Jin Tang

Jiandong Jin, Xiao Wang, Yin Lin et al.

Current pedestrian attribute recognition (PAR) algorithms use multi-label or multi-task learning frameworks with specific classification heads. These models often struggle with imbalanced data and noisy samples. Inspired by the success of generative models, we propose Sequence Pedestrian Attribute Recognition (SequencePAR), a novel sequence generation paradigm for PAR. SequencePAR extracts pedestrian features using a language-image pre-trained model and embeds the attribute set into query tokens guided by text prompts. A Transformer decoder generates human attributes by integrating visual features and attribute query tokens. The masked multi-head attention layer in the decoder prevents the model from predicting the next attribute during training. The extensive experiments on multiple PAR datasets validate the effectiveness of SequencePAR. Specifically, we achieve 84.92\%, 90.44\%, 90.73\%, and 90.46\% in accuracy, precision, recall, and F1-score on the PETA dataset. The source code and pre-trained models are available at https://github.com/Event-AHU/OpenPAR.

Qingling Shu, Sibao Chen, Xiao Wang et al.

Accurate detection of road and bridge changes is crucial for urban planning and transportation management, yet presents unique challenges for general change detection (CD). Key difficulties arise from maintaining the continuity of roads and bridges as linear structures and disambiguating visually similar land covers (e.g., road construction vs. bare land). Existing spatial-domain models struggle with these issues, further hindered by the lack of specialized, semantically rich datasets. To fill these gaps, we introduce the Road and Bridge Semantic Change Detection (RB-SCD) dataset. As the first benchmark to systematically target semantic change detection of roads and bridges, RB-SCD offers comprehensive fine-grained annotations for 11 semantic change categories. This enables a detailed analysis of traffic infrastructure evolution. Building on this, we propose a novel framework, the Multimodal Frequency-Driven Change Detector (MFDCD). MFDCD integrates multimodal features in the frequency domain through two key components: (1) the Dynamic Frequency Coupler (DFC), which leverages wavelet transform to decompose visual features, enabling it to robustly model the continuity of linear transitions; and (2) the Textual Frequency Filter (TFF), which encodes semantic priors into frequency-domain graphs and applies filter banks to align them with visual features, resolving semantic ambiguities. Experiments demonstrate the state-of-the-art performance of MFDCD on RB-SCD and three public CD datasets. The code will be available at https://github.com/DaGuangDaGuang/RB-SCD.