Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images | Paper Citation Record

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This paper cites The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Unresolved cited work

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

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth

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This paper cites Multimodal fu- sion via teacher-student network for indoor action recogni- tion.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Multimodal fu- sion via teacher-student network for indoor action recogni- tion

Reference 4

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This paper cites Realtime multi-person 2d pose estimation using part affinity fields.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Realtime multi-person 2d pose estimation using part affinity fields

Reference 5

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

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Emerg- ing properties in self-supervised vision transformers

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This paper cites Higherhrnet: Scale- aware representation learning for bottom-up human pose es- timation.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Higherhrnet: Scale- aware representation learning for bottom-up human pose es- timation

Reference 7

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This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

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This paper cites Keras documentation - model checkpoint.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Keras documentation - model checkpoint

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Keras documentation - early stopping

Reference 10

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This paper cites Unihcp: A unified model for human-centric perceptions.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Unihcp: A unified model for human-centric perceptions

Reference 11

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This paper cites Adam: A method for stochastic opti- mization.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Adam: A method for stochastic opti- mization

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

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Unresolved cited work

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Unresolved cited work

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This paper cites A stop list for general text.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images A stop list for general text

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This paper cites Visiongpt2.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Visiongpt2

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This paper cites Understanding the diffi- culty of training deep feedforward neural networks.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Understanding the diffi- culty of training deep feedforward neural networks

Reference 18

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This paper cites Densepose: Dense human pose estimation in the wild.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Densepose: Dense human pose estimation in the wild

Reference 19

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This paper cites Deep residual learning for image recognition.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Deep residual learning for image recognition

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Mask r-cnn

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Long short-term memory

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This paper cites A multi-instance multi-label dual learning approach for video captioning.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images A multi-instance multi-label dual learning approach for video captioning

Reference 23

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This paper cites Densecap: Fully convolutional localization networks for dense caption- ing.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Densecap: Fully convolutional localization networks for dense caption- ing

Reference 24

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Panoptic studio: A massively multiview system for social motion capture

Reference 25

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Bag of Tricks for Efficient Text Classification

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Repurpos- ing diffusion-based image generators for monocular depth estimation

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This paper cites Sapiens: Foundation for Human Vision Models.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Sapiens: Foundation for Human Vision Models

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Sapiens discrete models repository

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Sapiens: Foundation for human vision mod- els

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Segment any- thing

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Visual genome: 9 Connecting language and vision using crowdsourced dense image annotations

Reference 32

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Imagenet classification with deep convolutional neural net- works

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This paper cites Motion-x: A large- scale 3d expressive whole-body human motion dataset.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Motion-x: A large- scale 3d expressive whole-body human motion dataset

Reference 34

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Microsoft coco: Common objects in context

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Learning features combination for human action recognition from skeleton sequences

Reference 36

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Exploring the limits of weakly supervised pretraining

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Amass: Archive of motion capture as surface shapes

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Y-net: joint segmen- tation and classification for diagnosis of breast biopsy im- ages

Reference 39

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Efficient Estimation of Word Representations in Vector Space

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Distributed representations of words and phrases and their compositionality

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Stacked hour- glass networks for human pose estimation, 2016

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Derpa- nis, and Kostas Daniilidis

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Unresolved cited work

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Unidepth: Universal monocular metric depth estimation

Reference 45

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

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Learning transferable visual models from natural language supervi- sion

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

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer

Reference 47

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This paper cites You only look once: Unified, real-time object de- tection.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images You only look once: Unified, real-time object de- tection

Reference 48

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Faster r-cnn: Towards real-time object detection with region proposal networks

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Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Lcr-net: Localization-classification-regression for human pose

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This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images U- net: Convolutional networks for biomedical image segmen- tation

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This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-12T19:48:32.359234Z

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Observation fb2359f6-b880-46b6-8b69-6fc8656090fe · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T19:48:32.362357Z

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Observation aa671b21-aa1b-437b-9dde-05c9a0c9a442 · outbound

This paper cites Revisiting unreasonable effectiveness of data in deep learning era.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Revisiting unreasonable effectiveness of data in deep learning era

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T19:48:32.365988Z

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Observation 48d3e48a-0dd9-46b8-8b38-87ed0ecbc87d · outbound

This paper cites Mask-yolo.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Mask-yolo

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:48:33.346046Z

Source-reported events for the cited work

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Observation bfe92ecd-e62f-48ec-bd44-92f29778b4d2 · outbound

This paper cites Deep high-resolution representation learning for human pose es- timation.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Deep high-resolution representation learning for human pose es- timation

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T19:48:32.372784Z

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Observation d1cd060e-49eb-4ff1-bee3-d65e31d2f793 · outbound

This paper cites Going deeper with convolutions.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Going deeper with convolutions

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T19:48:32.426648Z

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Observation 59ea1e12-48d8-47aa-acf9-f7a9673e7583 · outbound

This paper cites Joint training of a convolutional network and a graphical model for human pose estimation.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Joint training of a convolutional network and a graphical model for human pose estimation

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:48:33.327248Z

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Observation 341623ba-bfa1-47d8-9676-cff011fdb21b · outbound

This paper cites Deeppose: Human pose estimation via deep neural networks.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Deeppose: Human pose estimation via deep neural networks

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:48:33.295813Z

Source-reported events for the cited work

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Observation 2026cb34-3bec-4e5f-bf61-a4792a93ede3 · outbound

This paper cites Attention is all you need.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Attention is all you need

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T19:48:32.498323Z

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Observation 3c14e94f-9f80-4137-b377-7a40cc9bf495 · outbound

This paper cites Deep high-resolution repre- sentation learning for visual recognition.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Deep high-resolution repre- sentation learning for visual recognition

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-12T19:48:32.501928Z

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Observation 7fa5486b-09f6-49d6-9255-b8ef1b6333d2 · outbound

This paper cites Y-net: a one-to-two deep learning framework for digital holographic reconstruction.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Y-net: a one-to-two deep learning framework for digital holographic reconstruction

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:48:33.170351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2293a741-20c4-470b-a5ae-451939fc08fb · outbound

This paper cites Dust3r: Geometric 3d vi- sion made easy.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Dust3r: Geometric 3d vi- sion made easy

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T19:48:32.508802Z

Source-reported events for the cited work

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Observation 1d1cda28-5ac5-4b04-8792-b9b17cead7c6 · outbound

This paper cites Detectron2.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Detectron2

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:48:33.155649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8950cc69-942a-4279-803d-5428e95bd62f · outbound

This paper cites Empirical Evaluation of Rectified Activations in Convolutional Network.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Empirical Evaluation of Rectified Activations in Convolutional Network

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T19:48:32.515471Z

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Observation 16e4fbf8-a0e3-4195-a75b-374508f50223 · outbound

This paper cites Unifying flow, stereo and depth estimation.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Unifying flow, stereo and depth estimation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:48:33.049507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 753987b0-d2fa-4ab8-aaa3-28fd3c6f4e0e · outbound

This paper cites Zoomnas: search- ing for whole-body human pose estimation in the wild.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4):5296–5313, 2022.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Zoomnas: search- ing for whole-body human pose estimation in the wild.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4):5296–5313, 2022

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:48:33.039696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a241ed22-71e7-4d35-b08b-1f6dde22ba20 · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Depth anything: Unleashing the power of large-scale unlabeled data

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:48:33.029033Z

Source-reported events for the cited work

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Observation c1354a33-3ee3-4967-b85e-0b0ca0fc6370 · outbound

This paper cites Depth Anything V2.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Depth Anything V2

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-12T19:48:32.530018Z

Source-reported events for the cited work

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Observation c34cc9b5-2ad8-4723-9b2c-823938e1acc8 · outbound

This paper cites End-to-end learning of deformable mixture of parts and deep convolutional neural networks for human pose esti- mation.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images End-to-end learning of deformable mixture of parts and deep convolutional neural networks for human pose esti- mation

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:48:33.016587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 34546536-b324-400e-a137-5f4da12cf965 · outbound

This paper cites Dptext-detr: Towards better scene text detection with dynamic points in transformer.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Dptext-detr: Towards better scene text detection with dynamic points in transformer

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:48:33.003423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fc42eb17-c79f-4c14-8074-0ec034090818 · outbound

This paper cites Differential transformer, 2024.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Differential transformer, 2024

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:48:32.990798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation daf28c49-891e-4fc3-955e-76127723f4e4 · outbound

This paper cites Metric3d: Towards zero-shot metric 3d prediction from a single image.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Metric3d: Towards zero-shot metric 3d prediction from a single image

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-12T19:48:32.699287Z

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Observation bdc3a0ef-dd0d-4440-bd1c-9e029e715f33 · outbound

This paper cites Learn- ing from multiple teacher networks.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Learn- ing from multiple teacher networks

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:48:32.971313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ce5ed699-a2cf-45ac-b305-b824d190de42 · outbound

This paper cites Lite-hrnet: A lightweight high-resolution network.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Lite-hrnet: A lightweight high-resolution network

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T19:48:32.879333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bd8d7780-775a-429b-ad41-918618667f47 · outbound

This paper cites Attention Heads of Large Language Models: A Survey.

Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images Attention Heads of Large Language Models: A Survey

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-12T19:48:32.707991Z

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