Text-based person search aims to retrieve the most relevant pedestrian images from an image gallery based on textual descriptions. Most existing methods rely on two separate encoders to extract the image and text features, and then elaborately design various schemes to bridge the gap between image and text modalities. However, the shallow interaction between both modalities in these methods is still insufficient to eliminate the modality gap. To address the above problem, we propose TransTPS, a transformer-based framework that enables deeper interaction between both modalities through the self-attention mechanism in transformer, effectively alleviating the modality gap. In addition, due to the small inter-class variance and large intra-class variance in image modality, we further develop two techniques to overcome these limitations. Specifically, Cross-modal Multi-Granularity Matching (CMGM) is proposed to address the problem caused by small inter-class variance and facilitate distinguishing pedestrians with similar appearance. Besides, Contrastive Loss with Weakly Positive pairs (CLWP) is introduced to mitigate the impact of large intra-class variance and contribute to the retrieval of more target images. Experiments on CUHK-PEDES and RSTPReID datasets demonstrate that our proposed framework achieves state-of-the-art performance compared to previous methods.

Moiré artifacts are common in digital photography, resulting from the interference between high-frequency scene content and the color filter array of the camera. Existing deep learning-based demoiréing methods trained on large scale datasets are limited in handling various complex moiré patterns, and mainly focus on demoiréing of photos taken of digital displays. Moreover, obtaining moiré-free ground-truth in natural scenes is difficult but needed for training. In this paper, we propose a self-adaptive learning method for demoiréing a high-frequency image, with the help of an additional defocused moiré-free blur image. Given an image degraded with moiré artifacts and a moiré-free blur image, our network predicts a moiré-free clean image and a blur kernel with a self-adaptive strategy that does not require an explicit training stage, instead performing test-time adaptation. Our model has two sub-networks and works iteratively. During each iteration, one sub-network takes the moiré image as input, removing moiré patterns and restoring image details, and the other sub-network estimates the blur kernel from the blur image. The two sub-networks are jointly optimized. Extensive experiments demonstrate that our method outperforms state-of-the-art methods and can produce high-quality demoiréd results. It can generalize well to the task of removing moiré artifacts caused by display screens. In addition, we build a new moiré dataset, including images with screen and texture moiré artifacts. As far as we know, this is the first dataset with real texture moiré patterns.