Search Results for author:
Found 9 papers, 6 papers with code
A Modular Approach for Multimodal Summarization of TV Shows
In this paper we address the task of summarizing television shows, which touches key areas in AI research: complex reasoning, multiple modalities, and long narratives.
Minimum Description Length Clustering to Measure Meaningful Image Complexity
We conduct experiments on seven different sets of images, which show that our method assigns the most accurate scores to all images considered.
Correcting Flaws in Common Disentanglement Metrics
Recent years have seen growing interest in learning disentangled representations, in which distinct features, such as size or shape, are represented by distinct neurons.
Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentation
We propose a method that does not require data augmentation, and that, differently from existing methods, regularizes the hard assignments.
Efficient Deep Clustering of Human Activities and How to Improve Evaluation
Progress is starting to be made in the unsupervised setting, in the form of deep HAR clustering models, which can assign labels to data without having been given any labels to train on, but there are problems with evaluating deep HAR clustering models, which makes assessing the field and devising new methods difficult.
Ranked #1 on
Human Activity Recognition
on PAMAP2
The Proof is in the Pudding: Using Automated Theorem Proving to Generate Cooking Recipes
This paper presents FASTFOOD, a rule-based Natural Language Generation Program for cooking recipes.
Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech
We then demonstrate the utility of the compiled corpora through (1) a longitudinal corpus study of the prevalence of different syntactic and semantic phenomena in the CDS, and (2) applying an existing computational model of language acquisition to the two corpora and briefly comparing the results across languages.
Selective Pseudo-label Clustering
The most accurate existing approaches combine the training of the DNN with the clustering objective, so that information from the clustering process can be used to update the DNN to produce better features for clustering.
Ranked #1 on
Image Clustering
on USPS
Knowledge Graph Extraction from Videos
Nearly all existing techniques for automated video annotation (or captioning) describe videos using natural language sentences.
