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Hierarchical Kinematic Probability Distributions for 3D Human Shape…
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2021-ICCV-3D-human-shape-in-wild.pdf9 Apr 2022: 3] - 55.6DaNet [63] 82.4 54.8HMR (unpaired) [20] 126.3 92.0Kundu et al. ... Figure 4(a) shows. Max. inputset size. Method SSP-3DPVE-T-SC. HMR [20] 22.9GraphCMR [27] 19.5. -
Lifted Semantic Graph Embedding for Omnidirectional Place Recognition
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2021-3DV-omnidirectional-localisation.pdf9 Apr 2022: ing [5, 4]. State-of-the-art methods which finetune networkend-to-end for place recognition include NetVLAD [1] forVLAD [20] and [28] for Fisher Vector [29]. ... 4321. [20] Hervé Jégou, Matthijs Douze, Cordelia Schmid, and PatrickPérez. Aggregating -
PX-NET: Simple and Efficient Pixel-Wise Trainingof Photometric Stereo …
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2021-ICCV-PX-NET-photometric-normals.pdf9 Apr 2022: Startingfrom the basic linear light response for diffuse reflection[21, 13], more specular behaviour of reflected light havebeen proposed [31, 3, 8, 20, 38, 40]. ... Test-timeaccuracy evolution of CNN-PS [16] network when trained. in total on 20, 30, 40 -
ACCEPTED FOR PUBLICATION IN IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH,…
mi.eng.cam.ac.uk/~mjfg/ALTA/publications/IEEEACMTransASLP2022_Ragni_Confidence.pdf11 Apr 2022: P(Crefj |Ci, O) =. wrefj Crefj. P(wrefj |Ci, O)P(wrefj |Crefj , O) (20). ... Word error rates forthose languages commonly range between 20-60% [72] andnecessitate the use of error mitigation approaches, such asconfidence scores, to achieve high -
Probabilistic 3D Human Shape and Pose Estimation From Multiple…
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2021-CVPR-3D-body-shape-in-wild.pdf9 Apr 2022: 57, 36, 45, 38, 41, 50], ii) video [26, 20, 47, 40, 16] with. ... methods [20, 26, 47, 49, 40] modify single-image predictors. to take sequences of frames as inputs. -
Vision Encoders in Visual Question Answering
mi.eng.cam.ac.uk/~wjb31/Ryan_Anderson_Vision_Encoders_in_VQA.pdf10 Sep 2022: Our results show that explicit alignment enables our VLMs to achieve a significantly higherzero-shot (34.49% vs 20.89%) and best overall (40.39% vs 30.83%) VQA score on ... 4.1 Architecture. 19. 4.1.1 Frozen pretrained LM. 20. 4.1.2 Frozen pretrained -
Lifted Semantic Graph Embedding for Omnidirectional Place Recognition
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2021-3DV-omnidirectional-localisation.pdf9 Apr 2022: ing [5, 4]. State-of-the-art methods which finetune networkend-to-end for place recognition include NetVLAD [1] forVLAD [20] and [28] for Fisher Vector [29]. ... 4321. [20] Hervé Jégou, Matthijs Douze, Cordelia Schmid, and PatrickPérez. Aggregating -
Improving Attention-based Sequence-to-sequence Models
mi.eng.cam.ac.uk/~mjfg/thesis_qd212.pdf5 Jul 2022: 20. 3.1 Illustration of an encoder-decoder model without attention [168]. <BOS>and <EOS> are special tokens for the beginning and the end of thesequence. ... Equations 2.16 and 2.17 become. cl =Ll′=1. αl,l′hl′ (2.20). αl,l′ =exp(f (hl, hl′ ; -
4F12-examples-2.dvi
mi.eng.cam.ac.uk/~cipolla/lectures/4F12/Examples/4F12-examples-2.pdf17 Oct 2022: The camerais calibrated by observing the image of three markers placed 0, 20 and 30m alongthe track. -
Towards Learning Orientated Assessment for Non-native Learner Spoken…
mi.eng.cam.ac.uk/~mjfg/ALTA/presentations/ALTA_Sheffield_20190306.pdf21 Feb 2022: 400 hour BULATS training set. 20. AM LM % WER.
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