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Affinity LCFCN: Learning to Segment Fish with Weak Supervision
IssamLaradji, AlzayatSaleh, PauRodriguez....
Published date-11/06/2020
Aquaculture industries rely on the availability of accurate fish body measurements, e.g., length, width and mass. Manual methods that rely on physical tools like rulers are time and labour intensive. …
Modular Primitives for High-Performance Differentiable Rendering
SamuliLaine, JanneHellsten, TeroKarras....
Published date-11/06/2020
We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines. Our design supports all crucial operations in a …
Learning to Orient Surfaces by Self-supervised Spherical CNNs
RiccardoSpezialetti, FedericoStella, MarlonMarcon....
Published date-11/06/2020
Defining and reliably finding a canonical orientation for 3D surfaces is key to many Computer Vision and Robotics applications. This task is commonly addressed by handcrafted algorithms exploiting geometric cues …
Massively Parallel Graph Drawing and Representation Learning
ChristianBöhm, ClaudiaPlant....
Published date-11/06/2020
GraphEmbedding, GraphRepresentationLearning, RepresentationLearning
To fully exploit the performance potential of modern multi-core processors, machine learning and data mining algorithms for big data must be parallelized in multiple ways. Today's CPUs consist of multiple …
ULSD: Unified Line Segment Detection across Pinhole, Fisheye, and Spherical Cameras
HaoLi, HuaiYu, WenYang....
Published date-11/06/2020
LineSegmentDetection
Line segment detection is essential for high-level tasks in computer vision and robotics. Currently, most stateof-the-art (SOTA) methods are dedicated to detecting straight line segments in undistorted pinhole images, thus …
Single-Node Attack for Fooling Graph Neural Networks
BenFinkelshtein, ChaimBaskin, EvgeniiZheltonozhskii....
Published date-11/06/2020
Graph neural networks (GNNs) have shown broad applicability in a variety of domains. Some of these domains, such as social networks and product recommendations, are fertile ground for malicious users …