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Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting
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abstract
Pedestrian attribute recognition aims to assign multiple attributes to one pedestrian image captured by a video surveillance camera. Although numerous methods are proposed and make tremendous progress, we argue that it is time to step back and analyze the status quo of the area. We review and rethink the recent progress from three perspectives. First, given that there is no explicit and complete definition of pedestrian attribute recognition, we formally define and distinguish pedestrian attribute recognition from other similar tasks. Second, based on the proposed definition, we expose the limitations of the existing datasets, which violate the academic norm and are inconsistent with the essential requirement of practical industry application. Thus, we propose two datasets, PETA\textsubscript{$ZS$} and RAP\textsubscript{$ZS$}, constructed following the zero-shot settings on pedestrian identity. In addition, we also introduce several realistic criteria for future pedestrian attribute dataset construction. Finally, we reimplement existing state-of-the-art methods and introduce a strong baseline method to give reliable evaluations and fair comparisons. Experiments are conducted on four existing datasets and two proposed datasets to measure progress on pedestrian attribute recognition.
Forward citations
Cited by 5 Pith papers
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ReSAGE-PAR: Representational Similarity Assessment for Generative Expansion in Pedestrian Attribute Recognition
ReSAGE-PAR adapts diffusion models with LoRA, scores generated images via vision-language prompts, and applies Bayesian classification to produce pseudo-labels, yielding up to 8.7% gains when used to expand PAR datasets.
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A Data-Centric Approach to Pedestrian Attribute Recognition: Synthetic Augmentation via Prompt-driven Diffusion Models
A prompt-driven diffusion augmentation pipeline with a custom weighted loss gives modest PAR improvements, but test-set tuning inflates the reported gains.
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Dynamic Pattern Alignment Learning for Pretraining Lightweight Human-Centric Vision Models
Distilling three pattern-specific alignments from a large human-centric teacher yields a 5M-parameter student that approaches teacher-level generalization on many downstream tasks.
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Open-Attribute Recognition for Person Retrieval: Finding People Through Distinctive and Novel Attributes
A task, a model, and four rebuilt benchmarks for retrieving people from attribute words unseen during training, presented as Open-Attribute Recognition for Person Retrieval.
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Enhancing Zero-Shot Pedestrian Attribute Recognition with Synthetic Data Generation: A Comparative Study with Image-To-Image Diffusion Models
Diffusion-based synthetic expansion of pedestrian attribute training sets yields 1.1 to 3.3 point mA gains over the base PAR model on PA100k, PETAzs, and RAPzs.
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