99 citations · 279 across the 37 of their papers we have counts for
10 papers · 1 filter
Everything at Once -- Multi-modal Fusion Transformer for Video Retrieval
Nina Shvetsova, Brian Chen, Andrew Rouditchenko +6
Multi-modal learning from video data has seen increased attention recently as it allows to train semantically meaningful embeddings without human annotation enabling tasks like zer…
Cascaded Multilingual Audio-Visual Learning from Videos
Andrew Rouditchenko, Angie Boggust, David Harwath +8
In this paper, we explore self-supervised audio-visual models that learn from instructional videos. Prior work has shown that these models can relate spoken words and sounds to vis…
Learning with Algorithmic Supervision via Continuous Relaxations
Felix Petersen, Christian Borgelt, Hilde Kuehne +1
The integration of algorithmic components into neural architectures has gained increased attention recently, as it allows training neural networks with new forms of supervision suc…
Style Agnostic 3D Reconstruction via Adversarial Style Transfer
Felix Petersen, Bastian Goldluecke, Oliver Deussen +1
Reconstructing the 3D geometry of an object from an image is a major challenge in computer vision. Recently introduced differentiable renderers can be leveraged to learn the 3D geo…
Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting
Anna Kukleva, Hilde Kuehne, Bernt Schiele
Both generalized and incremental few-shot learning have to deal with three major challenges: learning novel classes from only few samples per class, preventing catastrophic forgett…
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules
Aisha Urooj Khan, Hilde Kuehne, Kevin Duarte +3
The problem of grounding VQA tasks has seen an increased attention in the research community recently, with most attempts usually focusing on solving this task by using pretrained…
Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision
Felix Petersen, Christian Borgelt, Hilde Kuehne +1
Sorting and ranking supervision is a method for training neural networks end-to-end based on ordering constraints. That is, the ground truth order of sets of samples is known, whil…
Unsupervised Discriminative Embedding for Sub-Action Learning in Complex Activities
Sirnam Swetha, Hilde Kuehne, Yogesh S Rawat +1
Action recognition and detection in the context of long untrimmed video sequences has seen an increased attention from the research community. However, annotation of complex activi…
Detector-Free Weakly Supervised Grounding by Separation
Assaf Arbelle, Sivan Doveh, Amit Alfassy +14
Nowadays, there is an abundance of data involving images and surrounding free-form text weakly corresponding to those images. Weakly Supervised phrase-Grounding (WSG) deals with th…
Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos
Brian Chen, Andrew Rouditchenko, Kevin Duarte +10
Multimodal self-supervised learning is getting more and more attention as it allows not only to train large networks without human supervision but also to search and retrieve data…