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19 Nov

MagicFace: Training-free Universal-Style Human Image Customized Synthesis

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[Submitted on 14 Aug 2024 (v1), last revised 18 Nov 2024 (this version, v5)] View a PDF of the paper titled MagicFace: Training-free Universal-Style Human Image Customized Synthesis, by Yibin Wang and Weizhong Zhang and Cheng Jin View PDF HTML (experimental) Abstract:Current human image customization methods leverage Stable Diffusion (SD) for its rich semantic prior. However, since SD is not specifically designed for human-oriented generation, these methods often require extensive fine-tuning on large-scale datasets, which renders them susceptible to overfitting and hinders their ability to personalize individuals with previously unseen styles. Moreover, these methods extensively focus on single-concept human image…

19 Nov

Utilize the Flow before Stepping into the Same River Twice: Certainty Represented Knowledge Flow for Refusal-Aware Instruction Tuning

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[Submitted on 9 Oct 2024 (v1), last revised 18 Nov 2024 (this version, v2)] View a PDF of the paper titled Utilize the Flow before Stepping into the Same River Twice: Certainty Represented Knowledge Flow for Refusal-Aware Instruction Tuning, by Runchuan Zhu and 6 other authors View PDF HTML (experimental) Abstract:Refusal-Aware Instruction Tuning (RAIT) enables Large Language Models (LLMs) to refuse to answer unknown questions. By modifying responses of unknown questions in the training data to refusal responses such as "I don't know", RAIT enhances the reliability of LLMs and reduces their hallucination. Generally, RAIT modifies training samples based on…

19 Nov

Cloudera Enhances Data Catalog and Metadata Management with Octopai Acquisition

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Cloudera, an enterprise data cloud solutions provider, has agreed to acquire Octopai’s data lineage and catalog platform. The acquisition is expected to be completed by the end of this month. This move significantly enhances Cloudera’s data catalog and metadata management capabilities. The struggle to achieve comprehensive data intelligence is a fundamental challenge faced by enterprises in a data-driven world. This issue is especially pronounced in highly regulated industries, such as finance, healthcare, and telecommunications. Organizations often struggle to gain data visibility over multiple data solutions across hybrid environments. Cloudera aims to address the current fragmented state of enterprise data by…

19 Nov

5 Ways to Get Kickstarted with Databricks at AWS re:Invent

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Databricks is turning up the heat at AWS re:Invent 2024, and we’re bringing more than just data and AI solutions to the table. From hands-on demos and inspiring sessions to networking events — and the chance to win a pair of custom sneakers — here are five reasons to make our booth a must-visit this year.1. Learn How to Accelerate Data and AI Innovation at Our Booth 1820Stop by the Databricks booth to explore the latest in data and AI innovation. Our experts will be there to walk you through our Data Intelligence Platform and discuss how Databricks can accelerate…

19 Nov

CART: Compositional Auto-Regressive Transformer for Image Generation

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arXiv:2411.10180v1 Announce Type: cross Abstract: In recent years, image synthesis has achieved remarkable advancements, enabling diverse applications in content creation, virtual reality, and beyond. We introduce a novel approach to image generation using Auto-Regressive (AR) modeling, which leverages a next-detail prediction strategy for enhanced fidelity and scalability. While AR models have achieved transformative success in language modeling, replicating this success in vision tasks has presented unique challenges due to the inherent spatial dependencies in images. Our proposed method addresses these challenges by iteratively adding finer details to an image compositionally, constructing it as a hierarchical combination of base and detail…

19 Nov

The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use

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18 Nov

VLEU: a Method for Automatic Evaluation for Generalizability of Text-to-Image Models

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[Submitted on 23 Sep 2024 (v1), last revised 15 Nov 2024 (this version, v2)] View a PDF of the paper titled VLEU: a Method for Automatic Evaluation for Generalizability of Text-to-Image Models, by Jingtao Cao and 2 other authors View PDF HTML (experimental) Abstract:Progress in Text-to-Image (T2I) models has significantly improved the generation of images from textual descriptions. However, existing evaluation metrics do not adequately assess the models' ability to handle a diverse range of textual prompts, which is crucial for their generalizability. To address this, we introduce a new metric called Visual Language Evaluation Understudy (VLEU). VLEU uses large…

18 Nov

Pentaho President Maggie Laird Discusses Company’s Return to Data Management Roots

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The AI boom is forcing companies to come to grips with an uncomfortable reality: Their data is not well managed. That’s good news for data intelligence companies like Pentaho, which is refocusing its efforts on data management and governance under Maggie Laird, who was promoted to president of the Hitachi-Vantara subsidiary in April. Laird recently joined the Big Data Debrief to chat about the impact of AI on data management, the massive opportunity it poses for data intelligence, and the new direction she is leading Pentaho to monetize that opportunity and help customers come to grips with their big data…

18 Nov

Databricks training invests in closing the data + AI skills gap across enterprises

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The Data + AI Skills GapThe “skills gap” has been a concern for CEOs and leaders for many years, and the gap is only widening. According to McKinsey, as many as 375 million workers globally might have to change occupations soon to meet company needs. There is a significant divide between where workforces are presently and where they need to be in a few years. With this anticipated gap in mind, leaders need to shift their focus on upskilling and reskilling their workforce. This skills gap is especially felt with the fast-paced technology advancements of data + AI.Impact of Databricks…

18 Nov

How to Annotate Image Files for Machine Learning at Scale

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The performance of computer vision algorithms is greatly influenced by the quality of the images used for the training and validation. Every image in your training dataset should have thoughtful and accurate labels depending on the computer vision task you are working on. Image labeling and annotation are the foundational steps in accurately labeling the image data and developing machine learning (ML) models for the computer vision task. Tasks like object detection, image classification, segmentation, etc. require image annotation so that ML models can interpret the images at a granular level and produce high-quality predictions in real-world applications.While image annotation…