The Pedestrian Attribute Recognition (PAR) task aims to identify various detailed attributes of an individual, such as clothing, accessories, and gender. To enhance PAR performance, a model must capture features ranging from coarse-grained global attributes (e.g., for identifying gender) to fine-grained local details (e.g., for recognizing accessories) that may appear in diverse regions. Recent research suggests that body part representation can enhance the model’s robustness and accuracy, but these methods are often restricted to attribute classes within fixed horizontal regions, leading to degraded performance when attributes appear in varying or unexpected body locations. In this paper, we propose Visual and Textual Attribute Alignment with Attribute Prompting for Pedestrian Attribute Recognition, dubbed as ViTA-PAR, to enhance attribute recognition through specialized multimodal prompting and vision-language alignment. We introduce visual attribute prompts that capture global-to-local semantics, enabling diverse attribute representations. To enrich textual embeddings, we design a learnable prompt template, termed person and attribute context prompting, to learn person and attributes context. Finally, we align visual and textual attribute features for effective fusion. ViTA-PAR is validated on four PAR benchmarks, achieving competitive performance with efficient inference. We release our code and model at https://github.com/mlnjeongpark/ViTA-PAR.

Interests in manufacturing process management and analysis are increasing, but it is difficult to conduct process analysis due to the increase of manufacturing data. Therefore, we suggest a manufacturing data analysis system that collects event logs from so-called big data and analyzes the collected logs with process mining. There are two kinds of big data generated from manufacturing processes, structured data and unstructured data. Usually, manufacturing process analysis is conducted by using only structured data, however the proposed system uses both structured and unstructured data for enhancing the process analysis results. The system automatically discovers a process model and conducts various performance analysis on the manufacturing processes.

This letter proposes a new algorithm, termed bidimensional statistical empirical mode decomposition (BSEMD) that adopts a smoothing procedure instead of an interpolation when constructing 2-D upper and lower envelopes. For this purpose, we investigate the sifting process effect of conventional bidimensional empirical mode decomposition (BEMD) on the decomposition results, and propose a modified BEMD via the smoothing sifting process coupling with a new identification method of 2-D local extrema. Furthermore, theoretical rationale for smoothing sifting is investigated.