Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition | Paper Citation Record

Observation 7833460c-58a6-44bc-9cb7-df0d5798617a · outbound

This paper cites On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning

Reference 1

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Observation 673b826f-cab2-4096-b2fc-6d2cd10f4d62 · outbound

This paper cites Eme: Energy-based multiexpert model for long-tailed remote sensing image classification.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Eme: Energy-based multiexpert model for long-tailed remote sensing image classification

Reference 2

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Observation 38718ec9-d871-4277-a3fd-060f5a878db8 · outbound

This paper cites Ace: Ally complementary experts for solving long-tailed recognition in one-shot.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Ace: Ally complementary experts for solving long-tailed recognition in one-shot

Reference 3

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Observation 75646d4d-58c4-435c-80f1-84adc12c406c · outbound

This paper cites Learning imbalanced datasets with label-distribution-aware margin loss.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Learning imbalanced datasets with label-distribution-aware margin loss

Reference 4

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This paper cites Area: adaptive reweighting via effective area for long-tailed classi- fication.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Area: adaptive reweighting via effective area for long-tailed classi- fication

Reference 5

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Observation 3a0d3707-a8e7-4bc7-938c-843cec54bdba · outbound

This paper cites Remix: rebalanced mixup.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Remix: rebalanced mixup

Reference 6

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Observation 0c47e901-0638-4c9b-9cb4-1df3057bcb0a · outbound

This paper cites Reslt: Resid- ual learning for long-tailed recognition.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Reslt: Resid- ual learning for long-tailed recognition

Reference 7

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Observation 54ae1b96-d331-4271-aaf2-f384f795555d · outbound

This paper cites Parametric con- trastive learning.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Parametric con- trastive learning

Reference 8

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Observation f8d4f800-27cb-4de3-bd12-ebf2b22f4574 · outbound

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

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Class- balanced loss based on effective number of samples

Reference 9

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Observation 67faef51-9a47-4ac7-93e6-ae24999c2895 · outbound

This paper cites Global and local mixture consistency cumulative learning for long-tailed visual recogni- tions.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Global and local mixture consistency cumulative learning for long-tailed visual recogni- tions

Reference 10

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Observation 0955d25f-1ec5-477c-b96e-1ac0f71f8a94 · outbound

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

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Exploring classification equilib- rium in long-tailed object detection

Reference 11

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Observation 89489d6e-625f-4480-a2af-992b40375991 · outbound

This paper cites Shrec’22 track: Open-set 3d object retrieval.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Shrec’22 track: Open-set 3d object retrieval

Reference 12

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Observation fa9ffaeb-a955-496b-8662-e902e9e7b1fd · outbound

This paper cites Dynamic mixup for multi-label long-tailed food ingredient recognition.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Dynamic mixup for multi-label long-tailed food ingredient recognition

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:17.773438Z

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Observation 271fe953-a030-4cf1-a426-fba4e8775cca · outbound

This paper cites Long-tailed out-of-distribution detection: Prioritizing attention to tail.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Long-tailed out-of-distribution detection: Prioritizing attention to tail

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:17.740808Z

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Observation 32749d87-506e-4b46-880f-72fe1afaa723 · outbound

This paper cites Disentangling label distribution for long-tailed visual recognition.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Disentangling label distribution for long-tailed visual recognition

Reference 15

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Observation fc14c8e3-b211-4f16-b96b-66e99660a030 · outbound

This paper cites Recon- boost: Boosting can achieve modality reconcilement.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Recon- boost: Boosting can achieve modality reconcilement

Reference 16

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Observation 9bc0a84d-1652-4a3b-b235-85d8114d4e7f · outbound

This paper cites Openworldauc: Towards unified evaluation and optimization for open-world prompt tuning.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Openworldauc: Towards unified evaluation and optimization for open-world prompt tuning

Reference 17

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Observation a4698c7f-8570-4903-a915-91df296d7856 · outbound

This paper cites Hierarchical set-to-set represen- tation for 3-d cross-modal retrieval.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Hierarchical set-to-set represen- tation for 3-d cross-modal retrieval

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:17.606018Z

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Observation 18a6be70-ef86-4fdd-9a50-65851b85df8c · outbound

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

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Decoupling Representation and Classifier for Long-Tailed Recognition

Reference 19

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Observation a7b19d04-c5dd-499a-b6a0-83d2f95f4e36 · outbound

This paper cites Learning multiple layers of features from tiny images.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Learning multiple layers of features from tiny images

Reference 20

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Observation b0c386a1-6ec1-4536-85db-5607fda8abe3 · outbound

This paper cites Hybrid Generative Fusion for Efficient and Privacy-Preserving Face Recognition Dataset Generation.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Hybrid Generative Fusion for Efficient and Privacy-Preserving Face Recognition Dataset Generation

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:42:16.485500Z

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Observation e7de7799-de68-4d03-824b-547ef096d0f2 · outbound

This paper cites One image is worth a thousand words: A usability preservable text-image collaborative erasing framework.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition One image is worth a thousand words: A usability preservable text-image collaborative erasing framework

Reference 22

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Observation d2cfc05d-e1bf-476d-9d00-eadadf50ab5f · outbound

This paper cites Size-invariance matters: Rethinking metrics and losses for imbalanced multi-object salient object detection.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Size-invariance matters: Rethinking metrics and losses for imbalanced multi-object salient object detection

Reference 23

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Observation 62d4212f-d908-4901-8b28-982a0624c8f0 · outbound

This paper cites Metasaug: Meta semantic augmentation for long-tailed visual recognition.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Metasaug: Meta semantic augmentation for long-tailed visual recognition

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:17.491640Z

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Observation 3970c031-3d5e-4c52-9f14-179b0b88143f · outbound

This paper cites Focal loss for dense object detection.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Focal loss for dense object detection

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:17.465142Z

Source-reported events for the cited work

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Observation 4c14c6cc-cd78-4f0a-b408-bda65b7766b8 · outbound

This paper cites Large-scale long-tailed recognition in an open world.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Large-scale long-tailed recognition in an open world

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:17.429762Z

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Observation 3dfb2edc-4e26-44db-9832-2a8aec2ebb17 · outbound

This paper cites Out-of- distribution detection in long-tailed recognition with calibrated outlier class learn- ing.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Out-of- distribution detection in long-tailed recognition with calibrated outlier class learn- ing

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:17.405633Z

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Observation 8be0ff34-1753-47db-9705-4e825f166380 · outbound

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

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Balanced meta- softmax for long-tailed visual recognition

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:17.382408Z

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Observation 27e4ce2e-8a2d-4c9c-a346-31b20e2bebae · outbound

This paper cites Mol: Joint estimation of micro-expression, optical flow, and land- mark via transformer-graph-style convolution.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Mol: Joint estimation of micro-expression, optical flow, and land- mark via transformer-graph-style convolution

Reference 29

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Observation 014e8d9e-57fe-4a25-aff4-4407d326d071 · outbound

This paper cites Identity-invariant representation and transformer-style relation for micro- expression recognition.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Identity-invariant representation and transformer-style relation for micro- expression recognition

Reference 30

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Observation 634f9fb1-2b82-4370-862c-5b4577ce8be2 · outbound

This paper cites Joint facial action unit recognition and self-supervised optical flow estimation.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Joint facial action unit recognition and self-supervised optical flow estimation

Reference 31

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Observation 2527fd44-f1a5-433f-9dc9-c45ac70e412e · outbound

This paper cites Difficulty-net: Learning to predict difficulty for long-tailed recognition.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Difficulty-net: Learning to predict difficulty for long-tailed recognition

Reference 32

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Observation e86659e1-573e-4bf2-8ed8-da0c1a6196ef · outbound

This paper cites Class-wise difficulty- balanced loss for solving class-imbalance.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Class-wise difficulty- balanced loss for solving class-imbalance

Reference 33

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Observation 04913215-7a92-4b1a-a0b8-602ebed671c0 · outbound

This paper cites Difficulty-aware balancing margin loss for long-tailed recognition.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Difficulty-aware balancing margin loss for long-tailed recognition

Reference 34

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Observation 8b6567d3-342b-43c7-8c51-531fd943c715 · outbound

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

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Equalization loss v2: A new gradient balance approach for long-tailed object detection

Reference 35

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Observation 3063e731-2ac5-499c-b952-0d5c56e437bf · outbound

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

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Equalization loss for long-tailed object recognition

Reference 36

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Observation 24cc6985-184e-4350-a678-294c98469d40 · outbound

This paper cites Partial and asymmetric contrastive learning for out-of- distribution detection in long-tailed recognition.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Partial and asymmetric contrastive learning for out-of- distribution detection in long-tailed recognition

Reference 37

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Observation 1779f0c3-bd40-4fc5-8cbc-07c89dcbb2d9 · outbound

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

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Seesaw loss for long-tailed instance segmentation

Reference 38

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Observation 46f1d17c-beb5-4726-a90a-432511bcdbe5 · outbound

This paper cites Contrastive learn- ing based hybrid networks for long-tailed image classification.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Contrastive learn- ing based hybrid networks for long-tailed image classification

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:16.929258Z

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Observation d91a60fb-c06b-466a-86d3-52e057099d06 · outbound

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

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition The devil is in classification: A simple framework for long-tail instance segmentation

Reference 40

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Observation ee30930c-2693-42d0-b7db-8ecb0c2ac013 · outbound

This paper cites Long-tailed Recognition by Routing Diverse Distribution-Aware Experts.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Long-tailed Recognition by Routing Diverse Distribution-Aware Experts

Reference 41

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Observation 3bbd1cb9-519e-43b2-9e46-ab95aa452ac0 · outbound

This paper cites Eat: Towards long-tailed out- of-distribution detection.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Eat: Towards long-tailed out- of-distribution detection

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:16.887498Z

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Observation 416348d1-d97a-4119-94f2-8842db353c89 · outbound

This paper cites Adversarial robust- ness under long-tailed distribution.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Adversarial robust- ness under long-tailed distribution

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:16.859266Z

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Observation 18d779bb-c42e-4c65-b4f4-4c49041c9588 · outbound

This paper cites Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:16.839468Z

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Observation a24a2698-1597-4eb4-9d14-47f6e0e91431 · outbound

This paper cites A re-balancing strategy for class-imbalanced classification based on instance diffi- culty.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition A re-balancing strategy for class-imbalanced classification based on instance diffi- culty

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:16.817812Z

Source-reported events for the cited work

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Observation bdc3b58e-c460-43f5-a0ff-10e9a1b9a884 · outbound

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

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Fasa: Feature augmentation and sampling adaptation for long-tailed instance segmentation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:16.792081Z

Source-reported events for the cited work

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Observation a17522c3-eb3b-4ef7-bf3f-ca7cf6c2174a · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition mixup: Beyond Empirical Risk Minimization

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-05T15:42:16.291066Z

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Observation 29246083-46e0-48d0-b9cb-cc462bb9b3cb · outbound

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

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Distribution alignment: A unified framework for long-tail visual recognition

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:16.768209Z

Source-reported events for the cited work

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Observation 5fe62a2f-a607-4434-b5e5-f898fa923579 · outbound

This paper cites Self-supervised ag- gregation of diverse experts for test-agnostic long-tailed recognition.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Self-supervised ag- gregation of diverse experts for test-agnostic long-tailed recognition

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:16.737777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2edd793c-f014-4e7f-94aa-cfe11c72686c · outbound

This paper cites Ltgc: Long- tail recognition via leveraging llms-driven generated content.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Ltgc: Long- tail recognition via leveraging llms-driven generated content

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:16.705522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 119365ef-dc81-4a32-ac9f-68c210203ee8 · outbound

This paper cites Ltrl: Boosting long-tail recognition via reflective learning.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Ltrl: Boosting long-tail recognition via reflective learning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:16.680803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f144430e-6f20-42f9-a813-8360def6e591 · outbound

This paper cites Mdcs: More diverse experts with consistency self-distillation for long-tailed recognition.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Mdcs: More diverse experts with consistency self-distillation for long-tailed recognition

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:16.656877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9352fb1d-bc8c-4b80-b4cb-9f0b117f28fb · outbound

This paper cites Improving calibration for long-tailed recognition.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Improving calibration for long-tailed recognition

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:16.635342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ba173539-3881-46f7-b25c-e3e65fe1c51d · outbound

This paper cites Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:16.599987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0134d6ce-c9b1-414f-816b-6c43f5732737 · outbound

This paper cites Balanced contrastive learning for long-tailed visual recognition.

Divide, Weight, and Route: Difficulty-Aware Optimization with Dynamic Expert Fusion for Long-tailed Recognition Balanced contrastive learning for long-tailed visual recognition

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:42:16.573945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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