Abstract Machine learning has made significant advances in the field of image processing.
The foundation of this success is supervised learning, which necessitates annotated labels …

Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in
the field of computer vision, speech, natural language processing (NLP), and recently, with …

Despite the remarkable accuracy of deep neural networks in object detection, they are costly
to train and scale due to supervision requirements. Particularly, learning more object …

Annotating videos is cumbersome, expensive and not scalable. Yet, many strong video
models still rely on manually annotated data. With the recent introduction of the HowTo100M …

Labeling data is often expensive and time-consuming, especially for tasks such as object
detection and instance segmentation, which require dense labeling of the image. While few …

One of the key factors of enabling machine learning models to comprehend and solve real-
world tasks is to leverage multimodal data. Unfortunately, annotation of multimodal data is …

Recent empirical studies show that adversarial topic models (ATM) can successfully capture
semantic patterns of the document by differentiating a document with another dissimilar …

We introduce the task of spatially localizing narrated interactions in videos. Key to our
approach is the ability to learn to spatially localize interactions with self-supervision on a …

We introduce the video detours problem for navigating instructional videos. Given a source
video and a natural language query asking to alter the how-to video's current path of …

In this work, we propose an open-vocabulary object detection method that, based on image-
caption pairs, learns to detect novel object classes along with a given set of known classes …