Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks | Paper Citation Record

Observation 0f9affe9-868e-4325-a73f-9c9c39fa7e4c · outbound

This paper cites Task2vec: Task embedding for meta-learning.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Task2vec: Task embedding for meta-learning

Reference 1

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This paper cites Green- house gas equivalencies calculator, Mar 2024.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Green- house gas equivalencies calculator, Mar 2024

Reference 2

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Observation f4778881-d479-492b-ac7b-febe1f1a29c7 · outbound

This paper cites Semantics- depth-symbiosis: Deeply coupled semi-supervised learning of semantics and depth.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Semantics- depth-symbiosis: Deeply coupled semi-supervised learning of semantics and depth

Reference 3

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This paper cites ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth

Reference 4

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This paper cites Vision transformer adapters for generalizable multi- task learning.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Vision transformer adapters for generalizable multi- task learning

Reference 5

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Observation 0fb12094-b83d-4bb0-9f64-8c22d8547017 · outbound

This paper cites MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation

Reference 6

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Observation 631680e5-c5ba-4f64-a85c-5f22e190fe9f · outbound

This paper cites Coco- stuff: Thing and stuff classes in context.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Coco- stuff: Thing and stuff classes in context

Reference 7

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Observation 2db10dc5-66bb-4599-a6b1-2305e62a4fb9 · outbound

This paper cites Multitask learning.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Multitask learning

Reference 8

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Observation 3a1e5236-7b74-4902-9d17-360215e3e6c7 · outbound

This paper cites Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos

Reference 9

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Observation 2bc1ae4b-c8e3-4581-884b-ea998e056a4b · outbound

This paper cites Matterport3d: Learning from rgb- d data in indoor environments.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Matterport3d: Learning from rgb- d data in indoor environments

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:12.156555Z

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Observation 5e11e0d3-695f-4f0d-9561-b739adb09a86 · outbound

This paper cites Auxiliary learning with joint task and data schedul- ing.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Auxiliary learning with joint task and data schedul- ing

Reference 11

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Observation 2bca9d05-84e9-4b3e-b900-7c4385c18676 · outbound

This paper cites Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks

Reference 12

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Observation 7752f3c0-f47a-4e25-b767-c3bedb9adeea · outbound

This paper cites MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark

Reference 13

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

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks The cityscapes dataset for semantic urban scene understanding

Reference 14

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Observation 0717d462-a5bb-4316-b3ea-953085b0f4db · outbound

This paper cites Efficient multi-task pro- gressive learning for semantic segmentation and disparity es- timation.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Efficient multi-task pro- gressive learning for semantic segmentation and disparity es- timation

Reference 15

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Observation 2e8d3a7a-aebb-4833-b546-be6dd3baaca1 · outbound

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

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Imagenet: A large-scale hierarchical image database

Reference 16

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Observation 3049c781-4371-44ba-b6e9-abbc8d747142 · outbound

This paper cites AANG: Automating Auxiliary Learning.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks AANG: Automating Auxiliary Learning

Reference 17

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Observation 6d10ec0f-f5b2-4920-b79b-ed397c4ef1e8 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 18

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This paper cites Representation similar- ity analysis for efficient task taxonomy & transfer learning.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Representation similar- ity analysis for efficient task taxonomy & transfer learning

Reference 19

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Observation a2048361-da5e-40fc-9d41-924d197b4559 · outbound

This paper cites Everingham, L.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Everingham, L

Reference 20

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Observation 21a6744c-be8c-41de-8765-abbeb6e01b3d · outbound

This paper cites Efficiently identifying task groupings for multi-task learning.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Efficiently identifying task groupings for multi-task learning

Reference 21

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Observation 3d3b955d-834e-47f4-b9bd-3b9cd2871648 · outbound

This paper cites Vision meets robotics: The kitti dataset.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Vision meets robotics: The kitti dataset

Reference 22

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Observation fece7a5c-7daa-4f91-a5f1-2fe4c4d88fbd · outbound

This paper cites Dynamic task prioritization for multitask learning.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Dynamic task prioritization for multitask learning

Reference 23

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This paper cites S 3 dmt-net: improving soft sharing based multi-task cnn using task-specific distillation and cross-task interactions.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks S 3 dmt-net: improving soft sharing based multi-task cnn using task-specific distillation and cross-task interactions

Reference 24

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Observation 04768fe5-6f59-4826-89ab-4b04adbb7ef8 · outbound

This paper cites Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics

Reference 25

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Observation 329750a8-7e9c-4e1c-a26e-f2204f8dc26c · outbound

This paper cites Juwels booster– a supercomputer for large-scale ai research.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Juwels booster– a supercomputer for large-scale ai research

Reference 26

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Observation d75e5656-105b-43f5-8cbd-968db00940c8 · outbound

This paper cites Uvim: A unified modeling approach for vision with learned guiding codes.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Uvim: A unified modeling approach for vision with learned guiding codes

Reference 27

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Observation e2cb7edd-85c7-4475-ba25-c068efb620fb · outbound

This paper cites Quantifying the Carbon Emissions of Machine Learning.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Quantifying the Carbon Emissions of Machine Learning

Reference 28

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This paper cites Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation

Reference 29

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Observation b281e777-cdb3-40dc-ac07-d032c24ae0b4 · outbound

This paper cites From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation

Reference 30

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Observation 45144340-3634-4596-bc7c-c22d19fa7b4b · outbound

This paper cites Monocular depth esti- mation using relative depth maps.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Monocular depth esti- mation using relative depth maps

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.969390Z

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Observation 8a598c78-b555-4fcc-824b-5031215403f3 · outbound

This paper cites Learning scribbles for dense depth: Weakly-supervised single underwater image depth es- timation boosted by multi-task learning.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Learning scribbles for dense depth: Weakly-supervised single underwater image depth es- timation boosted by multi-task learning

Reference 32

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Observation 56d1f68f-96b6-4f8b-8703-c0191199209f · outbound

This paper cites Learning multi- ple dense prediction tasks from partially annotated data.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Learning multi- ple dense prediction tasks from partially annotated data

Reference 33

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Observation 5ec0cb73-e31a-469b-a9a3-1cc20ad74114 · outbound

This paper cites Monocular depth estimation toolbox.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Monocular depth estimation toolbox

Reference 34

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Observation 54576035-03b9-4e02-a785-a4668535d14d · outbound

This paper cites DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation

Reference 35

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Observation 60b36644-479e-4f07-ace3-edb0edc13bdb · outbound

This paper cites BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation

Reference 36

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Observation 48731f56-8c2f-4701-b6a2-3eb9697819f3 · outbound

This paper cites Deep con- volutional neural fields for depth estimation from a single image.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Deep con- volutional neural fields for depth estimation from a single image

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.924122Z

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Observation dcd89134-f215-41b7-bc52-1d68daff6212 · outbound

This paper cites Meta-auxiliary learning for future depth pre- diction in videos.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Meta-auxiliary learning for future depth pre- diction in videos

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.912605Z

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Observation 2b90054e-1be5-4f92-aeda-ad214f0a21e0 · outbound

This paper cites End- to-end multi-task learning with attention.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks End- to-end multi-task learning with attention

Reference 39

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Observation 55602905-dc02-4205-bf2b-909e1a47c1dd · outbound

This paper cites A Survey on RGB-D Datasets.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks A Survey on RGB-D Datasets

Reference 40

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Observation 58b60318-dfbd-45f6-9f90-96d2a3ed51f8 · outbound

This paper cites The role of context for object detection and semantic segmentation in the wild.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks The role of context for object detection and semantic segmentation in the wild

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.886924Z

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Observation b2de2621-0e55-4888-b631-1d3ee29094bd · outbound

This paper cites Indoor segmentation and support inference from rgbd images.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Indoor segmentation and support inference from rgbd images

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.874887Z

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Observation d9de26e2-4f51-4131-91df-a842ccd9f184 · outbound

This paper cites Real-time joint se- mantic segmentation and depth estimation using asymmetric annotations.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Real-time joint se- mantic segmentation and depth estimation using asymmetric annotations

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.860729Z

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Observation 312a99ad-3e54-4b30-a6c7-c68f6c526b9b · outbound

This paper cites Matterport3d eula for academic use, Mar.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Matterport3d eula for academic use, Mar

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.844781Z

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Observation 81bf42c5-9e5f-4b71-888b-39418d372795 · outbound

This paper cites All in tokens: Unifying output space of visual tasks via soft token.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks All in tokens: Unifying output space of visual tasks via soft token

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.831429Z

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Observation 20baf22d-68aa-4ecd-aadf-16f92a09d60a · outbound

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

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks DINOv2: Learning Robust Visual Features without Supervision

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-10T16:50:11.167840Z

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Observation 0f934eb9-090e-47d3-bfd4-39269e421a82 · outbound

This paper cites Zero-shot task transfer.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Zero-shot task transfer

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.816417Z

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Observation 155c3fb5-2839-4949-92aa-677d75dfba4f · outbound

This paper cites Multi-task distributed learning using vision transformer with random patch permu- tation.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Multi-task distributed learning using vision transformer with random patch permu- tation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.803073Z

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Observation f42d7a1a-4385-4dfc-b35a-6bb1f3bd5257 · outbound

This paper cites Carbon Emissions and Large Neural Network Training.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Carbon Emissions and Large Neural Network Training

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T16:50:11.179063Z

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Observation 170ec25c-dff7-4a43-9fee-d12fa8359c14 · outbound

This paper cites On the uncertainty of self-supervised monocular depth estimation.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks On the uncertainty of self-supervised monocular depth estimation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.790165Z

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Observation dd0c47b4-01da-43c0-8b55-ffdd42fdcc2e · outbound

This paper cites Vi- sion transformers for dense prediction.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Vi- sion transformers for dense prediction

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.777391Z

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Observation 0943e369-5546-48f2-a0d0-04c88aea0740 · outbound

This paper cites Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-10T16:50:11.191665Z

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This paper cites Improving monocular depth esti- mation by semantic pre-training.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Improving monocular depth esti- mation by semantic pre-training

Reference 53

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This paper cites An Overview of Multi-Task Learning in Deep Neural Networks.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks An Overview of Multi-Task Learning in Deep Neural Networks

Reference 54

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Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Learning to relate depth and semantics for unsupervised domain adaptation

Reference 55

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Observation b562b0c5-7e6d-4173-ba7d-10a60037d45c · outbound

This paper cites Multi-task learning as multi-objective optimization.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Multi-task learning as multi-objective optimization

Reference 56

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Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Fully convolutional networks for semantic segmentation

Reference 57

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Observation 9d6c7cb0-4c2b-4084-8ef4-8b8de16e8be7 · outbound

This paper cites Sun rgb-d: A rgb-d scene understanding benchmark suite.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Sun rgb-d: A rgb-d scene understanding benchmark suite

Reference 58

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This paper cites Which tasks should be learned together in multi-task learning? In International Conference on Machine Learning, pages 9120–9132.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Which tasks should be learned together in multi-task learning? In International Conference on Machine Learning, pages 9120–9132

Reference 59

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Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks SwinMTL: A Shared Architecture for Simultaneous Depth Estimation and Semantic Segmentation from Monocular Camera Images

Reference 60

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Observation a886c463-a328-4be2-95e9-4a85f6f40451 · outbound

This paper cites Multi-task learning for dense prediction tasks: A survey.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Multi-task learning for dense prediction tasks: A survey

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.697627Z

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Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks DIODE: A Dense Indoor and Outdoor DEpth Dataset

Reference 62

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Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Attention is all you need

Reference 63

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This paper cites Neural taskon- omy: Inferring the similarity of task-derived representations from brain activity.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Neural taskon- omy: Inferring the similarity of task-derived representations from brain activity

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.677984Z

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This paper cites Sdc-depth: Semantic divide-and-conquer net- work for monocular depth estimation.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Sdc-depth: Semantic divide-and-conquer net- work for monocular depth estimation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.664242Z

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This paper cites Domain adaptive semantic segmentation with self-supervised depth estimation.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Domain adaptive semantic segmentation with self-supervised depth estimation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.649984Z

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This paper cites Images speak in images: A generalist painter for in-context visual learning.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Images speak in images: A generalist painter for in-context visual learning

Reference 67

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Observation e7b1eca6-ed3e-451c-86a8-9e414a2cfff7 · outbound

This paper cites Structured attention guided convolutional neu- ral fields for monocular depth estimation.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Structured attention guided convolutional neu- ral fields for monocular depth estimation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.623585Z

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This paper cites Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-10T16:50:11.260964Z

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Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Polymax: General dense prediction with mask transformer

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.609328Z

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Observation 59d566d2-3d20-41a9-8d18-5fbfe35ec373 · outbound

This paper cites Taskonomy: Disentangling task transfer learning.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Taskonomy: Disentangling task transfer learning

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.596167Z

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Observation d91fabc1-4bbb-451e-80ed-092e9458336d · outbound

This paper cites A survey on multi-task learn- ing.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks A survey on multi-task learn- ing

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.582218Z

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Observation 373d2beb-400f-4fd3-8f2f-3c92f5804b1d · outbound

This paper cites Scene parsing through ade20k dataset.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Scene parsing through ade20k dataset

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:50:11.567531Z

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Observation ba27660c-57e5-40c3-b957-8f328bda35a5 · outbound

This paper cites Upon acceptance, we are committed to publishing the code to facilitate trans- parency and enable other researchers to replicate our find- ings.

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks Upon acceptance, we are committed to publishing the code to facilitate trans- parency and enable other researchers to replicate our find- ings

Reference 2017

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