In this post, we explain the basics behind our paper “DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval”, […]
Last year, we had the opportunity to conduct a desk research study in an effort to map the landscape of […]
In this post we explain the basics behind our paper “Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation” […]
In this post, we explain the basics behind our paper “Operation-wise Attention Network for Tampering Localization Fusion”, which has been accepted for publication at this year’s Content-Based Multimedia Indexing conference (CBMI 2021).
In this post, we explain the basics behind our paper “Audio-based Near-Duplicate Video Retrieval with Audio Similarity Learning,” which has […]
It has been almost 2 months since the final deadline for the challenge on the Kaggle platform. Competition organizers have just finalized the standings (13th of June 2020) in the private leaderboard. A Kaggle staff member mentioned in a discussion that competition organizers took their time to validate winning submissions and ensure that they comply with the competition rules. This process resulted in the disqualification of the top-performing team due to the usage of external data without proper license. This caused a lot of disturbance among the Kaggle community mainly because the competition rules were vague.
In this post we explain the basics behind our paper with title “ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning” which was accepted for an oral presentation at this year’s International Conference on Computer Vision (ICCV 2019).