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50 papers · 1 filter
CourtMotion: Learning Event-Driven Motion Representations from Skeletal Data for Basketball
Omer Sela, Michael Chertok, Lior Wolf
This paper presents CourtMotion, a spatiotemporal modeling framework for analyzing and predicting game events and plays as they develop in professional basketball. Anticipating bas…
Leveraging Cycle-Consistent Anchor Points for Self-Supervised RGB-D Registration
Siddharth Tourani, Jayaram Reddy, Sarvesh Thakur +3
With the rise in consumer depth cameras, a wealth of unlabeled RGB-D data has become available. This prompts the question of how to utilize this data for geometric reasoning of sce…
Evaluating the Fairness of Discriminative Foundation Models in Computer Vision
Junaid Ali, Matthaeus Kleindessner, Florian Wenzel +3
We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP), that are used for labeling tasks. We then syst…
Scalable and Accurate Self-supervised Multimodal Representation Learning without Aligned Video and Text Data
Vladislav Lialin, Stephen Rawls, David Chan +3
Scaling up weakly-supervised datasets has shown to be highly effective in the image-text domain and has contributed to most of the recent state-of-the-art computer vision and multi…
A Comprehensive Review of Modern Object Segmentation Approaches
Yuanbo Wang, Unaiza Ahsan, Hanyan Li +1
Image segmentation is the task of associating pixels in an image with their respective object class labels. It has a wide range of applications in many industries including healthc…
Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images
Theophil Trippe, Martin Genzel, Jan Macdonald +1
This work presents a novel deep-learning-based pipeline for the inverse problem of image deblurring, leveraging augmentation and pre-training with synthetic data. Our results build…