Att2Search: an Attribute-based Pedestrian Search

Pedestrian search based on image or text query often incur high computational costs due to embedding learning. To address this, we propose Att2Search, a method that leverages semantic-level attributes extracted from images, including two main steps: attribute extraction and attribute matching. Extracted attributes are encoded into binary vectors, which are compared using logical XNOR and AND operations. The method supports two types of queries: image-based and attribute-list. Additionally, we use the multilingual Gemini-1.5-Flash LLM to generate Vietnamese text-descriptions from extracted attributes, enabling another matching method. Our experiments are conducted on the 3000VnPersonSearch dataset for Vietnamese person search. Two separate PAR networks that have been trained on the Market-1501 and PA100K datasets are used to extract different semantic-level attributes from the dataset. Att2Search achieves an accuracy of 75.40 % and 68.40% at R@1 when using the XNOR and AND-based matching strategy, respectively, outperforms the state-of-the-art text-based method on the same dataset.