Occluded person re-identification (Re-ID) focuses on addressing the occlusion problem when retrieving the person of interest across non-overlapping cameras. With the increasing demand for intelligent video surveillance and the application of person Re-ID technology, the real-world occlusion problem draws considerable interest from researchers. Although a large number of occluded person Re-ID methods have been proposed, there are few surveys that focus on occlusion. To fill this gap and help boost future research, this paper provides a systematic survey of occluded person Re-ID. In this paper, we review recent deep learning-based occluded person Re-ID researches. Firstly, we summarize the main issues caused by occlusion as four groups, i.e., position misalignment, scale misalignment, noisy information, and missing information. Secondly, we categorize existing methods into six solution groups, i.e., matching, image transformation, multi-scale features, attention mechanism, auxiliary information, and contextual recovery. We also discuss the characteristics of each approach, as well as the issues they addressed. Furthermore, we present the performance comparison of recent occluded person Re-ID methods on four public datasets: Partial-ReID, Partial-iLIDS, Occluded-ReID, and Occluded-DukeMTMC. We conclude the study with thoughts into promising future research directions.

Gait recognition captures gait patterns from the walking sequence of an individual for identification. Most existing gait recognition methods learn features from silhouettes or skeletons for the robustness to clothing, carrying, and other exterior factors. The combination of the two data modalities, however, is not fully exploited. Previous multimodal gait recognition methods mainly employ the skeleton to assist the local feature extraction where the intrinsic discrimination of the skeleton data is ignored. This paper proposes a simple yet effective Bimodal Fusion (BiFusion) network which mines discriminative gait patterns in skeletons and integrates with silhouette representations to learn rich features for identification. Particularly, the inherent hierarchical semantics of body joints in a skeleton is leveraged to design a novel Multi-Scale Gait Graph (MSGG) network for the feature extraction of skeletons. Extensive experiments on CASIA-B and OUMVLP demonstrate both the superiority of the proposed MSGG network in modeling skeletons and the effectiveness of the bimodal fusion for gait recognition. Under the most challenging condition of walking in different clothes on CASIA-B, our method achieves the rank-1 accuracy of 92.1%.

Recent advances in vision-language pre-training have significantly enhanced the model capabilities on grounded object detection. However, these studies often pre-train with coarse-grained text prompts, such as plain category names and brief grounded phrases. This limitation curtails the model's capacity for fine-grained linguistic comprehension and leads to a significant decline in performance when faced with detailed descriptions or contextual information. To tackle these problems, we develop DoGA: Detect objects with Grouped Attributes, which employs commonly apparent attributes to bridge different granular semantics and uses specific attributes to identify the object discrepancy. Our DoGA incorporates three principle components: 1) Generation of attribute-based prompts, consisting of linguistic definitions enriched with common-sense visible attributes and hard negative notations deriving from the image-specific attribute features; 2) Paralleled entity fusion and optimization, designed to manage long attribute-based descriptions and negative concepts efficiently; and 3) Prompt-wise grouped training to accommodate model to perform many-to-many assignments, facilitating simultaneous training and inferring with multiple attribute-based synonyms. Extensive experiments demonstrate that training with synonymous attribute-based prompts allows DoGA to generalize multi-granular prompts and surpass previous state-of-the-art approaches, yielding 50.2 on the COCO and 38.0 on the LVIS benchmarks under the zero-short setting. We will make our code publicly available upon acceptance.