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Viewing 18 papers from 2016 in PRIOR
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    • Award Best Student Paper Award
      AAAI 2016
      Babak Saleh, Ahmed Elgammal, Jacob Feldman, and Ali Farhadi
      The human visual system can spot an abnormal image, and reason about what makes it strange. This task has not received enough attention in computer vision. In this paper we study various types of atypicalities in images in a more comprehensive way than has been done before. We propose a new dataset…  (More)
    • AAAI 2016
      Hessam Bagherinezhad, Hannaneh Hajishirzi, Yejin Choi, and Ali Farhadi
      Human vision greatly benefits from the information about sizes of objects. The role of size in several visual reasoning tasks has been thoroughly explored in human perception and cognition. However, the impact of the information about sizes of objects is yet to be determined in AI. We postulate…  (More)
    • NAACL 2016
      Mark Yatskar, Vicente Ordonez, and Ali Farhadi
      Obtaining common sense knowledge using current information extraction techniques is extremely challenging. In this work, we instead propose to derive simple common sense statements from fully annotated object detection corpora such as the Microsoft Common Objects in Context dataset. We show that…  (More)
    • CVPR 2016
      Mark Yatskar, Luke Zettlemoyer, and Ali Farhadi
      This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including: (1) the main activity (e.g., clipping), (2) the participating actors, objects, substances, and locations (e.g., man, shears, sheep, wool, and field) and most…  (More)
    • CVPR 2016
      Roozbeh Mottaghi, Hessam Bagherinezhad, Mohammad Rastegari, and Ali Farhadi
      In this paper, we study the challenging problem of predicting the dynamics of objects in static images. Given a query object in an image, our goal is to provide a physical understanding of the object in terms of the forces acting upon it and its long term motion as response to those forces. Direct…  (More)
    • Award OpenCV People's Choice Award
      CVPR 2016
      Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi
      We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network pre- dicts…  (More)
    • CVPR 2016
      Xiaolong Wang, Ali Farhadi, and Abhinav Gupta
      What defines an action like “kicking ball”? We argue that the true meaning of an action lies in the change or transformation an action brings to the environment. In this paper, we propose a novel representation for actions by modeling an action as a transformation which changes the state of the…  (More)
    • CVPR 2016
      Roozbeh Mottaghi, Hannaneh Hajishirzi, and Ali Fahradi
      With the recent progress in visual recognition, we have already started to see a surge of vision related real-world applications. These applications, unlike general scene understanding, are task oriented and require specific information from visual data. Considering the current growth in new…  (More)
    • JCDL 2016
      Christopher Clark and Santosh Divvala
      Figures and tables are key sources of information in many scholarly documents. However, current academic search engines do not make use of figures and tables when semantically parsing documents or presenting document summaries to users. To facilitate these applications we develop an algorithm that…  (More)
    • ICML 2016
      Junyuan Xie, Ross Girshick, and Ali Farhadi
      Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning representations for clustering. In this paper, we propose Deep Embedded Clustering (DEC), a method…  (More)
    • ECCV 2016
      Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, and Ali Farhadi
      Diagrams are common tools for representing complex concepts, relationships and events, often when it would be difficult to portray the same information with natural images. Understanding natural images has been extensively studied in computer vision, while diagram understanding has received little…  (More)
    • ECCV 2016
      Roozbeh Mottaghi, Mohammad Rastegari, Abhinav Gupta, and Ali Farhadi
      What happens if one pushes a cup sitting on a table toward the edge of the table? How about pushing a desk against a wall? In this paper, we study the problem of understanding the movements of objects as a result of applying external forces to them. For a given force vector applied to a specific…  (More)
    • ECCV 2016
      Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi
      We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in $32\times$ memory saving. In XNOR-Networks, both the filters and the input to…  (More)
    • ECCV 2016
      Gunnar A. Sigurdsson, Gül Varol, Xiaolong Wang, Ali Farhadi, Ivan Laptev, and Abhinav Gupta
      Computer vision has a great potential to help our daily lives by searching for lost keys, watering flowers or reminding us to take a pill. To succeed with such tasks, computer vision methods need to be trained from real and diverse examples of our daily dynamic scenes. While most of such scenes are…  (More)
    • HCOMP 2016
      Gunnar A. Sigurdsson, Olga Russakovsky, Ali Farhadi, Ivan Laptev, and Abhinav Gupta
      Large-scale annotated datasets allow AI systems to learn from and build upon the knowledge of the crowd. Many crowdsourcing techniques have been developed for collecting image annotations. These techniques often implicitly rely on the fact that a new input image takes a negligible amount of time to…  (More)
    • ECCV 2016
      Noah Siegel, Zachary Horvitz, Roie Levin, Santosh Divvala, and Ali Farhadi
      ‘Which are the pedestrian detectors that yield a precision above 95% at 25% recall?’ Answering such a complex query involves identifying and analyzing the results reported in figures within several research papers. Despite the availability of excellent academic search engines, retrieving such…  (More)
    • ECCV 2016
      Junyuan Xie, Ross Girshick, and Ali Farhadi
      We propose Deep3D, a fully automatic 2D-to-3D conversion algorithm that takes 2D images or video frames as input and outputs stereo 3D image pairs. The stereo images can be viewed with 3D glasses or head-mounted VR displays. Deep3D is trained directly on stereo pairs from a dataset of 3D movies to…  (More)
    • CVPR 2016
      Mahyar Najibi, Mohammad Rastegari, and Larry Davis
      We introduce G-CNN, an object detection technique based on CNNs which works without proposal algorithms. G-CNN starts with a multi-scale grid of fixed bounding boxes. We train a regressor to move and scale elements of the grid towards objects iteratively. G-CNN models the problem of object…  (More)