Ishan Misra

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Andrew Zisserman34
Deva Ramanan29
Luc Van Gool22
Antonio Torralba22
Martial Hebert21
16Martial Hebert
9Abhinav Gupta
7Abhinav Shrivastava
6Leonidas J. Guibas
6Alexei A. Efros

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Learning a Predictable and Generative Vector Representation for Objects
What is a good vector representation of an object? We believe that it should be generative in 3D, in the sense that it can produce new 3D objects; as well as be predictable from 2D, in the sense that it can be perceived from 2D images. We propose a novel architecture, called the TL-embedding network , to learn an embedding space with these properties. The… (More)
Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification
In this paper, we present an approach for learning a visual representation from the raw spatiotemporal signals in videos. Our representation is learned without supervision from semantic labels. We formulate our method as an unsupervised sequential verification task, i.e., we determine whether a sequence of frames from a video is in the correct temporal… (More)
Generating Natural Questions About an Image
There has been an explosion of work in the vision & language community during the past few years from image captioning to video transcription, and answering questions about images. These tasks have fo-cused on literal descriptions of the image. To move beyond the literal, we choose to explore how questions about an image are often directed at commonsense… (More)
Cross-Stitch Networks for Multi-task Learning
Multi-task learning in Convolutional Networks has displayed remarkable success in the field of recognition. This success can be largely attributed to learning shared representations from multiple supervisory tasks. However, existing multi-task approaches rely on enumerating multiple network architectures specific to the tasks at hand, that do not… (More)
Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels
When human annotators are given a choice about what to label in an image, they apply their own subjective judgments on what to ignore and what to mention. We refer to these noisy "human-centric" annotations as exhibiting human reporting bias. Examples of such annotations include image tags and keywords found on photo sharing sites, or in datasets containing… (More)
Visual Storytelling
We introduce the first dataset for sequential vision-to-language, and explore how this data may be used for the task of visual storytelling. The first release of this dataset, SIND 1 v.1, includes 81,743 unique photos in 20,211 sequences , aligned to both descriptive (caption) and story language. We establish several strong baselines for the storytelling… (More)
Hybrid implementation of error diffusion dithering
Many image filtering operations provide ample parallelism, but progressive non-linear processing of images is among the hardest to parallelize due to long, sequential, and non-linear data dependency. A typical example of such an operation is error diffusion dithering, exemplified by the Floyd-Steinberg algorithm. In this paper, we present its… (More)
Watch and Learn: Semi-Supervised Learning for Object Detectors From Video
We present a semi-supervised approach that localizes multiple unknown object instances in long videos. We start with a handful of labeled boxes and iteratively learn and label hundreds of thousands of object instances. We propose criteria for reliable object detection and tracking for constraining the semi-supervised learning process and minimizing semantic… (More)
Watch and learn: Semi-supervised learning of object detectors from videos
We present a semi-supervised approach that localizes multiple unknown object instances in long videos. We start with a handful of labeled boxes and iteratively learn and label hundreds of thousands of object instances. We propose criteria for reliable object detection and tracking for constraining the semi-supervised learning process and minimizing semantic… (More)
Unsupervised Learning using Sequential Verification for Action Recognition
In this paper, we present an approach for learning a visual representation from the raw spatiotemporal signals in videos. Our representation is learned without supervision from semantic labels. We formulate our method as an unsupervised sequential verification task, i.e., we determine whether a sequence of frames from a video is in the correct temporal… (More)