Abstract

Semantic Segmentation Datasets for ResourceConstrained TrainingAshutosh Mishra1?, Sudhir Kumar1,2?, Tarun Kalluri1,3?, Girish Varma1,Anbumani Subramaian4, Manmohan Chandraker3, and CV Jawahar11IIIT Hyderabad2University at Buffalo, State University of New York3University of California, San Diego4Intel BangaloreAbstract.Several large scale datasets, coupled with advances in deepneural network architectures have been greatly successful in pushingthe boundaries of performance in semantic segmentation in recent years.However, the scale and magnitude of such datasets prohibits ubiquitoususe and widespread adoption of such models, especially in settings withserious hardware and software resource constraints. Through this work,we propose two simple variants of the recently proposed IDD dataset,namelyIDD-miniandIDD-lite, for scene understanding in unstructuredenvironments. Our main objective is to enable research and benchmark-ing in training segmentation models. We believe that this will enablequick prototyping useful in applications like optimum parameter andarchitecture search, and encourage deployment on low resource hardwaresuch as Raspberry Pi. We show qualitatively and quantitatively that withonly 1 hour of training on 4GB GPU memory, we can achieve satisfactorysemantic segmentation performance on the proposed datasets.

Abstract

In recent years, there has been an explosion in the number of memesbeing created and circulated in online social networks. Despite theirrapidly increasing impact on how we communicate online, memeimages are virtually inaccessible to the visually impaired users. Ex-isting automated assistive systems that were primarily devised fornatural photos in social media, overlook the specific fine-grainedvisual details in meme images. In this paper, we concentrate ondescribing one such prominent visual detail: the meme face emo-tion. We propose a novel automated method that enables visuallyimpaired social media users to understand and appreciate memeface emotions with the help of rich textual captions. We first collecta challenging dataset of meme face emotion captions to supportfuture research in face emotion understanding. We design a two-stage approach that significantly outperforms baseline approaches across all the standard captioning metrics and also generates richerdiscriminative captions. By validating our solution with the helpof visually impaired social media users, we show that our emotioncaptions enable them to understand and appreciate one of the mostpopular classes of meme images encountered on the Internet forthe first time. Code, data, and models are publicly available.

Abstract

3D Human Body Reconstruction from a monocular image is an important problem in computer vision with applications in virtual and augmented reality platforms, animation industry, en-commerce domain, etc. While several of the existing works formulate it as a volumetric or parametric learning with complex and indirect reliance on re-projections of the mesh, we would like to focus on implicitly learning the mesh representation. To that end, we propose a novel model, HumanMeshNet, that regresses a template mesh's vertices, as well as receives a regularization by the 3D skeletal locations in a multi-branch, multi-task setup. The image to mesh vertex regression is further regularized by the neighborhood constraint imposed by mesh topology ensuring smooth surface reconstruction. The proposed paradigm can theoretically learn local surface deformations induced by body shape variations and can therefore learn high-resolution meshes going ahead. We show comparable performance with SoA (in terms of surface and joint error) with far lesser computational complexity, modeling cost and therefore real-time reconstructions on three publicly available datasets. We also show the generalizability of the proposed paradigm for a similar task of predicting hand mesh models. Given these initial results, we would like to exploit the mesh topology in an explicit manner going ahead.

Abstract

No Results Found

Abstract

The popularity of egocentric cameras and their always-on nature has lead to the abundance of day-long first-person videos. Because of the extreme shake and highly redundant nature, these videos are difficult to watch from beginning to end and often require summarization tools for their efficient consumption. However, traditional summarization techniques developed for static surveillance videos, or highly curated sports videos and movies are, either, not suitable or simply do not scale for such hours long videos in the wild. On the other hand, specialized summarization techniques developed for egocentric videos limit their focus to important objects and people. In this paper, we present a novel unsupervised reinforcement learning technique to generate video summaries from day long egocentric videos. Our approach can be adapted to generate summaries of various lengths making it possible to view even 1-minute summaries of one’s entire day. The technique can also be adapted to various rewards, such as distinctiveness and indicativeness of the summary. When using the facial saliency-based reward, we show that our approach generates summaries focusing on social interactions, similar to the current state-of-the-art (SOTA). Quantitative comparison on the benchmark Disney dataset shows that our method achieves significant improvement in Relaxed F-Score (RFS) (32.56 vs. 19.21) and BLEU score (12.12 vs. 10.64). Finally, we show that our technique can be applied for summarizing traditional, short, hand-held videos as well, where we improve the SOTA F-score on benchmark SumMe and TVSum datasets from 41.4 to 45.6 and 57.6 to 59.1 respectively

Abstract

In light of the recent breakthroughs in automatic machine transla-tion systems, we propose a novel approach that we term as "Face-to-Face Translation". As today’s digital communication becomesincreasingly visual, we argue that there is a need for systems thatcan automatically translate a video of a person speaking in lan-guage A into a target language B with realistic lip synchronization.In this work, we create an automatic pipeline for this problem anddemonstrate its impact in multiple real-world applications. First, webuild a working speech-to-speech translation system by bringing together multiple existing modules from speech and language. Wethen move towards "Face-to-Face Translation" by incorporating anovel visual module, LipGAN for generating realistic talking facesfrom the translated audio. Quantitative evaluation of LipGAN onthe standard LRW test set shows that it significantly outperform sexisting approaches across all standard metrics. We also subjectour Face-to-Face Translation pipeline, to multiple human evalua-tions and show that it can significantly improve the overall user experience for consuming and interacting with multimodal content across languages. Code, models and demo video are made publicly available.

Abstract

No Abstract available

Abstract

While several datasets for autonomous navigation have become available in recent years, they have tended to focus on structured driving environments. This usually corresponds to well-delineated infrastructure such as lanes, a small number of well-defined categories for traffic participants, low variation in object or background appearance and strong adherence to traffic rules. We propose DS, a novel dataset for road scene understanding in unstructured environments where the above assumptions are largely not satisfied. It consists of 10,004 images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads. The label set is expanded in comparison to popular benchmarks such as Cityscapes, to account for new classes. It also reflects label distributions of road scenes significantly different from existing datasets, with most classes displaying greater within-class diversity. Consistent with …

Abstract

Spoof detectors are classifiers that are trained to distinguish spoof fingerprints from bonafide ones. However, state of the art spoof detectors do not generalize well on unseen spoof materials. This study proposes a style transfer based augmentation wrapper that can be used on any existing spoof detector and can dynamically improve the robustness of the spoof detection system on spoof materials for which we have very low data. Our method is an approach for synthesizing new spoof images from a few spoof examples that transfers the style or material properties of the spoof examples to the content of bonafide fingerprints to generate a larger number of examples to train the classifier on. We demonstrate the effectiveness of our approach on materials in the publicly available LivDet 2015 dataset and show that the proposed approach leads to robustness to fingerprint spoofs of the target material.

Abstract

A system and method for capturing Omni-Stereo videos using multi-sensor is disclosed. The system includes left cameras, right cameras and a viewing circle. The method of capturing omni stereo videos using multi-sensor approach includes steps of: capturing images of a scene using left cameras, capturing images of a scene using right cameras, processing each image from the left camera and right camera using a computation method, and obtaining a final omni stereo frame through the computation method.