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Found 16 papers, 5 papers with code
A Novel Approach to Balance Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes and its Implementation in BEACON
"A common decision made by people, whether healthy or with health conditions, is choosing meals like breakfast, lunch, and dinner, comprising combinations of foods for appetizer, main course, side dishes, desserts, and beverages.
PixRO: Pixel-Distributed Rotational Odometry with Gaussian Belief Propagation
Visual sensors are not only becoming better at capturing high-quality images but also they have steadily increased their capabilities in processing data on their own on-chip.
Community Detection Guarantees Using Embeddings Learned by Node2Vec
Embedding the nodes of a large network into an Euclidean space is a common objective in modern machine learning, with a variety of tools available.
Asymptotics of $\ell_2$ Regularized Network Embeddings
A common approach to solving prediction tasks on large networks, such as node classification or link prediction, begin by learning a Euclidean embedding of the nodes of the network, from which traditional machine learning methods can then be applied.
Asymptotics of Network Embeddings Learned via Subsampling
We prove, under the assumption that the graph is exchangeable, that the distribution of the learned embedding vectors asymptotically decouples.
Attention-driven Robotic Manipulation
Despite the success of reinforcement learning methods, they have yet to have their breakthrough moment when applied to a broad range of robotic manipulation tasks.
Ego-Centric Spatial Memory Networks
With our broad demonstrations, we show that ESMN represents a useful and general computation graph for embodied spatial reasoning, and the module forms a bridge between real-time mapping systems and differentiable memory architectures.
Next Waves in Veridical Network Embedding
Embedding nodes of a large network into a metric (e. g., Euclidean) space has become an area of active research in statistical machine learning, which has found applications in natural and social sciences.
NodeSLAM: Neural Object Descriptors for Multi-View Shape Reconstruction
The choice of scene representation is crucial in both the shape inference algorithms it requires and the smart applications it enables.
Comparing View-Based and Map-Based Semantic Labelling in Real-Time SLAM
Generally capable Spatial AI systems must build persistent scene representations where geometric models are combined with meaningful semantic labels.
Event-based Vision: A Survey
Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur.
MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM
It can provide robust camera tracking in dynamic environments and at the same time, continuously estimate geometric, semantic, and motion properties for arbitrary objects in the scene.
Semantic Texture for Robust Dense Tracking
We argue that robust dense SLAM systems can make valuable use of the layers of features coming from a standard CNN as a pyramid of `semantic texture' which is suitable for dense alignment while being much more robust to nuisance factors such as lighting than raw RGB values.
SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks
This not only produces a useful semantic 3D map, but we also show on the NYUv2 dataset that fusing multiple predictions leads to an improvement even in the 2D semantic labelling over baseline single frame predictions.
gvnn: Neural Network Library for Geometric Computer Vision
We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning.
Place Recognition with Event-based Cameras and a Neural Implementation of SeqSLAM
Event-based cameras offer much potential to the fields of robotics and computer vision, in part due to their large dynamic range and extremely high "frame rates".
