Publications

Lipstick virtual try-on (VTO) experiences have become widespread across the e-commerce sector and assist users in eliminating the guesswork of shopping online. How-ever, such experiences still lack in both realism and accuracy. In this work, we propose LipAT, a neural framework that blends the strengths of Physics-Based Rendering (PBR) and Neural Style Transfer (NST) approaches to directly apply lipstick

We propose DocFormerv2, a multi-modal transformer for Visual Document Understanding (VDU). The VDU domain entails understanding documents (beyond mere OCR predictions) e.g., extracting information from a form, VQA for documents and other tasks. VDU is challenging as it needs a model to make sense of multiple modalities (visual, language and spatial) to make a prediction. Our approach, termed DocFormerv2

Search tasks require finding items similar to a given query, making it a crucial aspect of various applications. However, storing and computing similarity for millions or billions of item representations can be computationally expensive. To address this, quantization-based hash methods present memory and inference-efficient solutions by converting continuous representations into non-negative integer codes

In online businesses, personalization of site content is crucial for providing a better user experience and increasing customer engagement. Machine learning algorithms are often used to analyze customer data such as browsing behavior, purchase history to tailor the website content to each individual customer’s preferences and needs. However, measuring the success of these personalized experiences can be

Transformers are central in modern natural language processing and computer vision applications. Despite recent works devoted to reducing the quadratic cost of such models (as a function of the sequence length), dealing with ultra long sequences (e.g., with more than 16K tokens) remains challenging. Applications such as answering questions based on a book or summarizing a scientific article are inefficient

While most task-oriented dialogues assume conversations between the agent and one user at a time, dialogue systems are increasingly expected to communicate with multiple users simultaneously who make decisions collaboratively. To facilitate development of such systems, we release the Multi-User MultiWOZ dataset: task-oriented dialogues among two users and one agent. To collect this dataset, each user utterance

To analyze multivariate time series, most previous methods assume regular subsampling of time series, where the interval between adjacent measurements and the number of samples remain unchanged. Practically, data collection systems could produce irregularly sampled time series due to sensor failures and interventions. However, existing methods designed for regularly sampled multivariate time series cannot

Online A/B tests have become an indispensable tool across all the technology industry: if performed correctly, “online” experiments can inform effective decision making and product development. It should therefore not be surprising that Gupta et al. [2019] estimates that online businesses alone collectively run hundreds of thousands of experiments annually. Modern online experiments are often run in marketplaces

Recent advancements in generative models have led to significant improvements in the quality of generated images, making them virtually indistinguishable from real ones. However, using AI generated images for training robust computer vision models for real-world applications, especially object detection in road scene perception, is still a challenge. AI generated images usually lack the required diversity

Studies in bias and fairness in natural language processing have primarily examined social biases within a single language and/or across few attributes (e.g. gender, race). However, biases can manifest differently across various languages for individual attributes. As a result, it is critical to examine biases within each language and attribute. Of equal importance is to study how these biases compare across

The quality of recorded videos and images is significantly influenced by the camera’s field of view (FOV). In critical applications like surveillance systems and self-driving cars, an inadequate FOV can give rise to severe safety and security concerns, including car accidents and thefts due to the failure to detect individuals and objects. The conventional methods for establishing the correct FOV heavily

Rust has risen in prominence as a systems programming language in large part due to its focus on reliability. The language’s advanced type system and borrow checker eliminate certain classes of memory safety violations. But for critical pieces of code, teams need assurance beyond what the type checker alone can provide. Verification tools for Rust can check other properties, from memory faults in unsafe

Multiple Gateways (GWs) provide network connectivity to Internet of Things (IoT) sensors in a Wide Area Network (WAN). The End Nodes (ENs) can connect to any GW by discovering and acquiring its periodic beacons. This provides GW diversity, improving coverage area. However, simultaneous periodic beacon transmissions among nearby GWs lead to interference and collisions. In this study, the impact of such intra-network

In this paper, we explore the intriguing effects of JPEG compression as a pre-processing technique for image classification tasks. Building upon the findings of a previous study by Friedland et al., which demonstrated that substantial JPEG compression does not significantly degrade classification accuracy, we investigate the potential benefits and limitations of this approach when applied to various classifiers

In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project1 and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using