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Found 10 papers, 1 papers with code
Moving Object Based Collision-Free Video Synopsis
Video synopsis, summarizing a video to generate a shorter video by exploiting the spatial and temporal redundancies, is important for surveillance and archiving.
Forensic Video Analytic Software
This project has resulted in three research outcomes Moving Object Based Collision Free Video Synopsis, Forensic and Surveillance Analytic Tool Architecture and Tampering Detection Inter-Frame Forgery.
Effect of Pressure for Compositionality on Language Emergence
In this paper, we present a learning environment where agents are pressured to make their emerging languages compositional by incorporating a metric of topological similarity into the loss function.
Neural Mixture Models with Expectation-Maximization for End-to-end Deep Clustering
The network parameters pose as the parameters of those distributions.
End-To-End Data-Dependent Routing in Multi-Path Neural Networks
Having multiple parallel convolutional/dense operations in each layer solves this problem, but without any context-dependent allocation of resources among these operations: the parallel computations tend to learn similar features making the widening process less effective.
Transferring Domain Knowledge with an Adviser in Continuous Tasks
Recent advances in Reinforcement Learning (RL) have surpassed human-level performance in many simulated environments.
Anomaly Detection using Deep Reconstruction and Forecasting for Autonomous Systems
In the competition dataset of camera frames consisting of both normal and anomalous cases, we achieve a test accuracy of 94% and an F1-score of 0. 95.
Feature-Dependent Cross-Connections in Multi-Path Neural Networks
As opposed to conventional network widening, multi-path architectures restrict the quadratic increment of complexity to a linear scale.
Context-Aware Multipath Networks
In view of this, networks which can allocate resources according to the context of the input and regulate flow of information across the network are effective.
Ranked #2 on
Image Classification
on Kuzushiji-MNIST
Context-Aware Automatic Occlusion Removal
Occlusion removal is an interesting application of image enhancement, for which, existing work suggests manually-annotated or domain-specific occlusion removal.
