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Titlebook: Computer Vision – ACCV 2018 Workshops; 14th Asian Conferenc Gustavo Carneiro,Shaodi You Conference proceedings 2019 Springer Nature Switzer

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51#
發(fā)表于 2025-3-30 08:20:07 | 只看該作者
52#
發(fā)表于 2025-3-30 15:18:03 | 只看該作者
Learning to Clean: A GAN Perspectiveel are unpaired sets of noisy and clean images. This paper explores the use of Generative Adversarial Networks (GAN) to generate denoised versions of the noisy documents. In particular, where paired information is available, we formulate the problem as an image-to-image translation task i.e, transla
53#
發(fā)表于 2025-3-30 19:46:38 | 只看該作者
Deep Reader: Information Extraction from Document Images via Relation Extraction and Natural Languagthe entities detected by the deep vision models and the relationships between them. DeepReader has a suite of state-of-the-art vision algorithms which are applied to recognize handwritten and printed text, eliminate noisy effects, identify the type of documents and detect visual entities like tables
54#
發(fā)表于 2025-3-30 22:40:26 | 只看該作者
Anti-occlusion Light-Field Optical Flow Estimation Using Light-Field Super-Pixelsn boundary areas. Light field cameras provide hundred of views in a single shot, so the ambiguity can be better analysed using other views. In this paper, we present a novel method for anti-occlusion optical flow estimation in a dynamic light field. We first model the light field superpixel (LFSP) a
55#
發(fā)表于 2025-3-31 03:51:11 | 只看該作者
Localizing the Gaze Target of a Crowd of Peopledifficult to estimate the gaze of each person in a crowd accurately and simultaneously with existing image-based eye tracking methods, since the image resolution of each person becomes low when we capture the whole crowd with a distant camera. Therefore, we introduce a new approach for localizing th
56#
發(fā)表于 2025-3-31 05:13:07 | 只看該作者
57#
發(fā)表于 2025-3-31 12:57:32 | 只看該作者
Summarizing Videos with Attentionent soft, self-attention mechanism. Current state of the art methods leverage bi-directional recurrent networks such as BiLSTM combined with attention. These networks are complex to implement and computationally demanding compared to fully connected networks. To that end we propose a simple, self-at
58#
發(fā)表于 2025-3-31 13:25:03 | 只看該作者
59#
發(fā)表于 2025-3-31 19:21:06 | 只看該作者
60#
發(fā)表于 2025-4-1 01:06:46 | 只看該作者
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