Text--image retrieval is necessary for applications such as product recommendation. Embedding-based approaches like CLIP enable efficient large-scale retrieval via vector similarity search, but they are primarily trained on literal caption-like text--image pairs and often fail to capture abstract or persona-driven attributes common in product recommendation applications (e.g., ``a gift for a mother who loves gardening''). In contrast, state-of-the-art vision--language models (vLLMs) can align text with images in a flexible manner, but their limited context window prevents them from directly handling retrieval over large catalogs. We propose a framework that distills the preference rankings of a powerful vLLM into an embedding-based system, transferring its nuanced alignment abilities while maintaining the inference-time scalability of an embedding-based approach. Experiments on persona-driven product recommendation tasks demonstrate that our method significantly outperforms existing embedding-based baselines, providing an efficient solution for personalized text--image retrieval.

The model is trained on over 700k The extraordinary growth of online retail - as < image, text > pairs from the inventory of of 2020, 4 trillion dollars per year (Cramer-Flood, Farfetch, one of the largest fashion luxury retailer 2020) - had a profound impact on the fashion industry, in the world, and is applied to use cases with 1 out of 4 transactions now happening online known to be crucial in a vast global market; (McKinsey, 2019). The combination of large amounts of data and variety of use cases supported 2. we evaluate FashionCLIP in a variety of by growing investments has made e-commerce fertile tasks, showing that fine-tuning helps capture for the application of cutting-edge machine domain-specific concepts and generalize them learning models, with NLP involved in recommendations in zero-shot scenarios; we supplement quantitative (de Souza Pereira Moreira et al., 2019; Guo tests with qualitative analyses, and et al., 2020; Goncalves et al., 2021), information offer preliminary insights of how concepts retrieval (IR) (Ai and Narayanan.R, 2021), product grounded in a visual space unlock linguistic

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This week marks the start of a series of talks about the iFetch project that I'll be giving. Our goal with iFetch is to create a trustworthy multimodal conversational agent for the online fashion marketplace. How do you get away from question-answering chatbots? How can multimodality be used to address various customers' intents? What should be the best policy to address our customer's goals and be capable of responding in the right tone?

Litigating Artificial Intelligence: When Does AI Violate Our Legal Rights? Read full article May 27, 2021, 3:20 PM ·3 min read From the minds of Canada's leading law and technology experts comes a playbook for understanding the multi-faceted intersection of AI and the law TORONTO, May 27, 2021 (GLOBE NEWSWIRE) -- We are living in an Artificial Intelligence (AI) boom. Self-driving cars, personal voice assistants, and facial recognition technology are just a few of the AI-enabled technologies permeating into everyday life. But what happens when AI causes harm or violates our rights? If your self-driving car gets into an accident while on autopilot, are you responsible? Emond Publishing, Canada's leading independent legal publisher, today announced the release of Litigating Artificial Intelligence, a book examining AI-informed legal determinations, AI-based lawsuits, and AI-enabled litigation tools. Anchored by the expertise of general editors Jill R. Presser, Jesse Beatson, and Gerald Chan, this title offers practical insights regarding AI's decision-making capabilities, position in evidence law and product-based lawsuits, role in automating legal work, and use by the courts, tribunals, and government agencies. For example, can government agencies use AI-powered facial recognition software to identify BLM protestors and Capitol rioters, or does this violate privacy rights? Who is liable, users, developers, or AI? What laws are in place to prevent AI-related crimes, and how do litigators prosecute the responsible parties?

The fourth-annual Midas List Europe, produced by Forbes in partnership with TrueBridge Capital Partners, has arrived, and we're excited to share the top companies that drove the portfolios of this year's top European venture capitalists. The outlook for the European venture market may have been cloudy at the beginning of the global pandemic as recessionary cutbacks loomed and the IPO window narrowed, but European startups and investors have since bounced back. A wide variety of tech-based startups have been able to ride the tailwinds of the crisis, with new areas of everyday life benefitting from the transition to a technology-driven environment. Evidentially, investors remain clear-eyed and eager to invest in growth and innovation on either side of the pond with European VC deal value – and potentially fundraising – on pace to set new annual records. Here are the top ten companies that acted as key drivers behind this year's Midas List Europe: It's been a boom year for Stockholm-based Spotify, which is making its third consecutive appearance as the #1 driver on the Midas List Europe and fourth appearance overall.

In this meetup series, we want to inspire everyone to use or implement Machine Learning and AI not only to serve great causes but also to have a demonstrable positive impact on society. We'll be talking about how we can use AI to do just that, through applying it to health, education, democracy, nutrition and more, but also how we can technically implement that while ensuring what we build is ethical. Please register on Eventbrite to be sure to secure a spot! Abstract: Healthcare is one of the most challenging spaces in which artificial intelligence operates. Medical machine learning solutions are highly regulated, require the most personal of data and have literal life-and-death consequences.