Conventional document layout analysis (DLA) traditionally depends on empirical priors or a fixed set of learnable queries executed in a single forward pass. While sufficient for early-generation documents with a small, predetermined number of regions, this paradigm struggles with contemporary documents, which exhibit diverse element counts and increasingly complex layouts. To address challenges posed by modern documents, we present HybriDLA, a novel generative framework that unifies diffusion and autoregressive decoding within a single layer. The diffusion component iteratively refines bounding-box hypotheses, whereas the autoregressive component injects semantic and contextual awareness, enabling precise region prediction even in highly varied layouts. To further enhance detection quality, we design a multi-scale feature-fusion encoder that captures both fine-grained and high-level visual cues. This architecture elevates performance to 83.5% mean Average Precision (mAP). Extensive experiments on the DocLayNet and M6Doc benchmarks demonstrate that HybriDLA sets a state-of-the-art performance, outperforming previous approaches.

Video data has become central in many modern systems, from public surveillance to autonomous vehicles, but its widespread use raises serious concerns about exposing people’s identities and behaviors. This survey takes a structured look at how recent research has tried to address these concerns through video anonymization. We conduct a systematic review of the literature and organize existing work into a taxonomy that separates different anonymization strategies, the visual regions they target, and the assumptions they make about the video setting. By examining these categories, we highlight how current methods approach privacy protection and where they tend to fall short. A consistent pattern across the field is that most studies are designed for single-view videos, even though many real environments involve multiple synchronized cameras. Only a handful of works consider this multi-perspective setting, revealing a clear disconnect between research and real-world needs. We also review the datasets and evaluation metrics commonly used in anonymization studies and show that they rarely capture multi-view complexity, underscoring the need for more representative benchmarks. Finally, we discuss the utility goals that anonymization methods aim to preserve and outline key gaps that future work must address to support practical, multi-camera video applications.

In a world driven by data visualization, ensuring the inclusive accessibility of charts for Blind and Visually Impaired (BVI) individuals remains a significant challenge. Charts are usually presented as raster graphics without textual and visual metadata needed for an equivalent exploration experience for BVI people. Additionally, converting these charts into accessible formats requires considerable effort from sighted individuals. Digitizing charts with metadata extraction is just one aspect of the issue; transforming it into accessible modalities, such as tactile graphics, presents another difficulty. To address these disparities, we propose Chart4Blind, an intelligent user interface that converts bitmap image representations of line charts into universally accessible formats. Chart4Blind achieves this transformation by generating Scalable Vector Graphics (SVG), Comma-Separated Values (CSV), and alternative text exports, all comply with established accessibility standards. Through interviews and a formal user study, we demonstrate that even inexperienced sighted users can make charts accessible in an average of 4 minutes using Chart4Blind, achieving a System Usability Scale rating of 90%. In comparison to existing approaches, Chart4Blind provides a comprehensive solution, generating end-to-end accessible SVGs suitable for assistive technologies such as embossed prints (papers and laser cut), 2D tactile displays, and screen readers. For additional information, including open-source codes and demos, please visit our project page https://moured.github.io/chart4blind/.