Customer-obsessed science


Research areas
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July 22, 2025Generating diverse synthetic prior distributions leads to a tabular foundation model that outperforms task-specific baselines.
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2025Text-to-Image diffusion models have shown remarkable capabilities in generating high-quality images. However, current models often struggle to adhere to the complete set of conditions specified in the input text and return unfaithful generations. Existing works address this problem by either fine-tuning the base model or modifying the latent representations during the inference stage with gradient-based
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2025Query-product relevance classification is crucial for e-commerce stores like Amazon, ensuring accurate search results that match customer intent. Using a unified multilingual model across multiple languages/marketplaces tends to yield superior outcomes but also presents challenges, especially in maintaining performance across all languages when the model is updated or expanded to include a new one. To tackle
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2025Audio Description (AD) plays a pivotal role as an application system aimed at guaranteeing accessibility in multi-media content, which provides additional narrations at suitable intervals to describe visual elements, catering specifically to the needs of visually impaired audiences. In this paper, we introduce CA3D, the pioneering unified Context-Aware Automatic Audio Description system that provides AD
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2025In various video-language learning tasks, the challenge of achieving cross-modality alignment with multi-grained data persists. We propose a method to tackle this challenge from two crucial perspectives: data and modeling. Given the absence of a multi-grained video-text pretraining dataset, we introduce a Granularity EXpansion (GEX) method with Integration and Compression operations to expand the granularity
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3DV 20252025Current image-to-3D approaches suffer from high computational costs and lack scalability for high-resolution outputs. In contrast, we introduce a novel framework to directly generate explicit surface geometry and texture using multi-view 2D depth and RGB images along with 3D Gaussian features using a repurposed Stable Diffusion model. We introduce a depth branch into U-Net for efficient and high quality
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