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Local-Global Context Aware Transformer for Language-Guided Video Segmentation ...
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Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learning ...
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Cumulative Effects of Physical, Chemical, and Biological Measures on Algae Growth Inhibition
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In: Water; Volume 14; Issue 6; Pages: 877 (2022)
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Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation ...
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CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes ...
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Constructing a Psychometric Testbed for Fair Natural Language Processing ...
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Juegos serios en web para la auto-protección y prevención del COVID-19: Desarrollo y pruebas de usabilidad
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In: Comunicar: Revista científica iberoamericana de comunicación y educación, ISSN 1134-3478, Nº 69, 2021 (Ejemplar dedicado a: Participación ciudadana en la esfera digital), pags. 97-111 (2021)
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ActBERT: Learning Global-Local Video-Text Representations ...
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Speech-to-Singing Conversion in an Encoder-Decoder Framework ...
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Symbiotic Attention with Privileged Information for Egocentric Action Recognition ...
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Grounded and Controllable Image Completion by Incorporating Lexical Semantics ...
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Measurement of $W^{\pm}$-boson and $Z$-boson production cross-sections in $pp$ collisions at $\sqrt{s}=2.76$ TeV with the ATLAS detector
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Baidu-UTS Submission to the EPIC-Kitchens Action Recognition Challenge 2019 ...
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Abstract:
In this report, we present the Baidu-UTS submission to the EPIC-Kitchens Action Recognition Challenge in CVPR 2019. This is the winning solution to this challenge. In this task, the goal is to predict verbs, nouns, and actions from the vocabulary for each video segment. The EPIC-Kitchens dataset contains various small objects, intense motion blur, and occlusions. It is challenging to locate and recognize the object that an actor interacts with. To address these problems, we utilize object detection features to guide the training of 3D Convolutional Neural Networks (CNN), which can significantly improve the accuracy of noun prediction. Specifically, we introduce a Gated Feature Aggregator module to learn from the clip feature and the object feature. This module can strengthen the interaction between the two kinds of activations and avoid gradient exploding. Experimental results demonstrate our approach outperforms other methods on both seen and unseen test set. ...
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Keyword:
Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1906.09383 https://arxiv.org/abs/1906.09383
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Китайско-русский параллельный дискурсивный корпус: выравнивание на уровне клаузы и статистический анализ ; Chinese-Russian Parallel Discourse Corpus: Alignment of Clauses and Statistical Analysis
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Extensive translation of circular RNAs driven by N6-methyladenosine
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Китайско-русский параллельный корпус с дискурсивно-структурной разметкой: теоретические аспекты ; Theoretical aspects of building a Chinese-Russian Parallel Corpus with discourse-structure annotation
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