We present the first multimodal dataset MuMMy, for developing research assistants that can interpret Egyptian hieroglyphic texts. It pairs images with Gardiner codes, transliteration, and English translation at two levels of granularity. We also evaluate several deep learning pipelines across OCR, transliteration, and translation tasks, revealing the complexity of the domain and the challenges posed by error accumulation.

The classification of Egyptian hieroglyphs remains a challenging problem due to the vast variability in writing styles across time periods, regions, and individual scribes. In this work, we present a comprehensive evaluation of hieroglyph classification performance across diverse stylistic domains, highlighting the limitations of current models in generalizing beyond a single style. We introduce a dataset that spans multiple writing styles, ranging from monumental inscriptions to handwritten manuscripts, and assess several near state-of-the-art recognition models. Our analysis reveals significant discrepancies in model performance when exposed to unseen styles, underscoring the need for style-aware learning strategies. This study provides a framework for future research on hieroglyph recognition with a focus on stylistic diversity and serves as a first step toward building vision-language systems capable of analyzing Egyptian hieroglyphic writings.

This paper addresses the challenge of artwork colorization by proposing a benchmark for manga colorization using real black-and-white and colorized image pairs. Color images are widely recognized for their ability to capture attention and improve memory retention, yet the manual process of colorization is labor-intensive. Deep learning methods for supervised image-to-image translation offer a promising solution, relying on aligned pairs of black-and-white and color images for training. However, these pairs are often generated synthetically, introducing a domain gap that limits model performance. To address this, we explore the use of real data, proposing a method for creating such datasets. Our benchmarks reveal that models trained on real data significantly outperform those trained on synthetic pairs. Furthermore, we present a pipeline for text removal and panel segmentation, streamlining the comic colorization process. These contributions aim to enhance the generalization and applicability of deep learning models for artwork colorization.

This paper addresses the critical challenges of sparsity and occlusion in LiDAR-based 3D object detection. Current methods often rely on supplementary modules or specific architectural designs, potentially limiting their applicability to new and evolving architectures. To our knowledge, we are the first to propose a versatile technique that seamlessly integrates into any existing framework for 3D Object Detection, marking the first instance of Weak-to-Strong generalization in 3D computer vision. We introduce a novel framework, X-Ray Distillation with Object-Complete Frames, suitable for both supervised and semi-supervised settings, that leverages the temporal aspect of point cloud sequences. This method extracts crucial information from both previous and subsequent LiDAR frames, creating Object-Complete frames that represent objects from multiple viewpoints, thus addressing occlusion and sparsity. Given the limitation of not being able to generate Object-Complete frames during online inference, we utilize Knowledge Distillation within a Teacher-Student framework. This technique encourages the strong Student model to emulate the behavior of the weaker Teacher, which processes simple and informative Object-Complete frames, effectively offering a comprehensive view of objects as if seen through X-ray vision. Our proposed methods surpass state-of-the-art in semi-supervised learning by 1-1.5 mAP and enhance the performance of five established supervised models by 1–2 mAP on standard autonomous driving datasets, even with default hyperparameters. Code for Object-Complete frames is available here: https://github.com/sakharok13/X-Ray-Teacher-Patching-Tools.