Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs | Paper Citation Record

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This paper cites An evaluation of region based object detection strategies within X-ray baggage security imagery.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs An evaluation of region based object detection strategies within X-ray baggage security imagery

Reference 1

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This paper cites Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving

Reference 2

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Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Onboard Dynamic-Object Detection and Tracking for Autonomous Robot Navigation With RGB-D Camera

Reference 3

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This paper cites Reliable Vision-Based Grasping Target Recog- nition for Upper Limb Prostheses.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Reliable Vision-Based Grasping Target Recog- nition for Upper Limb Prostheses

Reference 4

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This paper cites Intuitive movement-based prosthesis control enables arm amputees to reach naturally in virtual reality.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Intuitive movement-based prosthesis control enables arm amputees to reach naturally in virtual reality

Reference 5

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This paper cites Perceptually-guided deep neural networks for ego-action prediction: Object grasp- ing.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Perceptually-guided deep neural networks for ego-action prediction: Object grasp- ing

Reference 6

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Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Unresolved cited work

Reference 7

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This paper cites DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion

Reference 8

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This paper cites DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Reference 9

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This paper cites On the Opportunities and Risks of Foundation Models.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs On the Opportunities and Risks of Foundation Models

Reference 10

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This paper cites https://github.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs https://github

Reference 11

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Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Segment Anything

Reference 12

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This paper cites An Iterative Segmentation Method Based on a Contextual Color and Shape Criterion.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs An Iterative Segmentation Method Based on a Contextual Color and Shape Criterion

Reference 13

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This paper cites Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images

Reference 14

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Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs ImageNet Classification with Deep Convolutional Neural Networks

Reference 15

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Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 16

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This paper cites Going deeper with convolutions.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Going deeper with convolutions

Reference 17

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This paper cites Invited Talk: U-Net Convolutional Networks for Biomedical Image Segmentation.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Invited Talk: U-Net Convolutional Networks for Biomedical Image Segmentation

Reference 18

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This paper cites SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Reference 19

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This paper cites Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Reference 20

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Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs ImageNet: A large-scale hierarchical image database

Reference 21

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This paper cites Microsoft COCO: Common Objects in Context.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Microsoft COCO: Common Objects in Context

Reference 22

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This paper cites Available from: https://github.com/ultralytics/ yolov5.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Available from: https://github.com/ultralytics/ yolov5

Reference 23

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This paper cites Prediction of Alzheimer’s Disease Using Adaptive Fine-Tuned Deep Resnet-50 with Attention Mechanism.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Prediction of Alzheimer’s Disease Using Adaptive Fine-Tuned Deep Resnet-50 with Attention Mechanism

Reference 24

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This paper cites Zero-Shot Object Detection by Semantics- Aware DETR with Adaptive Contrastive Loss.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Zero-Shot Object Detection by Semantics- Aware DETR with Adaptive Contrastive Loss

Reference 25

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This paper cites On the opportunities and risks of foundation models.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs On the opportunities and risks of foundation models

Reference 26

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This paper cites Foundational Models Defining a New Era in Vision: A Survey and Outlook.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Foundational Models Defining a New Era in Vision: A Survey and Outlook

Reference 27

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This paper cites Low-Resource Vision Challenges for Foundation Models.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Low-Resource Vision Challenges for Foundation Models

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Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs DINOv2: Learning Robust Visual Features without Supervision

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Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Masked-attention Mask Transformer for Universal Image Segmentation

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This paper cites Per-Pixel Classification is Not All You Need for Semantic Segmentation.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Per-Pixel Classification is Not All You Need for Semantic Segmentation

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Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Deep Residual Learning for Image Recognition

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This paper cites Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

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Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Aggregated Residual Transformations for Deep Neural Networks

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Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Visual vs internal attention mechanisms in deep neural networks for image classification and object detection

Reference 35

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This paper cites 3D-ARM-Gaze: a public dataset of 3D Arm Reaching Movements with Gaze information in virtual reality.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs 3D-ARM-Gaze: a public dataset of 3D Arm Reaching Movements with Gaze information in virtual reality

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

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Attention is All you Need

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This paper cites Splines: a perfect fit for signal and image processing.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Splines: a perfect fit for signal and image processing

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This paper cites Improving saliency models’ pre- dictions of the next fixation with humans’ intrinsic cost of gaze shifts.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Improving saliency models’ pre- dictions of the next fixation with humans’ intrinsic cost of gaze shifts

Reference 40

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This paper cites Hybrid FPGA-CPU-Based Architecture for Object Recognition in Visual Servoing of Arm Prosthesis.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Hybrid FPGA-CPU-Based Architecture for Object Recognition in Visual Servoing of Arm Prosthesis

Reference 41

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Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Density estimation for statistics and data analysis

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This paper cites Saccades and microsaccades during visual fixation, exploration, and search: Foun- dations for a common saccadic generator.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Saccades and microsaccades during visual fixation, exploration, and search: Foun- dations for a common saccadic generator

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This paper cites Accessed: 2024-07-11.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Accessed: 2024-07-11

Reference 44

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Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Accessed: 2024-07-11

Reference 45

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This paper cites Towards a guideline for evaluation metrics in medical image segmentation.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Towards a guideline for evaluation metrics in medical image segmentation

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This paper cites Eye Tracking: A Comprehensive Guide to Methods, Paradigms, and Measures.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Eye Tracking: A Comprehensive Guide to Methods, Paradigms, and Measures

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Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Eye movements: the past 25 years

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This paper cites The information capacity of the human motor system in controlling the amplitude of movement.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs The information capacity of the human motor system in controlling the amplitude of movement

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This paper cites Variability and Motor Control.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Variability and Motor Control

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cf23f417-5d50-4e5d-aac1-d7fc46e967d3 · outbound

This paper cites The Co-ordination and Regulation of Movements.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs The Co-ordination and Regulation of Movements

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:15:17.804624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5c1f7d18-f2f2-4229-815e-e487ae7cfb07 · outbound

This paper cites Chapter 4 - Psychological Foundations.

Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs Chapter 4 - Psychological Foundations

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:15:17.770582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Reference resolution

Outbound references

Pith citing papers