Weakly Supervised Localization using Deep Feature Maps

Object localization is an important computer vision problem with a variety of applications. The lack of large scale object-level annotations and the relative abundance of image-level labels makes a compelling case for weak supervision in the object localization task. Deep Convolutional Neural Networks are a class of state-of-the-art methods for the related problem of object recognition. In this paper, we describe a novel object localization algorithm which uses classification networks trained on only image labels. This weakly supervised method leverages local spatial and semantic patterns captured in the convolutional layers of classification networks. We propose an efficient beam search based approach to detect and localize multiple objects in images. The proposed method significantly outperforms the state-of-the-art in standard object localization data-sets with a 8 point increase in mAP scores

Search Tracker: Human-derived object tracking in-the-wild through large-scale search and retrieval

Humans use context and scene knowledge to easily localize moving objects in conditions of complex illumination changes, scene clutter and occlusions. In this paper, we present a method to leverage human knowledge in the form of annotated video libraries in a novel search and retrieval based setting to track objects in unseen video sequences. For every video sequence, a document that represents motion information is generated. Documents of the unseen video are queried against the library at multiple scales to find videos with similar motion characteristics. This provides us with coarse localization of objects in the unseen video. We further adapt these retrieved object locations to the new video using an efficient warping scheme. The proposed method is validated on in-the-wild video surveillance datasets where we outperform state-of-the-art appearance-based trackers. We also introduce a new challenging dataset with complex object appearance changes.Comment: Under review with the IEEE Transactions on Circuits and Systems for Video Technolog

Compatibility of \u3cem\u3eMetarhizium anisopliae\u3c/em\u3e (Metsch.) Sorok. with \u3cem\u3eOcimum sanctum\u3c/em\u3e Linn. (Tulsi) (Lamiaceae) Extracts

The compatibility of Metarhizium anisopliae with Ocimum sanctum was studied in vitro. Leaves, roots, stems and seed extracts of O. sanctum were mixed in a Potato Dextrose Agar and Potato Dextrose Broth. M. anisopliae was inoculated and the mycelial dry weight and spore count were assessed. The behavior of the fungus with the extracts was similar in terms of mycelial dry weight, except for methanol extracts of leaves, ether extracts of roots, water and acetone extracts of seeds and benzene, methanol and acetone extracts of stems which reduced the mycelial dry weight of the fungal colonies. Benzene extract of leaves and methanol extract of roots of O. sanctum were found to be highly compatible with M. anisopliae whereas ether extract of roots and benzene as well as acetone extracts of stem were classified as very toxic. The results of the current study revealed that O. sanctum extracts did not affect the inoculum potential of M. anisopliae in terms of mycelial dry weight and spore count and hence M. anisopliae was compatible with O. sanctum

Beyond Spatial Auto-Regressive Models: Predicting Housing Prices with Satellite Imagery

When modeling geo-spatial data, it is critical to capture spatial correlations for achieving high accuracy. Spatial Auto-Regression (SAR) is a common tool used to model such data, where the spatial contiguity matrix (W) encodes the spatial correlations. However, the efficacy of SAR is limited by two factors. First, it depends on the choice of contiguity matrix, which is typically not learnt from data, but instead, is assumed to be known apriori. Second, it assumes that the observations can be explained by linear models. In this paper, we propose a Convolutional Neural Network (CNN) framework to model geo-spatial data (specifi- cally housing prices), to learn the spatial correlations automatically. We show that neighborhood information embedded in satellite imagery can be leveraged to achieve the desired spatial smoothing. An additional upside of our framework is the relaxation of linear assumption on the data. Specific challenges we tackle while implementing our framework include, (i) how much of the neighborhood is relevant while estimating housing prices? (ii) what is the right approach to capture multiple resolutions of satellite imagery? and (iii) what other data-sources can help improve the estimation of spatial correlations? We demonstrate a marked improvement of 57% on top of the SAR baseline through the use of features from deep neural networks for the cities of London, Birmingham and Liverpool.Comment: 10 pages, 5 figure