Fusing infrared polarization images for road detection via denoising diffusion probabilistic models.

Recent advancements in road detection using infrared polarization imaging have shown promising results. However, existing methods focus on refined network structures without effectively exploiting infrared polarization imaging mechanisms for enhanced detection. The scarcity of datasets also limits the performance of these methods.

SketchTrans: Disentangled Prototype Learning With Transformer for Sketch-Photo Recognition.

Matching hand-drawn sketches with photos (a.k.a sketch-photo recognition or re-identification) faces the information asymmetry challenge due to the abstract nature of the sketch modality.

Noise-aware infrared polarization image fusion based on salient prior with attention-guided filtering network.

Infrared polarization image fusion integrates intensity and polarization information, producing a fused image that enhances visibility and captures crucial details. However, in complex environments, polarization imaging is susceptible to noise interference. Existing fusion methods typically use the infrared intensity (S0) and degree of linear polarization (DoLP) images for fusion but fail to consider the noise interference, leading to reduced performance.

Structure-Aware Positional Transformer for Visible-Infrared Person Re-Identification.

Visible-infrared person re-identification (VI-ReID) is a cross-modality retrieval problem, which aims at matching the same pedestrian between the visible and infrared cameras. Due to the existence of pose variation, occlusion, and huge visual differences between the two modalities, previous studies mainly focus on learning image-level shared features. Since they usually learn a global representation or extract uniformly divided part features, these methods are sensitive to misalignments.

End-to-End Comparative Attention Networks for Person Re-Identification.

Person re-identification across disjoint camera views has been widely applied in video surveillance yet it is still a challenging problem. One of the major challenges lies in the lack of spatial and temporal cues, which makes it difficult to deal with large variations of lighting conditions, viewing angles, body poses, and occlusions. Recently, several deep-learning-based person re-identification approaches have been proposed and achieved remarkable performance.