation and sampling adaptation for long-tailed instance segmentation.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Fasa: Feature augmentation and sampling adaptation for long-tailed instance segmentation

Reference 47

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Observation 32e1ef1e-b6d5-40b7-96b4-9ed9be3e757e · outbound

This paper cites Distribution alignment: A unified framework for long-tail visual recognition.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Distribution alignment: A unified framework for long-tail visual recognition

Reference 48

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Observation e2b9a4cd-749a-4fec-b8ea-9fbe4f1853c1 · outbound

This paper cites Object detection in 20 years: A survey.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Object detection in 20 years: A survey

Reference 49

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This paper cites write newline.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount write newline

Reference 50

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This paper cites @esa (Ref.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount @esa (Ref

Reference 51

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Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Unresolved cited work

Reference 52

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Observation a36c9b85-87c9-47ef-9f30-e8420c3c35c2 · outbound

This paper cites Unveiling and Mitigating Generalized Biases of DNNs through the Intrinsic Dimensions of Perceptual Manifolds.

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount Unveiling and Mitigating Generalized Biases of DNNs through the Intrinsic Dimensions of Perceptual Manifolds

Reference 53

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Pith citing papers