MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images | Paper Citation Record

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This paper cites Liu et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Liu et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Wang et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Kumar et al

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This paper cites Liu et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Liu et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Ju et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Jiang et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Zhao et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Xu et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Gong et al

Reference 9

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This paper cites Warburg et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Warburg et al

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This paper cites Mao et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Mao et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Dokuz et al

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This paper cites Liu et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Liu et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Morerio et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Binkowski et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Kong et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Wang et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Meng et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Unresolved cited work

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Unresolved cited work

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Yang et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Tang et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Tang et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Zhang et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Zhou et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Han et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Ghasedi et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Gong et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Zhang et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Li et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Lin et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Tang et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Unresolved cited work

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Gal and Z

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Teye et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Corbi `ere et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Yang et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Zhou et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Tang et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Xia et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Yan et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Shen et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Peng et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Franchi et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Won et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Yang et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Training region-based object detectors with online hard example mining

Reference 48

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Dong et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Sun et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images He et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Xu et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Zhang et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Li et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Tan et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Cui et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Hou et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Zhou et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Jiang et al

Reference 59

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Lin et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Ren et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Cui et al

Reference 62

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Huang et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Khan et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Tan et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Park et al

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Li et al

Reference 67

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MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Long-tail learning via logit adjustment

Reference 68

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This paper cites Hong et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Hong et al

Reference 69

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Observation aa795d02-6057-4371-b9be-eb74192d8722 · outbound

This paper cites Tang et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Tang et al

Reference 70

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Observation 21d80975-2553-420e-ba56-3836a5bc1c30 · outbound

This paper cites Zhang et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Zhang et al

Reference 71

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Observation cc2a01bc-4f3d-4c78-965f-d4d276b4fc72 · outbound

This paper cites Cao et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Cao et al

Reference 72

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Observation 97503ecd-7cc5-4806-9426-9fa9ddcf4f60 · outbound

This paper cites Cao et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Cao et al

Reference 73

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Observation 1acb9f3d-11d0-4592-9073-9734bc1ef2bc · outbound

This paper cites Wang et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Wang et al

Reference 74

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Observation 72955919-b765-41a1-a1bb-0ae95d3669ff · outbound

This paper cites Zhong et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Zhong et al

Reference 75

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Observation 3bf9a5dd-68f0-4bb8-8590-9d4d7b0df181 · outbound

This paper cites Zhou et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Zhou et al

Reference 76

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Observation 7b9a9e49-64f7-4cad-bdad-2ce148c88513 · outbound

This paper cites Wang et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Wang et al

Reference 77

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Observation 73efa748-2fc4-4845-9e80-37e73668dd64 · outbound

This paper cites Song et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Song et al

Reference 78

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Observation 245759d6-8c18-4f7b-9817-c845628cd570 · outbound

This paper cites Xiao et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Xiao et al

Reference 79

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Observation e88b62e1-8384-45bd-a22f-6485762af7f3 · outbound

This paper cites Chen et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Chen et al

Reference 80

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Observation cedaecda-9738-492e-99e5-105576dd7c6d · outbound

This paper cites Goldberger et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Goldberger et al

Reference 81

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Observation 1a9e8bd4-0425-4136-ab30-beb315653618 · outbound

This paper cites Han et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Han et al

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:17.880033Z

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Observation 9a43474f-d278-4917-a16c-ac98819ddda9 · outbound

This paper cites Cheng et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Cheng et al

Reference 83

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Observation 7c8cb34b-d37f-4bae-a80a-c8179a2f7497 · outbound

This paper cites Jindal et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Jindal et al

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:40.452000Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bd51fdc6-9124-4871-b472-43555f67ff0b · outbound

This paper cites Lee et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Lee et al

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:40.285878Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 841788d6-3e8d-43c7-95eb-ce7da8f3b4ad · outbound

This paper cites Zhou et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Zhou et al

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:40.142596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d14fe451-8a56-45b9-9879-6b2a0c1e3872 · outbound

This paper cites Xia et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Xia et al

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:40.005790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e3958592-fed6-455e-bbbf-122a07c890d6 · outbound

This paper cites Xie et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Xie et al

Reference 88

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0d9b7a26-961a-4cad-a143-35b0e2efb7d0 · outbound

This paper cites Gong et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Gong et al

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:39.689193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation db56af5e-132e-47e8-9d58-6497892de690 · outbound

This paper cites Fatras et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Fatras et al

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:39.497953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7504817d-c2d3-41f0-9268-59d88ecc35d9 · outbound

This paper cites Yang et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Yang et al

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:39.318587Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 530c1789-be68-4d57-9b69-7b43699b0be4 · outbound

This paper cites Tanno et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Tanno et al

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:39.131583Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 233699ac-5468-4e13-babc-ed9388f6a002 · outbound

This paper cites Menon et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Menon et al

Reference 93

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 82aa6f94-ffb0-4da0-b869-c81c7447a70b · outbound

This paper cites Xia et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Xia et al

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:38.625667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cfb5f403-cc24-43c4-a8c0-0d6f5f724222 · outbound

This paper cites Wei et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Wei et al

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:38.396279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f8396b58-c21e-4ecf-9b52-33c5dc4faf68 · outbound

This paper cites Regularizing Neural Networks by Penalizing Confident Output Distributions.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Regularizing Neural Networks by Penalizing Confident Output Distributions

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:19.089831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d2e4cff-1ce3-40d0-a9ee-7c774b59dc63 · outbound

This paper cites Lukasik et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Lukasik et al

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:37.991889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3e59d5d2-af7a-49fa-8976-a0279f06ea11 · outbound

This paper cites Wang et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Wang et al

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:37.700456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 322d618b-232b-4b5f-bb5e-aaad863478b3 · outbound

This paper cites Feng et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Feng et al

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:37.409925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4d412a62-3cff-4f5f-afe7-1465f5acbdb5 · outbound

This paper cites Liu et al.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images Liu et al

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:37.162748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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