Unlike traditional classification tasks, recommendation is inherently subjective-whether an item should be suggested depends not only on user preferences and item semantics, but also on latent behavioral patterns and contextual cues. While recent LLM-based recommenders excel at modeling semantics and intent through generative reasoning, they often fail to capture collaborative signals and suffer from inefficiencies when applied globally across large interaction spaces. We propose Local Large Language Models for Recommendation(L3Rec), a novel model-agnostic framework that integrates collaborative filtering(CF) with generative LLMs through localized modeling. Our approach first applies a light-weight CF model to derive user and item embeddings, then clusters them into behaviorally coherent subgroups. Each cluster is assigned a dedicated generative LLM-referred to as a local LLM-trained only on its corresponding data subset. This enables fine-grained personalization while improving training efficiency through parallelism. At inference time, predictions from local models are aggregated via a fusion strategy, with a global CF fallback when needed. To the best of our knowledge, this is the first LLM-based recommendation framework to incorporate local collaborative structure. Experiments show that it achieves state-of-the-art performance with significantly better scalability and efficiency.

Inverse rendering aims to reconstruct geometry and reflectance from captured images. Display-camera imaging systems offer unique advantages for this task: each pixel can easily function as a programmable point light source, and the polarized light emitted by LCD displays facilitates diffuse-specular separation. Despite these benefits, there is currently no public real-world dataset captured using display-camera systems, unlike other setups such as light stages. This absence hinders the development and evaluation of display-based inverse rendering methods. In this paper, we introduce the first real-world dataset for display-based inverse rendering. To achieve this, we construct and calibrate an imaging system comprising an LCD display and stereo polarization cameras. We then capture a diverse set of objects with diverse geometry and reflectance under one-light-at-a-time (OLAT) display patterns. We also provide high-quality ground-truth geometry. Our dataset enables the synthesis of captured images under arbitrary display patterns and different noise levels. Using this dataset, we evaluate the performance of existing photometric stereo and inverse rendering methods, and provide a simple, yet effective baseline for display inverse rendering, outperforming state-of-the-art inverse rendering methods. Code and dataset are available on our project page at https://michaelcsj.github.io/DIR/

Image datasets are essential not only in validating existing methods in computer vision but also in developing new methods. Many image datasets exist, consisting of trichromatic intensity images taken with RGB cameras, which are designed to replicate human vision. However, polarization and spectrum, the wave properties of light that animals in harsh environments and with limited brain capacity often rely on, remain underrepresented in existing datasets. Although there are previous spectro-polarimetric datasets, they have insufficient object diversity, limited illumination conditions, linear-only polarization data, and inadequate image count. Here, we introduce two spectro-polarimetric datasets, consisting of trichromatic Stokes images and hy-perspectral Stokes images. These datasets encompass both linear and circular polarization; they introduce multiple spectral channels; and they feature a broad selection of real-world scenes. With our dataset in hand, we analyze the spectro-polarimetric image statistics, develop efficient representations of such high-dimensional data, and evaluate spectral dependency of shape-from-polarization methods. As such, the proposed dataset promises a foundation for data-driven spectro-polarimetric imaging and vision research.

Graph convolutions have been successfully applied to recommendation systems, utilizing high-order collaborative signals present in the user-item interaction graph. This idea, however, has not been applicable to the cold-start items, since cold nodes are isolated in the graph and thus do not take advantage of information exchange from neighboring nodes. Recently, there have been a few attempts to utilize graph convolutions on item-item or user-user attribute graphs to capture high-order collaborative signals for cold-start cases, but these approaches are still limited in that the item-item or user-user graph falls short in capturing the dynamics of user-item interactions, as their edges are constructed based on arbitrary and heuristic attribute similarity.

Abstract Holography stands at the forefront of visual technology, offering immersive, three‐dimensional visualizations through the manipulation of light wave amplitude and phase. Although generative models have been extensively explored in the image domain, their application to holograms remains relatively underexplored due to the inherent complexity of phase learning. Exploiting generative models for holograms offers exciting opportunities for advancing innovation and creativity, such as semantic‐aware hologram generation and editing. Currently, the most viable approach for utilizing generative models in the hologram domain involves integrating an image‐based generative model with an image‐to‐hologram conversion model, which comes at the cost of increased computational complexity and inefficiency. To tackle this problem, we introduce P‐Hologen, the first end‐to‐end generative framework designed for phase‐only holograms (POHs). P‐Hologen employs vector quantized variational autoencoders to capture the complex distributions of POHs. It also integrates the angular spectrum method into the training process, constructing latent spaces for complex phase data using strategies from the image processing domain. Extensive experiments demonstrate that P‐Hologen achieves superior quality and computational efficiency compared to the existing methods. Furthermore, our model generates high‐quality unseen, diverse holographic content from its learned latent space without requiring pre‐existing images. Our work paves the way for new applications and methodologies in holographic content creation, opening a new era in the exploration of generative holographic content. The code for our paper is publicly available on https://github.com/james0223/P-Hologen .