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The Impact of Removing Head Movements on Audio-visual Speech Enhancement
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In: ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.inria.fr/hal-03551610 ; ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE Signal Processing Society, May 2022, Singapore, Singapore. pp.1-5 (2022)
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An Overview of Indian Spoken Language Recognition from Machine Learning Perspective
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In: ISSN: 2375-4699 ; EISSN: 2375-4702 ; ACM Transactions on Asian and Low-Resource Language Information Processing ; https://hal.inria.fr/hal-03616853 ; ACM Transactions on Asian and Low-Resource Language Information Processing, ACM, In press, ⟨10.1145/3523179⟩ (2022)
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BBC-Oxford British Sign Language Dataset
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In: https://hal.archives-ouvertes.fr/hal-03516444 ; 2022 (2022)
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Can machines learn to see without visual databases?
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In: https://hal.archives-ouvertes.fr/hal-03526569 ; 2022 (2022)
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Large-scale Bilingual Language-Image Contrastive Learning ...
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Bridging Video-text Retrieval with Multiple Choice Questions ...
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Abstract:
Pre-training a model to learn transferable video-text representation for retrieval has attracted a lot of attention in recent years. Previous dominant works mainly adopt two separate encoders for efficient retrieval, but ignore local associations between videos and texts. Another line of research uses a joint encoder to interact video with texts, but results in low efficiency since each text-video pair needs to be fed into the model. In this work, we enable fine-grained video-text interactions while maintaining high efficiency for retrieval via a novel pretext task, dubbed as Multiple Choice Questions (MCQ), where a parametric module BridgeFormer is trained to answer the "questions" constructed by the text features via resorting to the video features. Specifically, we exploit the rich semantics of text (i.e., nouns and verbs) to build questions, with which the video encoder can be trained to capture more regional content and temporal dynamics. In the form of questions and answers, the semantic associations ... : Accepted by CVPR 2022 ...
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Keyword:
Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2201.04850 https://dx.doi.org/10.48550/arxiv.2201.04850
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Towards a Perceptual Model for Estimating the Quality of Visual Speech ...
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An error correction scheme for improved air-tissue boundary in real-time MRI video for speech production ...
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Expression-preserving face frontalization improves visually assisted speech processing ...
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WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language ...
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Modeling Intensification for Sign Language Generation: A Computational Approach ...
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Keypoint based Sign Language Translation without Glosses ...
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A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition ...
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A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation ...
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Including Facial Expressions in Contextual Embeddings for Sign Language Generation ...
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Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production ...
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Statistical and Spatio-temporal Hand Gesture Features for Sign Language Recognition using the Leap Motion Sensor ...
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Multi-View Spatial-Temporal Network for Continuous Sign Language Recognition ...
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