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Discrete neural representations for explainable anomaly detection…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2022-WACV-anomaly-detection.pdf13 Mar 2023: These methods useout of distribution detection algorithms [4, 6, 21] appliedto the task of anomaly detection based on learned featurerepresentations [17, 24, 28]. ... Springer,2015. [24] M. Sabokrou, M. Fathy, M. Hoseini, and R. Klette. -
CHARLES ET AL.: STYLE2NERF FOR ONE-SHOT SEMANTIC 3D RECONSTRUCTION ...
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2022-BMVC-Style2Nerf.pdf13 Mar 2023: This has spurred a num-ber of works to look at recovering NeRFs from a single image (one-shot NeRF) [18, 24,29, 32, 35, 38]. ... 24] Norman Müller, Andrea Simonelli, Lorenzo Porzi, Samuel Rota Bulò, MatthiasNießner, and Peter Kontschieder. -
BOYNE, CHARLES, CIPOLLA: FIND - 3D MODEL OF ARTICULATED ...
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2022-BMVC-FIND-Implicit-Foot-3D-Model.pdf13 Mar 2023: References[1] Artec Leo, Artec3D. https://www.artec3d.com/. portable-3d-scanners/artec-leo,. Accessed: 2022-07-24. [2] Artec Studio. ... scanner. https://vorum.com/yeti-3d-foot-scanner/. Accessed: 2022-07-24. [5] Yuval Alaluf, Or Patashnik, and Daniel -
Multi-View Depth Estimation by FusingSingle-View Depth Probability…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2022-CVPR-multiview-depth-fusion.pdf13 Mar 2023: They use surface normal as additionalsupervisory signal [24, 30] or enforce the spatio-temporalconsistency between multiple frames [27, 29]. ... 80 0.1675 0.2970 0.3905 0.2061 76.03NAS [24] 0.0941 0.1928 0.2703 0.1269 90.09 0.1631 0.2885 0.3791 0.1997 -
DigiFace-1M: 1 Million Digital Face Images for Face Recognition
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2023-WACV-DigiFace-1M-face-recognition.pdf13 Mar 2023: Then, we can compare them against the 18 imagesselected randomly. For the randomly selected images, thestandard deviation in horizontal and vertical angles were(σhori, σvert) = (24.13. , ... 24] Qiang Meng, Shichao Zhao, Zhida Huang, and Feng -
ZHANG ET AL.: IMAGE RERANKING USING PRETRAINED VISION TRANSFORMERS ...
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2022-BMVC-Zhang-Image-Retrieval.pdf13 Mar 2023: On the one hand, recent works [24, 27] exploring feature correspondence have adopteda hybrid model with modular design. ... Given keypoint descriptors, SuperGlue [24] usesa graph neural network and attention layers to solve an assignment problem. -
Discrete neural representations for explainable anomaly detection…
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2022-WACV-anomaly-detection.pdf13 Mar 2023: These methods useout of distribution detection algorithms [4, 6, 21] appliedto the task of anomaly detection based on learned featurerepresentations [17, 24, 28]. ... Springer,2015. [24] M. Sabokrou, M. Fathy, M. Hoseini, and R. Klette. -
BAE, BUDVYTIS, CIPOLLA: IRONDEPTH 1 IronDepth: Iterative Refinement…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2022-BMVC-IronDepth-3D-reconstruction-normal.pdf13 Mar 2023: Owing to the advances in deep neural networks (DNNs), state-of-the-art ap-proaches [3, 4, 11, 12, 14, 17, 20, 21, 22, 24, 37, 38] use DNNs to extract ... of IEEE/CVFConference on Computer Vision and Pattern Recognition (CVPR), 2019. [24] Fayao Liu, -
BOYNE, CHARLES, CIPOLLA: FIND - 3D MODEL OF ARTICULATED ...
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2022-BMVC-FIND-Implicit-Foot-3D-Model.pdf13 Mar 2023: References[1] Artec Leo, Artec3D. https://www.artec3d.com/. portable-3d-scanners/artec-leo,. Accessed: 2022-07-24. [2] Artec Studio. ... scanner. https://vorum.com/yeti-3d-foot-scanner/. Accessed: 2022-07-24. [5] Yuval Alaluf, Or Patashnik, and Daniel -
Multi-View Depth Estimation by FusingSingle-View Depth Probability…
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2022-CVPR-multiview-depth-fusion.pdf13 Mar 2023: They use surface normal as additionalsupervisory signal [24, 30] or enforce the spatio-temporalconsistency between multiple frames [27, 29]. ... 80 0.1675 0.2970 0.3905 0.2061 76.03NAS [24] 0.0941 0.1928 0.2703 0.1269 90.09 0.1631 0.2885 0.3791 0.1997
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