Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-31T12:04:53.720631Z
Paper Citation Record · LEDGER
As of 9 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2607.28293.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-31T12:04:53.720631Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
50 of 50 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 61049a36-78ef-4403-9fe0-6b7981fff4a1 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Wearable supernumerary robotic limb system using a hybrid control approach based on motor imagery and object detection,
Reference 1
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Observation 75969e8d-7ba3-4a95-a594-7b0ed1dc86e0 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Current rates of prosthetic usage in upper-limb amputees–have innovations had an impact on device acceptance?
Reference 2
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Observation 653f4bec-7358-4698-bf0e-b1f86b0e8829 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Real-time radar-based gesture detection and recognition built in an edge-computing platform,
Reference 3
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Observation 53a6c7ca-b17c-4204-ad4f-56891d78c6f1 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras The future of bionic limbs: The untapped synergy of signal processing, control, and wireless connectivity,
Reference 4
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Observation c9024cff-6943-46f8-aab3-59eab275a24f · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras A survey of teleceptive sensing for wearable assistive robotic devices,
Reference 5
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Observation f44010d2-2924-4732-aa77-6919691cf63c · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Explorations of autonomous prosthetic grasping via proximity vision and deep learning,
Reference 6
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Observation 40d5445f-44e1-4ecf-a4c9-5ba57a62bbac · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Continuous semi-autonomous prosthesis control using a depth sensor on the hand,
Reference 7
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Observation cfda2acf-b812-4424-9349-256ff4e46896 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Hardware-aware affordance detection for application in portable embedded systems,
Reference 8
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Observation 2536c0aa-6f5c-4cb6-a2f9-4794b33579aa · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Learning affordance segmentation for real-world robotic manipulation via synthetic images,
Reference 9
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Observation e952610d-8ded-44e1-b7dd-dee59e6518fb · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations
Reference 10
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Unavailable: canonical work link unavailable.
Observation 3164627b-93dc-4cb8-9ab8-297293ef054f · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras A large scale multi-view RGBD visual affordance learning dataset
Reference 11
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Observation 3f89105c-742e-4acc-a00a-1590864373f1 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Affordance seg- mentation using tiny networks for sensing systems in wearable robotic devices,
Reference 12
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Observation f2dc1a5f-f5b9-4f5a-bd59-eb4be6a75fe7 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras A survey on rgb-d datasets,
Reference 13
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Observation df2465a9-d452-4986-92b7-e4b0c4e5c719 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Affordance segmentation using rgb-d sensors for application in portable embedded systems,
Reference 14
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Observation 4c9fb2a0-aac8-47a6-9d74-87c84511735f · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Embedded deep learning accel- erators: A survey on recent advances,
Reference 15
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Observation d95b81b6-a22c-4513-a904-1caaeef5b959 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Machine learning for microcontroller-class hardware-a review,
Reference 16
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Observation f9192972-3dd1-4311-9b29-28f05830c25a · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Affordance detection of tool parts from geometric features,
Reference 17
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Observation 2e1cc977-4641-4ab7-acf9-6e269750e624 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Detecting object affordances with convolutional neural networks,
Reference 18
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Observation 86ba1a69-8db9-4eba-82cb-7d2bda450cda · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Resource-aware object classification and segmentation for semi-autonomous grasping with prosthetic hands,
Reference 19
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Observation dc2d2dea-7902-4f2b-b2cc-b08db6739c93 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Semi-autonomous control of prosthetic hands based on multimodal sensing, human grasp demonstration and user intention,
Reference 20
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Unavailable: canonical work link unavailable.
Observation ab87b167-d949-45fe-bfa9-121e4eecbdc7 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Designing prosthetic hands with embodied intelligence: The kit prosthetic hands,
Reference 21
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Observation 2c0a1207-cdca-41bc-9a84-90f285a794b6 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Sensor fusion and computer vision for context-aware control of a multi degree-of-freedom prosthesis,
Reference 22
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Observation ad5687f4-ec03-4ca9-b138-6ea34f46a858 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Learning from 10 Demos: Generalisable and Sample-Efficient Policy Learning with Oriented Affordance Frames
Reference 23
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Observation c46e03b2-0d11-4283-8c5d-00514e7e44fb · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras A review of sensory feedback in upper- limb prostheses from the perspective of human motor control,
Reference 24
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Observation f8fd19ce-acde-41e1-8e54-3db746c7f8ac · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Toward affordance detection and ranking on novel objects for real-world robotic manipulation,
Reference 25
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Observation bc3261d5-58af-4722-8e38-fbe34de305f6 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras An affordance keypoint detection network for robot manipulation,
Reference 26
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Unavailable: canonical work link unavailable.
Observation 5f86c07b-0d67-44a5-9812-778dbc88b976 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Sgsin: Simultaneous grasp and suction inference network via attention-based affordance learning,
Reference 27
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Observation 5d607acb-af7d-43b9-bbd0-786b15b01a4e · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Visual affordance and function understanding: A survey,
Reference 28
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Observation 8f6aab5b-e2f4-4e22-a37d-d382676bd092 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Ganhand: Predicting human grasp affordances in multi-object scenes,
Reference 29
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Unavailable: canonical work link unavailable.
Observation 0115dc47-86ee-4462-99dd-9e76d28bb9a4 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Affordance Labeling and Exploration: A Manifold-Based Approach
Reference 30
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Observation 781ff80f-1b53-4783-856c-a9d2d15a7695 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras An affordance detection pipeline for resource-constrained AUTHORet al.: PREP ARA TION OF P APERS FOR IEEE TRANSACTIONS AND JOURNALS 11 devices,
Reference 31
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Observation f7066046-0aca-4ea8-9050-e975cd0260ce · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Lightweight neural networks for affordance segmentation: Enhancement of the decoder module,
Reference 32
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Observation 5c36917a-671e-4d00-bbf2-4ba74ec0895b · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras 3d affordancenet: A benchmark for visual object affordance understanding,
Reference 33
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Observation 6f4f66d0-347a-4a8c-be50-128525e8d2b3 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Are dense labels always necessary for 3d object detection from point cloud?
Reference 34
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Observation 7aadbe42-c1a8-40e6-b1c0-62fab3e6f0ad · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Dgpinet- kd: Deep guided and progressive integration network with knowledge distillation for rgb-d indoor scene analysis,
Reference 35
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Observation 281c8eaf-53eb-403a-850e-34867602de47 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras A Comprehensive Survey on Hardware-Aware Neural Architecture Search
Reference 36
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Observation 4851b7eb-d21f-4d8e-b600-7c88822fe8ef · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Automl: A systematic review on automated machine learning with neural architecture search,
Reference 37
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Observation b4a9ad73-f08a-4124-82a8-5f31664c3cb2 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Mnasnet: Platform-aware neural architecture search for mobile,
Reference 38
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Observation 6e115c23-24bd-47fb-92e5-c2ddf3c1afae · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Fast hardware- aware neural architecture search,
Reference 39
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Observation 0a2d43dc-3ee7-4939-912f-5ec41a0295d7 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras The deep learning compiler: A comprehensive survey,
Reference 40
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Observation c70ea481-2679-46f7-aaa4-58192a0f1ab7 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark
Reference 41
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Observation fc24b57f-d6ba-41de-907f-1dc0e45c3c5a · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras On- device training under 256kb memory,
Reference 42
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Observation 39dc84f9-0994-4ae8-9082-bcb0d582680b · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Micronets: Neural network architectures for deploying tinyml applications on commodity microcontrollers,
Reference 43
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Observation 58cdad47-8917-4a78-b1f4-5437a82eacf0 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras A Machine Learning-oriented Survey on Tiny Machine Learning
Reference 44
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Observation 31db7990-175c-4380-bbe7-93667d8ab982 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Single path one-shot neural architecture search with uniform sampling,
Reference 45
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Observation 34111498-0d41-4ee1-b652-a77eedbb9bc3 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Enhancing neural architecture search with multiple hardware constraints for deep learning model deployment on tiny iot devices,
Reference 46
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Observation a8f68ee2-662f-4272-8cad-009c9041db42 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Efficient evaluation methods for neural architecture search: A survey,
Reference 47
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Observation 8e9b4216-068a-4730-9568-363f4168851b · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Zero-shot neural architecture search: Challenges, solutions, and opportunities,
Reference 48
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Observation 7a359b29-2932-4a35-8bdd-c5f357d92b09 · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras Segformer: Simple and efficient design for semantic segmentation with transformers,
Reference 49
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Observation db0c681f-4fc4-4b97-9bb8-ce3622a5b53f · outbound
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras A survey of multi- fingered robotic manipulation: Biological results, structural evolvements and learning methods,
Reference 50
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No inbound Pith citation observations are available.