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

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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

Reference 12

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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

Reference 16

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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

Reference 22

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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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This paper cites Panoptic studio: A massively multiview system for social motion capture.

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

Reference 27

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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

Reference 30

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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

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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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This paper cites Learning features combination for human action recognition from skeleton sequences.

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

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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

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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

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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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This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.

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

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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

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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

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

Reference 55

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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

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

Reference 57

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

Reference 69

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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

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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

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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

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

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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

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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

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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

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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

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