841 citations · 7.7k across the 158 of their papers we have counts for
11 papers · 1 filter
Instance-Level Segmentation for Autonomous Driving with Deep Densely Connected MRFs
Ziyu Zhang, Sanja Fidler, Raquel Urtasun
Our aim is to provide a pixel-wise instance-level labeling of a monocular image in the context of autonomous driving. We build on recent work [Zhang et al., ICCV15] that trained a…
MovieQA: Understanding Stories in Movies through Question-Answering
Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen +3
We introduce the MovieQA dataset which aims to evaluate automatic story comprehension from both video and text. The dataset consists of 14,944 questions about 408 movies with high…
Training Deep Neural Networks via Direct Loss Minimization
Yang Song, Alexander G. Schwing, Richard S. Zemel +1
Supervised training of deep neural nets typically relies on minimizing cross-entropy. However, in many domains, we are interested in performing well on metrics specific to the appl…
Order-Embeddings of Images and Language
Ivan Vendrov, Ryan Kiros, Sanja Fidler +1
Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images. In this paper we advocate…
Reduced-Precision Strategies for Bounded Memory in Deep Neural Nets
Patrick Judd, Jorge Albericio, Tayler Hetherington +4
This work investigates how using reduced precision data in Convolutional Neural Networks (CNNs) affects network accuracy during classification. More specifically, this study consid…
Skip-Thought Vectors
Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov +4
We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoder-decoder model that tries…
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
Yukun Zhu, Ryan Kiros, Richard Zemel +4
Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and h…
Monocular Object Instance Segmentation and Depth Ordering with CNNs
Ziyu Zhang, Alexander G. Schwing, Sanja Fidler +1
In this paper we tackle the problem of instance-level segmentation and depth ordering from a single monocular image. Towards this goal, we take advantage of convolutional neural ne…
Fully Connected Deep Structured Networks
Alexander G. Schwing, Raquel Urtasun
Convolutional neural networks with many layers have recently been shown to achieve excellent results on many high-level tasks such as image classification, object detection and mor…
Generating Multi-Sentence Lingual Descriptions of Indoor Scenes
Dahua Lin, Chen Kong, Sanja Fidler +1
This paper proposes a novel framework for generating lingual descriptions of indoor scenes. Whereas substantial efforts have been made to tackle this problem, previous approaches f…
segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
Yukun Zhu, Raquel Urtasun, Ruslan Salakhutdinov +1
In this paper, we propose an approach that exploits object segmentation in order to improve the accuracy of object detection. We frame the problem as inference in a Markov Random F…