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Found 9 papers, 1 papers with code
Learning Temporal Alignment Uncertainty for Efficient Event Detection
A popular approach in this regard is to represent a sequence using a bag of words (BOW) representation due to its: (i) fixed dimensionality irrespective of the sequence length, and (ii) its ability to compactly model the statistics in the sequence.
Incremental Real-Time Multibody VSLAM with Trajectory Optimization Using Stereo Camera
Real time outdoor navigation in highly dynamic environments is an crucial problem.
Joint Max Margin and Semantic Features for Continuous Event Detection in Complex Scenes
In this paper the problem of complex event detection in the continuous domain (i. e. events with unknown starting and ending locations) is addressed.
Bayesian Conditional Generative Adverserial Networks
Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input $z$ to a sample $\mathbf{x}$ that the discriminator seeks to distinguish.
Meta Transfer Learning for Facial Emotion Recognition
The use of deep learning techniques for automatic facial expression recognition has recently attracted great interest but developed models are still unable to generalize well due to the lack of large emotion datasets for deep learning.
Gold Seeker: Information Gain from Policy Distributions for Goal-oriented Vision-and-Langauge Reasoning
For each potential action a distribution of the expected outcomes is calculated, and the value of the potential information gain assessed.
A Weakly Supervised Approach to Emotion-change Prediction and Improved Mood Inference
Whilst a majority of affective computing research focuses on inferring emotions, examining mood or understanding the \textit{mood-emotion interplay} has received significantly less attention.
Efficient Labelling of Affective Video Datasets via Few-Shot & Multi-Task Contrastive Learning
Whilst deep learning techniques have achieved excellent emotion prediction, they still require large amounts of labelled training data, which are (a) onerous and tedious to compile, and (b) prone to errors and biases.
SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation
SCONE-GAN presents an end-to-end image translation, which is shown to be effective for learning to generate realistic and diverse scenery images.
