LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning | Paper Citation Record

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This paper cites Unsupervised robust domain adaptation with- out source data.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Unsupervised robust domain adaptation with- out source data

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This paper cites Com- positional mixture representations for vision and text.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Com- positional mixture representations for vision and text

Reference 2

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This paper cites learn2learn: A Library for Meta-Learning Research.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning learn2learn: A Library for Meta-Learning Research

Reference 3

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This paper cites Reed, Roei Herzig, Gal Chechik, Anna Rohrbach, Trevor Darrell, and Amir Globerson.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Reed, Roei Herzig, Gal Chechik, Anna Rohrbach, Trevor Darrell, and Amir Globerson

Reference 4

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This paper cites Is space-time attention all you need for video understanding? In ICML, pages 813–824, 2021.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Is space-time attention all you need for video understanding? In ICML, pages 813–824, 2021

Reference 5

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This paper cites Swimming pool and car detec- tion.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Swimming pool and car detec- tion

Reference 6

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This paper cites Few-shot video classification via tem- poral alignment.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Few-shot video classification via tem- poral alignment

Reference 7

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This paper cites Unsupervised learn- ing of visual features by contrasting cluster assignments.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Unsupervised learn- ing of visual features by contrasting cluster assignments

Reference 8

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This paper cites Emerg- ing properties in self-supervised vision transformers.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Emerg- ing properties in self-supervised vision transformers

Reference 9

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This paper cites Fewshotqa: A sim- ple framework for few-shot learning of question answering tasks using pre-trained text-to-text models.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Fewshotqa: A sim- ple framework for few-shot learning of question answering tasks using pre-trained text-to-text models

Reference 10

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This paper cites Meta-baseline: Exploring simple meta- learning for few-shot learning.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Meta-baseline: Exploring simple meta- learning for few-shot learning

Reference 11

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This paper cites The cityscapes dataset for semantic urban scene understanding.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning The cityscapes dataset for semantic urban scene understanding

Reference 12

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This paper cites A baseline for few-shot image clas- sification.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning A baseline for few-shot image clas- sification

Reference 13

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LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Unresolved cited work

Reference 14

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LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Unresolved cited work

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This paper cites Unsupervised domain adaptation by statistics alignment for deep sleep staging networks.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Unsupervised domain adaptation by statistics alignment for deep sleep staging networks

Reference 16

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This paper cites Model- agnostic meta-learning for fast adaptation of deep networks.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Model- agnostic meta-learning for fast adaptation of deep networks

Reference 17

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This paper cites Deep residual learning for image recognition.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Deep residual learning for image recognition

Reference 18

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This paper cites Masked autoencoders are scalable vision learners.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Masked autoencoders are scalable vision learners

Reference 19

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LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Hospedales

Reference 20

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This paper cites Model adaptation: Historical contrastive learning for unsu- pervised domain adaptation without source data.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Model adaptation: Historical contrastive learning for unsu- pervised domain adaptation without source data

Reference 21

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LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Unresolved cited work

Reference 22

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This paper cites Hydra - a framework for elegantly configur- ing complex applications.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Hydra - a framework for elegantly configur- ing complex applications

Reference 23

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This paper cites Cross-domain weakly-supervised object de- tection through progressive domain adaptation.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Cross-domain weakly-supervised object de- tection through progressive domain adaptation

Reference 24

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This paper cites Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics

Reference 25

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This paper cites Driving in the matrix: Can virtual worlds replace human- generated annotations for real world tasks? In ICRA, pages 746–753, 2017.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Driving in the matrix: Can virtual worlds replace human- generated annotations for real world tasks? In ICRA, pages 746–753, 2017

Reference 26

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This paper cites The Kinetics Human Action Video Dataset.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning The Kinetics Human Action Video Dataset

Reference 27

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

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Novel dataset for fine-grained image categorization: Stanford dogs

Reference 28

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LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning 3d object representations for fine-grained categorization

Reference 29

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LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Learning multiple layers of features from tiny images, 2009

Reference 30

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LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Poggio, and Thomas Serre

Reference 31

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This paper cites Towards a framework for privacy-preserving pedes- trian analysis.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Towards a framework for privacy-preserving pedes- trian analysis

Reference 32

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This paper cites Looking back at labels: A class based domain adaptation technique.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Looking back at labels: A class based domain adaptation technique

Reference 33

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This paper cites xView: Objects in Context in Overhead Imagery.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning xView: Objects in Context in Overhead Imagery

Reference 34

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LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Gradient-based learning applied to document recog- nition

Reference 35

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LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Meta-learning with differentiable convex op- timization

Reference 36

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This paper cites LibFewShot: A Comprehensive Library for Few-shot Learning.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning LibFewShot: A Comprehensive Library for Few-shot Learning

Reference 37

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This paper cites Meta-SGD: Learning to Learn Quickly for Few-Shot Learning.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

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This paper cites KITTI-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning KITTI-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d

Reference 39

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This paper cites A Unified Framework with Meta-dropout for Few-shot Learning.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning A Unified Framework with Meta-dropout for Few-shot Learning

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This paper cites Graph consistency based mean-teaching for unsupervised domain adaptive person re- identification.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Graph consistency based mean-teaching for unsupervised domain adaptive person re- identification

Reference 41

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This paper cites Expanding language-image pretrained models for gen- eral video recognition, 2022.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Expanding language-image pretrained models for gen- eral video recognition, 2022

Reference 42

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This paper cites Sindagi, Vibashan VS, and Vishal M.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Sindagi, Vibashan VS, and Vishal M

Reference 43

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This paper cites Multi-adversarial domain adaptation.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Multi-adversarial domain adaptation

Reference 44

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This paper cites VisDA: The Visual Domain Adaptation Challenge.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning VisDA: The Visual Domain Adaptation Challenge

Reference 45

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This paper cites Moment matching for multi-source domain adaptation.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Moment matching for multi-source domain adaptation

Reference 46

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This paper cites Adapting self-supervised vision transformers by probing attention-conditioned masking consistency.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Adapting self-supervised vision transformers by probing attention-conditioned masking consistency

Reference 47

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This paper cites Learning transferable visual models from natural language supervision.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Learning transferable visual models from natural language supervision

Reference 48

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This paper cites Tenenbaum, Hugo Larochelle, and Richard S.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Tenenbaum, Hugo Larochelle, and Richard S

Reference 49

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This paper cites Imagenet large scale visual recognition challenge.IJCV, 115:211–252,.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Imagenet large scale visual recognition challenge.IJCV, 115:211–252,

Reference 50

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This paper cites Adapting visual category models to new domains.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Adapting visual category models to new domains

Reference 51

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This paper cites Semi-supervised domain adaptation via minimax entropy.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Semi-supervised domain adaptation via minimax entropy

Reference 52

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This paper cites Seman- tic foggy scene understanding with synthetic data.IJCV, 126 (9):973–992, 2018.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Seman- tic foggy scene understanding with synthetic data.IJCV, 126 (9):973–992, 2018

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.826022Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a96502b9-6276-4589-abcf-69dbb96ffc15 · outbound

This paper cites Normalized cuts and image segmentation.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Normalized cuts and image segmentation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.810364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 28155326-92f9-49f7-9763-3cfce2b74160 · outbound

This paper cites A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:37.973086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4c331e9-4d88-4af7-b6b4-1e908e604c29 · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:37.977965Z

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Observation f14614e0-2c49-4800-a92d-808748eb2d9d · outbound

This paper cites Discriminative adversarial domain adaptation.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Discriminative adversarial domain adaptation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.795133Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2aeb9aa2-17c7-4bf4-9802-315dd6049678 · outbound

This paper cites Model adaptation through hypothesis transfer with gradual knowledge distillation.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Model adaptation through hypothesis transfer with gradual knowledge distillation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.778806Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9154219b-b57f-4976-b7d3-ae8d88b4027f · outbound

This paper cites Meta-dataset: A dataset of datasets for learning to learn from few examples.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Meta-dataset: A dataset of datasets for learning to learn from few examples

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.762945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 883a50e0-8872-4039-a355-f4a90d45b39d · outbound

This paper cites Adversarial discriminative domain adaptation.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Adversarial discriminative domain adaptation

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.740743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 94a283cb-352b-44b6-bfb8-67961c3ae268 · outbound

This paper cites an unresolved cited work.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:51:38.726522Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7def9689-94b2-46d5-8963-34e170d03c75 · outbound

This paper cites Deep hashing network for unsupervised domain adaptation.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Deep hashing network for unsupervised domain adaptation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.712045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bfb643c0-3cc0-4a04-a255-4ccdf1956519 · outbound

This paper cites Matching networks for one shot learning.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Matching networks for one shot learning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.693392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0ebfd621-a7ba-4482-a565-8c4a07803bfa · outbound

This paper cites Matching networks for one shot learning.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Matching networks for one shot learning

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.677101Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 208c57ae-6273-42b2-993a-e887636646f2 · outbound

This paper cites Visda2019 - visual domain adaptation chal- lenge.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Visda2019 - visual domain adaptation chal- lenge

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.659632Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d43de17d-2a51-4efe-943f-651663d00d99 · outbound

This paper cites Belongie.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Belongie

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.644060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3e592f8a-6956-4626-b567-b5f96dd1d066 · outbound

This paper cites Cross-domain contrastive learning for unsupervised domain adaptation.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Cross-domain contrastive learning for unsupervised domain adaptation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.627658Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2cbee99a-64a5-46ac-af68-1c908846b639 · outbound

This paper cites Cut and Learn for Unsupervised Object Detection and Instance Segmentation.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Cut and Learn for Unsupervised Object Detection and Instance Segmentation

Reference 68

Resolution: verified exact; local_arxiv, observed 2026-08-11T10:51:38.221225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6ba08902-2b3e-4cd5-a156-dcf9f9dda148 · outbound

This paper cites Msmcnet: A modular few-shot learning framework for signal modulation classification.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Msmcnet: A modular few-shot learning framework for signal modulation classification

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.612342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 79a225ef-2af5-4fcf-a205-c07f35f8cbca · outbound

This paper cites an unresolved cited work.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Unresolved cited work

Reference 70

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:51:38.598634Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 98646a51-44ba-4820-8018-e9341d1015bd · outbound

This paper cites Adap- tive adversarial network for source-free domain adaptation.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Adap- tive adversarial network for source-free domain adaptation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.583601Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 03a7c35d-95cc-4d0b-a5a6-ef7c04dfaff3 · outbound

This paper cites Source data-free domain adaptation of object detector through domain-specific perturbation.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Source data-free domain adaptation of object detector through domain-specific perturbation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.566590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 09d467e0-a495-4087-8784-ff83ac4f19c5 · outbound

This paper cites ARID: A New Dataset for Recognizing Action in the Dark.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning ARID: A New Dataset for Recognizing Action in the Dark

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:38.061939Z

Source-reported events for the cited work

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Observation c757c6b0-008c-47a7-961b-a531eb4b4a18 · outbound

This paper cites Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:38.066487Z

Source-reported events for the cited work

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Observation 796c84b0-6729-4d13-ab18-3b482b639ce2 · outbound

This paper cites Aligning correlation information for domain adaptation in action recognition.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Aligning correlation information for domain adaptation in action recognition

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.550626Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e8372c7e-787e-42db-a9d0-df49427513ba · outbound

This paper cites Transformer-Based Source-Free Domain Adaptation.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Transformer-Based Source-Free Domain Adaptation

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-11T10:51:38.077113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e9b93b0-9e9a-4771-83d6-186301a1dcf3 · outbound

This paper cites Interact before align: Leveraging cross-modal knowledge for domain adaptive action recognition.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Interact before align: Leveraging cross-modal knowledge for domain adaptive action recognition

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.534283Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ede68a2b-8344-47f8-b755-55e6bb54a938 · outbound

This paper cites Unsupervised domain adaptation for one-stage object detector using offsets to bounding box.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Unsupervised domain adaptation for one-stage object detector using offsets to bounding box

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.519470Z

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Observation ce7ad5c7-a6fe-49a2-9498-1b3170931840 · outbound

This paper cites BDD100K: A diverse driving dataset for heterogeneous multitask learning.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning BDD100K: A diverse driving dataset for heterogeneous multitask learning

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.503801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 13cc6747-7dfa-4ba1-8a7a-62f13e619a76 · outbound

This paper cites Source-free domain adaptation for real-world image dehazing.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Source-free domain adaptation for real-world image dehazing

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.485857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 92fe056c-3575-4d58-9faa-ac34fe43c1c2 · outbound

This paper cites Source-style transferred mean teacher for source-data free object detection.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Source-style transferred mean teacher for source-data free object detection

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.469595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7a447d08-bdcb-4919-bb6f-f9dacbfd2f01 · outbound

This paper cites Inpaint2learn: A self-supervised framework for affordance learning.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Inpaint2learn: A self-supervised framework for affordance learning

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.455265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 550e54d6-ba2d-4ab4-9f84-f9b73dd2ea4a · outbound

This paper cites Robust re-weighting prototypical networks for few-shot clas- sification.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning Robust re-weighting prototypical networks for few-shot clas- sification

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.440056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5b0afc00-0672-4fcd-814d-37d8825005e5 · outbound

This paper cites A closer look at few-shot video classification: A new baseline and benchmark.

LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot Learning A closer look at few-shot video classification: A new baseline and benchmark

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:51:38.423590Z

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