Search Results for author:
Found 10 papers, 2 papers with code
End-to-end Face Detection and Cast Grouping in Movies Using Erdős-Rényi Clustering
We introduce a novel verification method, rank-1 counts verification, that has this property, and use it in a link-based clustering scheme.
End-To-End Face Detection and Cast Grouping in Movies Using Erdos-Renyi Clustering
We introduce a novel verification method, rank-1 counts verification, that has this property, and use it in a link-based clustering scheme.
Unsupervised Hard Example Mining from Videos for Improved Object Detection
In this work, we show how large numbers of hard negatives can be obtained {\em automatically} by analyzing the output of a trained detector on video sequences.
Automatic adaptation of object detectors to new domains using self-training
Our results demonstrate the usefulness of incorporating hard examples obtained from tracking, the advantage of using soft-labels via distillation loss versus hard-labels, and show promising performance as a simple method for unsupervised domain adaptation of object detectors, with minimal dependence on hyper-parameters.
Spoken Moments: Learning Joint Audio-Visual Representations from Video Descriptions
With this in mind, the descriptions people generate for videos of different dynamic events can greatly improve our understanding of the key information of interest in each video.
Cross-Modal Discrete Representation Learning
Recent advances in representation learning have demonstrated an ability to represent information from different modalities such as video, text, and audio in a single high-level embedding vector.
Leveraging Temporal Context in Low Representational Power Regimes
We show that including information from the ETM during training improves action recognition and anticipation performance on various egocentric video datasets.
LangNav: Language as a Perceptual Representation for Navigation
We explore the use of language as a perceptual representation for vision-and-language navigation (VLN), with a focus on low-data settings.
Learning Human Action Recognition Representations Without Real Humans
To this end, we present, for the first time, a benchmark that leverages real-world videos with humans removed and synthetic data containing virtual humans to pre-train a model.
FT2TF: First-Person Statement Text-To-Talking Face Generation
This achievement highlights our model capability to bridge first-person statements and dynamic face generation, providing insightful guidance for future work.
