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Pedestrian detection in crowded scenes is a challenging problem due to the diverse occlusion patterns and highly overlap. To tackle this critical problem, we propose a mutual learning detection network. First, a self-attention mechanism is proposed to achieve mutual learning between individuals by capturing similar semantics among pedestrians. Feature representation of occluded individuals is enhanced by locally fusing similar semantics.

Text-based pedestrian search (TBPS) aims at retrieving target persons from the image gallery through descriptive text queries. Despite remarkable progress in recent state-of-the-art approaches, previous works still struggle to efficiently extract discriminative features from multi-modal data. To address the problem of cross-modal fine-grained text-to-image, we proposed a novel Siamese Contrastive Language-Image Model (SiamCLIM).

Recently, although multispectral pedestrian detection has achieved remarkable performances, there is still a problem to be handled, position shift problem. Due to the problem, a pedestrian looks like existing in different positions between each modal image. Then, a single bounding box usually fails to capture an entire pedestrian properly in both modal images at the same time, which means it would not contain some parts of a pedestrian and includes noisy backgrounds instead.

Multimodal learning, particularly for pedestrian detection, has recently received emphasis due to its capability to function equally well in several critical autonomous driving scenarios such as low-light, night-time, and adverse weather conditions. However, in most cases, the training distribution largely emphasizes the contribution of one specific input that makes the network biased towards one modality.

Text-based person re-identification (ReID) aims to identify images of the targeted person from a large-scale person image database according to a given textual description. Most existing methods focus on establishing instance-level and word-region-level correspondences. However, these methods generally rely heavily on the similarity contributed by matched word-region pairs while neglecting the decisive mismatched word-region pairs, resulting in false-positive matching.

We propose an approach for enhancing pedestrian detection in thermal infrared images using paired visible-thermal images in training. Recently, approaches that retrieve the corresponding visible features from thermal features using a key-value memory network have been proven effective for improving detection results. However, for memory networks storing thermal-visible features, random initialization and end-to-end training may not be ideal, as this can reduce the diversity of memory slots.

Unsupervised person re-identification (Re-ID) aims to retrieve person images across cameras without any identity labels. Most clustering-based methods roughly divide image features into clusters and neglect the feature distribution noise caused by domain shifts among different cameras, leading to inevitable performance degradation. To address this challenge, we propose a novel label refinement framework with clustering intra-camera similarity.

Multi-person tracking is often solved with the tracking-by-detection (TBD) paradigm. So-far tracked targets are matched to new detections on the basis of the current track states including motion or appearance information. If tracks are updated with inaccurate detections or unreliable appearance features, e.g., due to occlusion, the track states can become distorted leading to errors in the association.

Accurate segmentation of the cervical vertebrae in CT images can assist clinicians in analyzing the adolescent patient’s growth and development and establishing an effective orthodontic plan. This work develops an enhanced U2-Net architecture for fully automatic cervical vertebrae segmentation in CT images.

This paper presents a deep learning framework for medical video segmentation. Convolution neural network (CNN) and transformer-based methods have achieved great milestones in medical image segmentation tasks due to their incredible semantic feature encoding and global information comprehension abilities. However, most existing approaches ignore a salient aspect of medical video data - the temporal dimension.