ECCV
Collection
Accepted papers for ECCV (European Conference on Computer Vision), one dataset per year. • 4 items • Updated
paper_id uint32 | title string | authors list | ecva_url string | pdf_url string | supp_url string | doi string | arxiv_id string | arxiv_id_source string | abstract large_string | embedding list |
|---|---|---|---|---|---|---|---|---|---|---|
0 | Quaternion Equivariant Capsule Networks for 3D Point Clouds | [
"Yongheng Zhao",
"Tolga Birdal",
"Jan Eric Lenssen",
"Emanuele Menegatti",
"Leonidas Guibas",
"Federico Tombari"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/267_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460001.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460001-supp.pdf | 10.1007/978-3-030-58452-8_1 | 1912.12098 | title_snapshot | We present a 3D capsule module for processing point clouds that is equivariant to 3D rotations and translations, as well as invariant to permutations of the input points. The operator receives a sparse set of local reference frames, computed from an input point cloud and establishes end-to-end transformation equivarian... | [
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1 | DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares | [
"Yizhak Ben-Shabat",
"Stephen Gould"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/283_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460018.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460018-supp.zip | 10.1007/978-3-030-58452-8_2 | 2003.10826 | title_snapshot | We propose a surface fitting method for unstructured 3D point clouds. This method, called DeepFit, incorporates a neural network to learn point-wise weights for weighted least squares polynomial surface fitting. The learned weights act as a soft selection for the neighborhood of surface points thus avoiding the scale s... | [
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2 | NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search | [
"Zhichao Lu",
"Kalyanmoy Deb",
"Erik Goodman",
"Wolfgang Banzhaf",
"Vishnu Naresh Boddeti"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/343_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460035.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460035-supp.pdf | 10.1007/978-3-030-58452-8_3 | 2007.10396 | title_snapshot | In this paper, we propose an efficient NAS algorithm for generating task-specific models that are competitive under multiple competing objectives. It comprises of two surrogates, one at the architecture level to improve sample efficiency and one at the weights level, through a supernet, to improve gradient descent trai... | [
-0.013892171904444695,
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0.040521614253520966,
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3 | Describing Textures using Natural Language | [
"Chenyun Wu",
"Mikayla Timm",
"Subhransu Maji"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/384_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460052.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460052-supp.pdf | 10.1007/978-3-030-58452-8_4 | 2008.01180 | title_snapshot | Textures in natural images can be characterized by color, shape, periodicity of elements within them, and other attributes that can be described using natural language. In this paper, we study the problem of describing visual attributes of texture on a novel dataset containing rich descriptions of textures, and conduct... | [
0.005807932466268539,
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4 | Empowering Relational Network by Self-Attention Augmented Conditional Random Fields for Group Activity Recognition | [
"Rizard Renanda Adhi Pramono",
"Yie Tarng Chen",
"Wen Hsien Fang"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/410_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460069.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460069-supp.pdf | 10.1007/978-3-030-58452-8_5 | null | null | This paper presents a novel relational network for group activity recognition. The core of our network is to augment the conditional random fields (CRF), amenable to learning inter-dependency of correlated observations, with the newly devised temporal and spatial self-attention to learn the temporal evolution and spati... | [
0.016397925093770027,
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5 | AiR: Attention with Reasoning Capability | [
"Shi Chen",
"Ming Jiang",
"Jinhui Yang",
"Qi Zhao"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/445_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460086.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460086-supp.zip | 10.1007/978-3-030-58452-8_6 | 2007.14419 | title_snapshot | While attention has been an increasingly popular component in deep neural networks to both interpret and boost performance of models, little work has examined how attention progresses to accomplish a task and whether it is reasonable. In this work, we propose an Attention with Reasoning capability (AiR) framework that ... | [
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6 | Self6D: Self-Supervised Monocular 6D Object Pose Estimation | [
"Gu Wang",
"Fabian Manhardt",
"Jianzhun Shao",
"Xiangyang Ji",
"Nassir Navab",
"Federico Tombari"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/500_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460103.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460103-supp.zip | 10.1007/978-3-030-58452-8_7 | 2004.06468 | title_snapshot | 6D object pose estimation is a fundamental problem in computer vision. Convolutional Neural Networks (CNNs) have recently proven to be capable of predicting reliable 6D pose estimates even from monocular images. Nonetheless, CNNs are identified as being extremely data-driven, and acquiring adequate annotations is often... | [
0.0031111540738493204,
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... |
7 | Invertible Image Rescaling | [
"Mingqing Xiao",
"Shuxin Zheng",
"Chang Liu",
"Yaolong Wang",
"Di He",
"Guolin Ke",
"Jiang Bian",
"Zhouchen Lin",
"Tie-Yan Liu"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/529_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460120.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460120-supp.pdf | 10.1007/978-3-030-58452-8_8 | 2005.05650 | title_snapshot | High-resolution digital images are usually downscaled to fit various display screens or save the cost of storage and bandwidth, meanwhile the post-upscaling is adpoted to recover the original resolutions or the details in the zoom-in images. However, typical image downscaling is a non-injective mapping due to the loss ... | [
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0.0... |
8 | Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation | [
"Yingda Xia",
"Yi Zhang",
"Fengze Liu",
"Wei Shen",
"Alan L. Yuille"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/612_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460137.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460137-supp.pdf | 10.1007/978-3-030-58452-8_9 | 2003.08440 | title_snapshot | The ability to detect failures and anomalies are fundamental requirements for building reliable systems for computer vision applications, especially safety-critical applications of semantic segmentation, such as autonomous driving and medical image analysis. In this paper, we systematically study failure and anomaly de... | [
0.020696667954325676,
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0.00... |
9 | House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation | [
"Nelson Nauata",
"Kai-Hung Chang",
"Chin-Yi Cheng",
"Greg Mori",
"Yasutaka Furukawa"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/677_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460154.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460154-supp.pdf | 10.1007/978-3-030-58452-8_10 | 2003.06988 | title_snapshot | This paper proposes a novel graph-constrained generative adversarial network, whose generator and discriminator are built upon relational architecture. The main idea is to encode the constraint into the graph structure of its relational networks. We have demonstrated the proposed architecture for a new house layout gen... | [
0.027873018756508827,
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0.001577843213453889,
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0.02708231657743454,
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-0.037038836628198624,
-0.03202670067548752,
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0.00... |
10 | Crowdsampling the Plenoptic Function | [
"Zhengqi Li",
"Wenqi Xian",
"Abe Davis",
"Noah Snavely"
] | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/736_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460171.pdf | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460171-supp.zip | 10.1007/978-3-030-58452-8_11 | 2007.15194 | title_snapshot | Many popular tourist landmarks are captured in a multitude of online, public photos. These photos represent a sparse and unstructured sampling of the plenoptic function for a particular scene. In this paper,we present a new approach to novel view synthesis under time-varying illumination from such data. Our approach bu... | [
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0.... |
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