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Meet Gen4Gen: A Semi-Automated Dataset Creation Pipeline Using Generative Models

Text-to-image diffusion models are among the best advances in the field of Artificial Intelligence (AI). However, there are constraints associated with personalizing existing text-to-image diffusion models with various concepts. The current personalization methods are not able to extend to numerous ideas consistently, and it attributes this problem to a possible mismatch between the simple text…

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CMU Researchers Unveil Groundbreaking AI Method for Camera Pose Estimation: Harnessing Ray Diffusion for Enhanced 3D Reconstruction

The pursuit of high-fidelity 3D representations from sparse images has seen considerable advancements, yet the challenge of accurately determining camera poses remains a significant hurdle. Traditional structure-from-motion methods often falter when faced with limited views, prompting a shift towards learning-based strategies that aim to predict camera poses from a sparse image set. These innovative approaches…

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BigGait: Revolutionizing Gait Recognition with Unsupervised Learning and Large Vision Models

In the ever-evolving domain of remote identification technologies, gait recognition stands out for its unique capacity to identify individuals from a certain distance without requiring direct engagement. This cutting-edge approach leverages the distinctive walking patterns of each person, offering a seamless integration into surveillance and security systems. Its non-intrusive nature distinguishes it from more conventional…

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Revolutionizing Image Quality Assessment: The Introduction of Co-Instruct and MICBench for Enhanced Visual Comparisons

Image Quality Assessment (IQA) is a method that standardizes the evaluation criteria for analyzing different aspects of images, including structural information, visual content, etc. To improve this method, various subjective studies have adopted comparative settings. In recent studies, researchers have explored large multimodal models (LMMs) to expand IQA from giving a scalar score to open-ended…

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UC Berkeley Researchers Introduce the Touch-Vision-Language (TVL) Dataset for Multimodal Alignment

Almost all forms of biological perception are multimodal by design, allowing agents to integrate and synthesize data from several sources. Linking modalities, including vision, language, audio, temperature, and robot behaviors, have been the focus of recent research in artificial multimodal representation learning. Nevertheless, the tactile modality is still mostly unexplored when it comes to multimodal…

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Google AI Introduces VideoPrism: A General-Purpose Video Encoder that Tackles Diverse Video Understanding Tasks with a Single Frozen Model

Google researchers address the challenges of achieving a comprehensive understanding of diverse video content by introducing a novel encoder model, VideoPrism. Existing models in video understanding have struggled with various tasks with complex systems and motion-centric reasoning and demonstrated poor performance across different benchmarks. The researchers aimed to develop a general-purpose video encoder that can…

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Meet Swin3D++: An Enhanced AI Architecture based on Swin3D for Efficient Pretraining on Multi-Source 3D Point Clouds

Point clouds serve as a prevalent representation of 3D data, with the extraction of point-wise features being crucial for various tasks related to 3D understanding. While deep learning methods have made significant strides in this domain, they often rely on large and diverse datasets to enhance feature learning, a strategy commonly employed in natural language…

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Meet CoLLaVO: KAIST’s AI Breakthrough in Vision Language Models Enhancing Object-Level Image Understanding

The evolution of Vision Language Models (VLMs) towards general-purpose models relies on their ability to understand images and perform tasks via natural language instructions. However, it must be clarified if current VLMs truly grasp detailed object information in images. The analysis shows that their image understanding correlates strongly with zero-shot performance on vision language tasks.…

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Apple Researchers Propose MAD-Bench Benchmark to Overcome Hallucinations and Deceptive Prompts in Multimodal Large Language Models

Multimodal Large Language Models (MLLMs), having contributed to remarkable progress in AI, face challenges in accurately processing and responding to misleading information, leading to incorrect or hallucinated responses. This vulnerability raises concerns about the reliability of MLLMs in applications where accurate interpretation of text and visual data is crucial. Recent research has explored visual instruction…

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