2.6k citations · 9.3k across the 89 of their papers we have counts for
8 papers · 1 filter
COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning
Simon Ging, Mohammadreza Zolfaghari, Hamed Pirsiavash +1
Many real-world video-text tasks involve different levels of granularity, such as frames and words, clip and sentences or videos and paragraphs, each with distinct semantics. In th…
Beyond Single Stage Encoder-Decoder Networks: Deep Decoders for Semantic Image Segmentation
Gabriel L. Oliveira, Senthil Yogamani, Wolfram Burgard +1
Single encoder-decoder methodologies for semantic segmentation are reaching their peak in terms of segmentation quality and efficiency per number of layers. To address these limita…
Scaling Imitation Learning in Minecraft
Artemij Amiranashvili, Nicolai Dorka, Wolfram Burgard +2
Imitation learning is a powerful family of techniques for learning sensorimotor coordination in immersive environments. We apply imitation learning to attain state-of-the-art perfo…
FlowControl: Optical Flow Based Visual Servoing
Max Argus, Lukas Hermann, Jon Long +1
One-shot imitation is the vision of robot programming from a single demonstration, rather than by tedious construction of computer code. We present a practical method for realizing…
Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View with a Reachability Prior
Osama Makansi, Özgün Cicek, Kevin Buchicchio +1
In this paper, we investigate the problem of anticipating future dynamics, particularly the future location of other vehicles and pedestrians, in the view of a moving vehicle. We a…
Explicitly Modeled Attention Maps for Image Classification
Andong Tan, Duc Tam Nguyen, Maximilian Dax +2
Self-attention networks have shown remarkable progress in computer vision tasks such as image classification. The main benefit of the self-attention mechanism is the ability to cap…
Temporal Shift GAN for Large Scale Video Generation
Andres Munoz, Mohammadreza Zolfaghari, Max Argus +1
Video generation models have become increasingly popular in the last few years, however the standard 2D architectures used today lack natural spatio-temporal modelling capabilities…
Optimized Generic Feature Learning for Few-shot Classification across Domains
Tonmoy Saikia, Thomas Brox, Cordelia Schmid
To learn models or features that generalize across tasks and domains is one of the grand goals of machine learning. In this paper, we propose to use cross-domain, cross-task data a…