Recent advances in large vision-language models have revolutionized the image classification paradigm. Despite showing impressive zero-shot capabilities, a pre-defined set of categories, a.k.a. the vocabulary, is assumed at test time for composing the textual prompts. However, such assumption can be impractical when the semantic context is unknown and evolving. We thus formalize a novel task, termed as Vocabulary-free Image Classification (VIC), where we aim to assign to an input image a class that resides in an unconstrained language-induced semantic space, without the prerequisite of a known vocabulary. VIC is a challenging task as the semantic space is extremely large, containing millions of concepts, with hard-to-discriminate fine-grained categories. In this work, we first empirically verify that representing this semantic space by means of an external vision-language database is the most effective way to obtain semantically relevant content for classifying the image. We then propose Category Search from External Databases (CaSED), a method that exploits a pre-trained vision-language model and an external vision-language database to address VIC in a training-free manner. CaSED first extracts a set of candidate categories from captions retrieved from the database based on their semantic similarity to the image, and then assigns to the image the best matching candidate category according to the same vision-language model. Experiments on benchmark datasets validate that CaSED outperforms other complex vision-language frameworks, while being efficient with much fewer parameters, paving the way for future research in this direction.

The following lemma states that each level set of the quadratic loss surface is a hyper-ellipsoid, providing another useful tool for the propositions given in this section. Lemma 2. The level set of the quadratic loss at ε is a hyper-ellipsoid defined by: (θ θ

The following lemma states that each level set of the quadratic loss surface is a hyper-ellipsoid, providing another useful tool for the propositions given in this section. Lemma 2. The level set of the quadratic loss at ε is a hyper-ellipsoid defined by: (θ θ

Recent advances in large vision-language models have revolutionized the image classification paradigm. Despite showing impressive zero-shot capabilities, a pre-defined set of categories, a.k.a. the vocabulary, is assumed at test time for composing the textual prompts. However, such assumption can be impractical when the semantic context is unknown and evolving. We thus formalize a novel task, termed as Vocabulary-free Image Classification (VIC), where we aim to assign to an input image a class that resides in an unconstrained language-induced semantic space, without the prerequisite of a known vocabulary. VIC is a challenging task as the semantic space is extremely large, containing millions of concepts, with hard-to-discriminate fine-grained categories.

Alex Galvagni, CEO of Age of Learning and a former artificial intelligence researcher with NASA, says advances in AI now make it possible to deliver to children "a personalized and supportive" experience in education. ROBOTIC POWER WEAR: A groundbreaking AI-powered exoskeleton developed by researchers at North Carolina State University and the University of North Carolina at Chapel Hill promises to be a game-changer for individuals with mobility issues. ELECTION SEASON: Google on Monday announced that it will have a mandatory requirement for advertisers to disclose election ads that use digitally altered content in depictions of real or realistic-looking people or events. Victor Miller is running for mayor of Cheyenne as AI bot'VIC' (Fox News Digital) 'AI FOR MAYOR': A Wyoming man who filed for the state capital's mayor's race as an AI bot named "VIC" spoke to Fox News Digital this week about VIC's landmark candidacy and a breaking setback he encountered moments before taping. SAFEGUARD SUMMER SOJOURNS: A new study by online protection company McAfee has identified the top five destinations most frequently targeted by cybercriminals for online booking scams.

This paper presents a framework that selectively triggers security reviews for incoming source code changes. Functioning as a review bot within a code review service, the framework can automatically request additional security reviews at pre-submit time before the code changes are submitted to a source code repository. Because performing such secure code reviews add cost, the framework employs a classifier trained to identify code changes with a high likelihood of vulnerabilities. The online classifier leverages various types of input features to analyze the review patterns, track the software engineering process, and mine specific text patterns within given code changes. The classifier and its features are meticulously chosen and optimized using data from the submitted code changes and reported vulnerabilities in Android Open Source Project (AOSP). The evaluation results demonstrate that our Vulnerability Prevention (VP) framework identifies approximately 80% of the vulnerability-inducing code changes in the dataset with a precision ratio of around 98% and a false positive rate of around 1.7%. We discuss the implications of deploying the VP framework in multi-project settings and future directions for Android security research. This paper explores and validates our approach to code change-granularity vulnerability prediction, offering a preventive technique for software security by preemptively detecting vulnerable code changes before submission.

Quantifying variable importance is essential for answering high-stakes questions in fields like genetics, public policy, and medicine. Current methods generally calculate variable importance for a given model trained on a given dataset. However, for a given dataset, there may be many models that explain the target outcome equally well; without accounting for all possible explanations, different researchers may arrive at many conflicting yet equally valid conclusions given the same data. Additionally, even when accounting for all possible explanations for a given dataset, these insights may not generalize because not all good explanations are stable across reasonable data perturbations. We propose a new variable importance framework that quantifies the importance of a variable across the set of all good models and is stable across the data distribution. Our framework is extremely flexible and can be integrated with most existing model classes and global variable importance metrics. We demonstrate through experiments that our framework recovers variable importance rankings for complex simulation setups where other methods fail. Further, we show that our framework accurately estimates the true importance of a variable for the underlying data distribution. We provide theoretical guarantees on the consistency and finite sample error rates for our estimator. Finally, we demonstrate its utility with a real-world case study exploring which genes are important for predicting HIV load in persons with HIV, highlighting an important gene that has not previously been studied in connection with HIV. Code is available at https://github.com/jdonnelly36/Rashomon_Importance_Distribution.

In "Server," your novella, an American teaching English in Japan is drawn into an old video game, where he finds his former best friend, Vic, now dead, somehow alive and in control. Until this novella, very little of your work has included fantastical elements. What led you to incorporate this idea, and what challenges did it pose for you? It was pretty challenging: this was fairly far outside my comfort zone. Nothing I've written--book, essay, audio fiction, nothing--has taken me as long to actually finish.

There has been substantial progress in the past decade in the development of object classifiers for images, for example of faces, humans and vehi- cles. Here we address the problem of contaminations (e.g. Variational inference is used to marginalize over contamination and obtain robust classification. In this way the VIC ap- proach can turn a kernel classifier for clean data into one that can tolerate contamination, without any specific training on contaminated positives. Recent progress in discriminative object detection, especially for faces, has yielded good performance and efficiency [1, 2, 3, 4].

Vic.ai, a leading provider of AI-powered AP Automation and Intelligence,announced it will offer autonomous invoicing in the Coupa App Marketplace, connecting businesses with certified, pre-built solutions. Coupa Software certified Vic.ai's Autonomous Invoice Processing solution for use within the Coupa Business Spend Management (BSM) Platform its cloud-based platform that empowers companies around the world with the visibility and control they need to make smarter spending decisions. Vic.ai's Coupa-certified invoicing solution replaces legacy OCR template and rules-based invoice processing methods using next-generation AI technology to deliver fully coded invoices to the Coupa BSM Platform. Every invoice is analyzed by Vic.ai's proprietary AI to extract and predict all relevant invoice information, including vendors, dates, amounts, cost accounts, and dimension values – all without the use of templates. Once coded, invoices are sent to Coupa for approval and payments.