Edit fidelity is a significant issue in open-world controllable generative image editing. Recently, CLIP-based approaches have traded off simplicity to alleviate these problems by introducing spatial attention in a handpicked layer of a StyleGAN. In this paper, we propose CoralStyleCLIP, which incorporates a multi-layer attention-guided blending strategy in the feature space of StyleGAN2 for obtaining high-fidelity edits. We propose multiple forms of our co-optimized region and layer selection strategy to demonstrate the variation of time complexity with the quality of edits over different architectural intricacies while preserving simplicity. We conduct extensive experimental analysis and benchmark our method against state-of-the-art CLIP-based methods. Our findings suggest that CoralStyleCLIP results in high-quality edits while preserving the ease of use.

Organizations measure their social audience based on the number of users, fans, and followers on social media. Every social media platform has its user identity and a single user is present across varied platforms. Due to the disconnected user profiles, identifying duplicate users across media is non-trivial. There is a need to create a complete view of a user for various applications such as targeting and user profile construction. This view is not easily available due to the individual identities. In this work, we explore the feature space across social media that can be leveraged for intelligent user identity aggregation. Further, we present a two-phased unified identity creation process using our feature analysis, unsupervised candidate selection, and supervised user matching algorithms on four different social networks.

Identifying and targeting visitors on an e-commerce website with personalized content in real-time is extremely important to marketers. Although such targeting exists today, it is based on demographic attributes of the visitors. We show that dynamic visitor attributes extracted from their click-stream provide much better predictive capabilities of visitor intent. In this demonstration, we showcase an interactive real-time user interface for marketers to visualize and target visitor segments. Our dashboard not only provides the marketers understanding of their visitor click patterns, but also lets them target individual or group of visitors with offers and promotions.

Location based services are proving to be the next driving factors for growth in smartphones. While GPS solves the problem of accurate localization in outdoor environments, indoor localization is still an area of active research. Emergence of new generation smartphones with low cost sensors, have provided an effective way of indoor localization by pedestrian dead reckoning (PDR). We propose a robust mechanism for detecting the step of a person and estimating his step length. Our system is independent of the location and orientation of the device. Our system is shown to perform 45% better than the traditional PDR systems proposed in prior-art. Another important problem in PDR system is determining the orientation of the mobile device and the direction of user motion. Many systems assume the device to be oriented in the direction of the user motion. Some of the recent systems use accelerometer, magnetometer patterns and PCA to detect the direction of user orientation. We propose a system which uses map matching and particle filtering to determine the direction of user motion. We tabulate our findings on the feasibility of such a system.