Research Area
Year Published

881 Results

October 27, 2019

Video Classification with Channel-Separated Convolutional Networks

International Conference on Computer Vision (ICCV)

This paper studies the effects of different design choices in 3D group convolutional networks for video classification. We empirically demonstrate that the amount of channel interactions plays an important role in the accuracy of 3D group convolutional networks.

By: Du Tran, Heng Wang, Lorenzo Torresani, Matt Feiszli

October 27, 2019

Compositional Video Prediction

International Conference on Computer Vision (ICCV)

We present an approach for pixel-level future prediction given an input image of a scene. We observe that a scene is comprised of distinct entities that undergo motion and present an approach that operationalizes this insight. We implicitly predict future states of independent entities while reasoning about their interactions, and compose future video frames using these predicted states.

By: Yufei Ye, Maneesh Singh, Abhinav Gupta, Shubham Tulsiani

October 27, 2019

DistInit: Learning Video Representations Without a Single Labeled Video

International Conference on Computer Vision (ICCV)

Video recognition models have progressed significantly over the past few years, evolving from shallow classifiers trained on hand-crafted features to deep spatiotemporal networks. However, labeled video data required to train such models has not been able to keep up with the ever increasing depth and sophistication of these networks. In this work we propose an alternative approach to learning video representations that requires no semantically labeled videos, and instead leverages the years of effort in collecting and labeling large and clean still-image datasets.

By: Rohit Girdhar, Du Tran, Lorenzo Torresani, Deva Ramanan

October 27, 2019

Improved Conditional VRNNs for Video Prediction

International Conference on Computer Vision (ICCV)

Predicting future frames for a video sequence is a challenging generative modeling task. Promising approaches include probabilistic latent variable models such as the Variational Auto-Encoder. While VAEs can handle uncertainty and model multiple possible future outcomes, they have a tendency to produce blurry predictions. In this work we argue that this is a sign of underfitting.

By: Lluís Castrejón, Nicolas Ballas, Aaron Courville

October 27, 2019

SCSampler: Sampling Salient Clips from Video for Efficient Action Recognition

International Conference on Computer Vision (ICCV)

In this paper we introduce a lightweight “clip-sampling” model that can efficiently identify the most salient temporal clips within a long video. We demonstrate that the computational cost of action recognition on untrimmed videos can be dramatically reduced by invoking recognition only on these most salient clips. Furthermore, we show that this yields significant gains in recognition accuracy compared to analysis of all clips or randomly/uniformly selected clips.

By: Bruno Korbar, Du Tran, Lorenzo Torresani

October 26, 2019

Co-Separating Sounds of Visual Objects

International Conference on Computer Vision (ICCV)

Learning how objects sound from video is challenging, since they often heavily overlap in a single audio channel. Current methods for visually-guided audio source separation sidestep the issue by training with artificially mixed video clips, but this puts unwieldy restrictions on training data collection and may even prevent learning the properties of “true” mixed sounds. We introduce a co-separation training paradigm that permits learning object-level sounds from unlabeled multi-source videos.

By: Ruohan Gao, Kristen Grauman

October 26, 2019

Grounded Human-Object Interaction Hotspots From Video

International Conference on Computer Vision (ICCV)

Learning how to interact with objects is an important step towards embodied visual intelligence, but existing techniques suffer from heavy supervision or sensing requirements. We propose an approach to learn human-object interaction “hotspots” directly from video.

By: Tushar Nagarajan, Christoph Feichtenhofer, Kristen Grauman

October 26, 2019

On Network Design Spaces for Visual Recognition

International Conference on Computer Vision (ICCV)

Over the past several years progress in designing better neural network architectures for visual recognition has been substantial. To help sustain this rate of progress, in this work we propose to reexamine the methodology for comparing network architectures. In particular, we introduce a new comparison paradigm of distribution estimates, in which network design spaces are compared by applying statistical techniques to populations of sampled models, while controlling for confounding factors like network complexity.

By: Ilija Radosavovic, Justin Johnson, Saining Xie, Wan-Yen Lo, Piotr Dollar

October 26, 2019

Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution

International Conference on Computer Vision (ICCV)

In natural images, information is conveyed at different frequencies where higher frequencies are usually encoded with fine details and lower frequencies are usually encoded with global structures. Similarly, the output feature maps of a convolution layer can also be seen as a mixture of information at different frequencies. In this work, we propose to factorize the mixed feature maps by their frequencies, and design a novel Octave Convolution (OctConv) operation to store and process feature maps that vary spatially “slower” at a lower spatial resolution reducing both memory and computation cost.

By: Yunpeng Chen, Haoqi Fan, Bing Xu, Zhicheng Yan, Yannis Kalantidis, Marcus Rohrbach, Shuicheng Yan, Jiashi Feng

October 25, 2019

Fashion++: Minimal Edits for Outfit Improvement

International Conference on Computer Vision (ICCV)

Given an outfit, what small changes would most improve its fashionability? This question presents an intriguing new vision challenge. We introduce Fashion++, an approach that proposes minimal adjustments to a full-body clothing outfit that will have maximal impact on its fashionability.

By: Wei-Lin Hsiao, Isay Katsman, Chao-Yuan Wu, Devi Parikh, Kristen Grauman