Customer-obsessed science

Synthesizing high-fidelity talking head videos of an arbitrary identity, lip-synced to a target speech segment, is a challenging problem. Recent GAN-based methods succeed by training a model on a large amount of videos, allowing the generator to learn a variety of audio-lip representations. However, they are unable to handle head pose changes. On the other hand, Neural Radiance Fields (NeRFs) model the

Existing general purpose frameworks for gigantic model training, i.e., dense models with billions of parameters, cannot scale efficiently on cloud environment with various networking conditions due to large communication overheads. In this paper, we propose MiCS, which Minimizes the Communication Scale to bring down communication overhead. Specifically, by decreasing the number of participants in a communication

Product dimension is a crucial piece of information enabling customers make better buying decisions. Ecommerce websites extract dimension attributes to enable customers filter the search results according to their requirements. The existing methods extract dimension attributes from textual data like title and product description. However, this textual information often exists in an ambiguous, disorganized

Selecting an ideal profile image to represent a person is a common problem with many applications. The ideal characteristics of a representative or profile image differ based on the application. In this work, we focus on selecting a representative face which is easy to recognise and aesthetically pleasing. Manually curating these images is time consuming, repetitive, and subjective. This makes the quality

Open-set logo recognition is commonly solved by first detecting possible logo regions and then matching the detected parts against an ever-evolving dataset of cropped logo images. The matching model, a metric learning problem, is especially challenging for logo recognition due to the mixture of text and symbols in logos. We propose two novel contributions to improve the matching model’s performance: (a)