Bringing the Context Back into Object Recognition, Robustly | Paper Citation Record

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This paper cites Detecting out-of-context objects using contextual cues.

Bringing the Context Back into Object Recognition, Robustly Detecting out-of-context objects using contextual cues

Reference 1

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Observation 4c66eb94-57ad-40a3-a009-829a9d5133e7 · outbound

This paper cites Masking strategies for background bias removal in computer vision models.

Bringing the Context Back into Object Recognition, Robustly Masking strategies for background bias removal in computer vision models

Reference 2

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Observation 457dc473-8042-430d-9890-c5ad937ea33d · outbound

This paper cites Masktune: Mitigating spurious correlations by forcing to explore.

Bringing the Context Back into Object Recognition, Robustly Masktune: Mitigating spurious correlations by forcing to explore

Reference 3

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This paper cites Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models.

Bringing the Context Back into Object Recognition, Robustly Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models

Reference 4

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Observation f7746e95-65b3-4227-b0bd-50c4c25f323c · outbound

This paper cites Bird- snap: Large-scale fine-grained visual categorization of birds.

Bringing the Context Back into Object Recognition, Robustly Bird- snap: Large-scale fine-grained visual categorization of birds

Reference 5

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This paper cites Are we done with ImageNet? 2020.

Bringing the Context Back into Object Recognition, Robustly Are we done with ImageNet? 2020

Reference 6

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This paper cites Mitigating the effect of incidental cor- relations on part-based learning.

Bringing the Context Back into Object Recognition, Robustly Mitigating the effect of incidental cor- relations on part-based learning

Reference 7

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Observation 2fa2aa9e-4ead-4cc0-b867-35c2e4ab3c9f · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

Bringing the Context Back into Object Recognition, Robustly Masked-attention mask transformer for universal image segmentation

Reference 8

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This paper cites Putting the object back into video object segmentation.

Bringing the Context Back into Object Recognition, Robustly Putting the object back into video object segmentation

Reference 9

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This paper cites Invariant Causal Mechanisms through Distribution Matching.

Bringing the Context Back into Object Recognition, Robustly Invariant Causal Mechanisms through Distribution Matching

Reference 10

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This paper cites Fine-grained Visual Classification with High-temperature Refinement and Background Suppression.

Bringing the Context Back into Object Recognition, Robustly Fine-grained Visual Classification with High-temperature Refinement and Background Suppression

Reference 11

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This paper cites Imagenet: A large-scale hierarchical image database.

Bringing the Context Back into Object Recognition, Robustly Imagenet: A large-scale hierarchical image database

Reference 12

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Observation d293c5e4-64f2-4bfa-ac8e-1c8d3c2fda80 · outbound

This paper cites An empirical study of context in object detection.

Bringing the Context Back into Object Recognition, Robustly An empirical study of context in object detection

Reference 13

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Observation c26ed2b6-531b-48f3-8385-be0f3f82978b · outbound

This paper cites Network calibration by class-based temperature scaling.

Bringing the Context Back into Object Recognition, Robustly Network calibration by class-based temperature scaling

Reference 14

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This paper cites Shortcut learning in deep neural networks.

Bringing the Context Back into Object Recognition, Robustly Shortcut learning in deep neural networks

Reference 15

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This paper cites Aspire: Language-guided data augmentation for improving robustness against spurious correlations.

Bringing the Context Back into Object Recognition, Robustly Aspire: Language-guided data augmentation for improving robustness against spurious correlations

Reference 16

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This paper cites On calibration of modern neural networks.

Bringing the Context Back into Object Recognition, Robustly On calibration of modern neural networks

Reference 17

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This paper cites The two dimensions of worst-case training and their integrated effect for out-of-domain generalization.

Bringing the Context Back into Object Recognition, Robustly The two dimensions of worst-case training and their integrated effect for out-of-domain generalization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.680063Z

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This paper cites Segment anything in high qual- ity.

Bringing the Context Back into Object Recognition, Robustly Segment anything in high qual- ity

Reference 19

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This paper cites Novel dataset for fine-grained image categorization.

Bringing the Context Back into Object Recognition, Robustly Novel dataset for fine-grained image categorization

Reference 20

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This paper cites Segment any- thing.

Bringing the Context Back into Object Recognition, Robustly Segment any- thing

Reference 21

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This paper cites Flaws of ImageNet, Computer Vision's Favourite Dataset.

Bringing the Context Back into Object Recognition, Robustly Flaws of ImageNet, Computer Vision's Favourite Dataset

Reference 22

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This paper cites Out-of-distribution general- ization via risk extrapolation (rex).

Bringing the Context Back into Object Recognition, Robustly Out-of-distribution general- ization via risk extrapolation (rex)

Reference 23

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This paper cites Domain generalization with adversarial feature learning.

Bringing the Context Back into Object Recognition, Robustly Domain generalization with adversarial feature learning

Reference 24

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This paper cites Just train twice: Improving group robustness without training group information.

Bringing the Context Back into Object Recognition, Robustly Just train twice: Improving group robustness without training group information

Reference 25

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This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

Bringing the Context Back into Object Recognition, Robustly Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 26

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This paper cites A convnet for the 2020s.

Bringing the Context Back into Object Recognition, Robustly A convnet for the 2020s

Reference 27

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This paper cites Camouflaged instance seg- mentation via explicit de-camouflaging.

Bringing the Context Back into Object Recognition, Robustly Camouflaged instance seg- mentation via explicit de-camouflaging

Reference 28

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This paper cites Spawrious: A benchmark for fine control of spurious correlation biases, 2023.

Bringing the Context Back into Object Recognition, Robustly Spawrious: A benchmark for fine control of spurious correlation biases, 2023

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.602381Z

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This paper cites Simple open-vocabulary object detection.

Bringing the Context Back into Object Recognition, Robustly Simple open-vocabulary object detection

Reference 30

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This paper cites Scaling open-vocabulary object detection.

Bringing the Context Back into Object Recognition, Robustly Scaling open-vocabulary object detection

Reference 31

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This paper cites A comprehensive study of image classification model sensitivity to foregrounds, backgrounds, and visual attributes.

Bringing the Context Back into Object Recognition, Robustly A comprehensive study of image classification model sensitivity to foregrounds, backgrounds, and visual attributes

Reference 32

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This paper cites Hard ima- genet: Segmentations for objects with strong spurious cues.

Bringing the Context Back into Object Recognition, Robustly Hard ima- genet: Segmentations for objects with strong spurious cues

Reference 33

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This paper cites Obtaining well calibrated probabilities using bayesian binning.

Bringing the Context Back into Object Recognition, Robustly Obtaining well calibrated probabilities using bayesian binning

Reference 34

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This paper cites The role of context in object recognition.

Bringing the Context Back into Object Recognition, Robustly The role of context in object recognition

Reference 35

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Observation 9592cc9d-a722-4765-9a7d-72a7d6e45c56 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Bringing the Context Back into Object Recognition, Robustly DINOv2: Learning Robust Visual Features without Supervision

Reference 36

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This paper cites Jeppe- sen, Jacob Heilmann-Clausen, Thomas Læssøe, and Tobias Frøslev.

Bringing the Context Back into Object Recognition, Robustly Jeppe- sen, Jacob Heilmann-Clausen, Thomas Læssøe, and Tobias Frøslev

Reference 37

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This paper cites FungiTastic: A multi-modal dataset and benchmark for image categorization.

Bringing the Context Back into Object Recognition, Robustly FungiTastic: A multi-modal dataset and benchmark for image categorization

Reference 38

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This paper cites Animal Identification with Independent Foreground and Background Modeling.

Bringing the Context Back into Object Recognition, Robustly Animal Identification with Independent Foreground and Background Modeling

Reference 39

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This paper cites Learning transferable visual models from natural language supervi- sion.

Bringing the Context Back into Object Recognition, Robustly Learning transferable visual models from natural language supervi- sion

Reference 40

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This paper cites SAM 2: Segment Anything in Images and Videos.

Bringing the Context Back into Object Recognition, Robustly SAM 2: Segment Anything in Images and Videos

Reference 41

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This paper cites High-resolution image synthesis with latent diffusion models.

Bringing the Context Back into Object Recognition, Robustly High-resolution image synthesis with latent diffusion models

Reference 42

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This paper cites Imagenet large scale visual recognition challenge.

Bringing the Context Back into Object Recognition, Robustly Imagenet large scale visual recognition challenge

Reference 43

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This paper cites Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization.

Bringing the Context Back into Object Recognition, Robustly Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Reference 44

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This paper cites Not using the car to see the sidewalk–quantifying and controlling the effects of context in classification and segmentation.

Bringing the Context Back into Object Recognition, Robustly Not using the car to see the sidewalk–quantifying and controlling the effects of context in classification and segmentation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.501921Z

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This paper cites Gradient Matching for Domain Generalization.

Bringing the Context Back into Object Recognition, Robustly Gradient Matching for Domain Generalization

Reference 46

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This paper cites Salient imagenet: How to discover spurious features in deep learning? In International Conference on Learning Representations, 2022.

Bringing the Context Back into Object Recognition, Robustly Salient imagenet: How to discover spurious features in deep learning? In International Conference on Learning Representations, 2022

Reference 47

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This paper cites Bioclip: A vision foundation model for the tree of life.

Bringing the Context Back into Object Recognition, Robustly Bioclip: A vision foundation model for the tree of life

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.482963Z

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Observation fe7fe90a-6c9c-4181-a1f6-40f60b408750 · outbound

This paper cites Deep coral: Correlation alignment for deep domain adaptation.

Bringing the Context Back into Object Recognition, Robustly Deep coral: Correlation alignment for deep domain adaptation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.473138Z

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Observation 6861247d-7b60-45a2-a3a0-b11c49406da1 · outbound

This paper cites Imagenet-hard: The hard- est images remaining from a study of the power of zoom and spatial biases in image classification.

Bringing the Context Back into Object Recognition, Robustly Imagenet-hard: The hard- est images remaining from a study of the power of zoom and spatial biases in image classification

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.463013Z

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Observation ecb286ba-8366-47e1-b494-3ae0b7533bd0 · outbound

This paper cites Contextual priming for object detection.

Bringing the Context Back into Object Recognition, Robustly Contextual priming for object detection

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.452466Z

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Observation 0e4552e8-2c22-44ca-ba45-224bf6e3718e · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

Bringing the Context Back into Object Recognition, Robustly SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 52

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Observation a621e092-8c3c-4444-8dc2-92bbb2052d9f · outbound

This paper cites Principles of risk minimization for learn- ing theory.

Bringing the Context Back into Object Recognition, Robustly Principles of risk minimization for learn- ing theory

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.442369Z

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Observation db1f8213-2b74-44d8-86a5-eaedba5e5804 · outbound

This paper cites When does dough become a bagel? Analyzing the remaining mistakes on ImageNet.

Bringing the Context Back into Object Recognition, Robustly When does dough become a bagel? Analyzing the remaining mistakes on ImageNet

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.432628Z

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Observation 91c4ee1c-9b6f-4dba-b35d-21c115132ab7 · outbound

This paper cites CLAD: A Contrastive Learning based Approach for Background Debiasing.

Bringing the Context Back into Object Recognition, Robustly CLAD: A Contrastive Learning based Approach for Background Debiasing

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T13:48:02.872256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c2586b2-0f56-4a93-a50a-4c348b9d00d8 · outbound

This paper cites A sober look at the robustness of clips to spurious features.

Bringing the Context Back into Object Recognition, Robustly A sober look at the robustness of clips to spurious features

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.422857Z

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 0e8cc2b9-770b-44da-96b8-5fe64faa054b · outbound

This paper cites Pytorch image models.

Bringing the Context Back into Object Recognition, Robustly Pytorch image models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T13:48:02.879044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69d7bfc8-ad53-4992-ba29-f6fa4d35c02e · outbound

This paper cites Con- vnext v2: Co-designing and scaling convnets with masked autoencoders.

Bringing the Context Back into Object Recognition, Robustly Con- vnext v2: Co-designing and scaling convnets with masked autoencoders

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.408234Z

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 2b34f12e-213f-455a-8535-ca11b78112cc · outbound

This paper cites Noise or Signal: The Role of Image Backgrounds in Object Recognition.

Bringing the Context Back into Object Recognition, Robustly Noise or Signal: The Role of Image Backgrounds in Object Recognition

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T13:48:02.885740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff393fdf-e3d2-4ae0-a3b5-7857a46e45c9 · outbound

This paper cites Adversarial domain adaptation with domain mixup.

Bringing the Context Back into Object Recognition, Robustly Adversarial domain adaptation with domain mixup

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.399447Z

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 6a13cff2-357d-4ecc-a655-b3d6eff51d7c · outbound

This paper cites Significant feature suppression and cross-feature fu- sion networks for fine-grained visual classification.Scientific Reports, 14(1):24051, 2024.

Bringing the Context Back into Object Recognition, Robustly Significant feature suppression and cross-feature fu- sion networks for fine-grained visual classification.Scientific Reports, 14(1):24051, 2024

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.389512Z

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 217849be-244f-4f29-b423-bc9bc0bbdea7 · outbound

This paper cites Improving out-of-distribution robustness via selective augmentation.

Bringing the Context Back into Object Recognition, Robustly Improving out-of-distribution robustness via selective augmentation

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T13:48:02.896685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86ebc9eb-c918-44bf-993f-1cc816f34e7a · outbound

This paper cites Fast Segment Anything.

Bringing the Context Back into Object Recognition, Robustly Fast Segment Anything

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T13:48:02.900021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32e9caf6-c1d5-4b67-8246-d4072d9dadcf · outbound

This paper cites Object Recognition with and without Objects.

Bringing the Context Back into Object Recognition, Robustly Object Recognition with and without Objects

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T13:48:02.903556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b943c99e-3fa2-486b-80ac-f9cc535120c4 · outbound

This paper cites volleyball on snow.

Bringing the Context Back into Object Recognition, Robustly volleyball on snow

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.373175Z

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 f4f4d741-7ac8-4886-815a-775d4e82bc73 · outbound

This paper cites an unresolved cited work.

Bringing the Context Back into Object Recognition, Robustly Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:48:03.269314Z

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 726872f2-eb8d-460e-b213-987fbe07cdae · outbound

This paper cites an unresolved cited work.

Bringing the Context Back into Object Recognition, Robustly Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:48:03.260735Z

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 182c8e4d-bc88-4c32-981e-b7217057b392 · outbound

This paper cites an unresolved cited work.

Bringing the Context Back into Object Recognition, Robustly Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:48:03.251196Z

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 38b078c5-fc63-48f2-99d6-b91244854cf7 · outbound

This paper cites an unresolved cited work.

Bringing the Context Back into Object Recognition, Robustly Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:48:03.242295Z

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 7a796fcc-1e4f-414d-8f89-0ed1310f3f73 · outbound

This paper cites an unresolved cited work.

Bringing the Context Back into Object Recognition, Robustly Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:48:03.233220Z

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 b64028f5-c775-4044-8858-a002b20cf0c7 · outbound

This paper cites the one with confidence ˆpi = max(ˆp1, ˆp2).

Bringing the Context Back into Object Recognition, Robustly the one with confidence ˆpi = max(ˆp1, ˆp2)

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.223750Z

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 ad016dad-88ff-42e6-b7f6-ec4125d0e3fa · outbound

This paper cites choose the one with ˜pi = max(˜p1, ˜p2).

Bringing the Context Back into Object Recognition, Robustly choose the one with ˜pi = max(˜p1, ˜p2)

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.213904Z

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 3c5aa6f2-859a-419d-ac34-f371e8c416dc · outbound

This paper cites Here t >0 is a parameter maximizing the new prediction ac- curacy on the validation set.

Bringing the Context Back into Object Recognition, Robustly Here t >0 is a parameter maximizing the new prediction ac- curacy on the validation set

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.202763Z

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 85bd9e91-ecc5-4551-a32a-aed2253ad25b · outbound

This paper cites The prediction is given by argmax as usual.

Bringing the Context Back into Object Recognition, Robustly The prediction is given by argmax as usual

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.192188Z

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 4bb27e00-174c-4300-b4de-fd89aa6bba0a · outbound

This paper cites The pose of flying birds is very different from those on the ground.

Bringing the Context Back into Object Recognition, Robustly The pose of flying birds is very different from those on the ground

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.181476Z

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 473d8df3-dc4b-45ae-ac7b-23714be34137 · outbound

This paper cites , z(C) 1 , z(1) 2 ,.

Bringing the Context Back into Object Recognition, Robustly , z(C) 1 , z(1) 2 ,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:48:03.171538Z

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 7f681a5f-e023-4b4c-953b-ba4ee513726d · outbound

This paper cites background.

Bringing the Context Back into Object Recognition, Robustly background

Reference 77

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T13:48:03.162167Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers