Zero-shot instance-level sketch retrieval addresses a practical retrieval scenario in which sketches from unseen categories during training serve as queries to retrieve matching RGB images. The core challenges of this task lie in two aspects: unknown category generalization and subjective style adaptation. Existing methods either focus solely on category generalization or apply simplistic style elimination techniques within a specific category, leading to suboptimal performance when both challenges are present. To this end, we propose the Dual-Attentive Prompt (DAP) method, which unifies category generalization and style adaptation into a single, interpretable framework. Central to DAP is a dual-attentive prompt composer, consisting of two self-attention-based modules. This composer dynamically integrates pre-learned category-specific knowledge with instance-specific prompts that adapt to sketch-specific styles. By cooperating with additional style alignment loss, the proposed method ensures robust generalization of unseen categories while mitigating the impact of subjective style variations. Extensive experimental results demonstrate the state-of-the-art performance of the proposed method. Additionally, some insights are provided into the challenges of traditional training processes when handling multi-style sketches, along with quantitative and qualitative evidence showing how the proposed approach effectively mitigates the negative impact of subjective style variations.

Object re-identification (or object re-id) has gained significant attention in recent years, fueled by the increasing demand for advanced video analysis and safety systems. In object re-id, a query can be of different modalities, such as an image, a video, or natural language, containing or describing the object of interest. This workshop aims to bring together researchers, practitioners, and enthusiasts interested in object re-id to delve into the latest advancements, challenges, and opportunities in this dynamic field. The workshop covers a spectrum of topics related to object re-id, including but not limited to deep metric learning, multi-view data generation, video-based object re-id, cross-domain object re-id and real-world applications. The workshop provides a platform for researchers to showcase their work, exchange ideas, and foster potential collaborations. Additionally, it serves as a valuable opportunity for practitioners to stay abreast of the latest developments in object re-id technology.

Cover Caption: The cover image is based on the Research Article Efficient virtual data search for annotationfree vehicle reidentification by Zhijing Wan et al., https://doi.org/10.1002/int.22829.

Vehicle reidentification (re-ID) is the task of retrieving the same vehicle across nonoverlapping cameras, which has made significant progress with the help of abundant manually annotated real images. To avoid the time-consuming and tedious labeling of real images, virtual data sets with large-scale synthetic images have recently been constructed to perform annotation-free model training. However, current methods fail to exploit the potential of virtual data search, that is, searching valuable and representative virtual subdata set for efficient training. This paper presents a novel data sampling strategy from both semantic and feature levels to perform an effective data search. The semantic level determines the sample number of each vehicle identity via the consistency constraint of attribute distribution for source domain and target domain; while the feature level searches valuable and representative samples of each vehicle identity. To our knowledge, we are among the first attempts to search effective virtual data to perform annotation-free vehicle re-ID. Extensive cross-domain experiments from virtual vehicle re-ID data sets to real vehicle re-ID data sets show that our data sampling strategy can significantly reduce the training data volume and even boost the re-ID performance.

It is common that TV audiences want to quickly browse scenes with certain actors in TV series. Since 2016, the TREC Video Retrieval Evaluation (TRECVID) Instance Search (INS) task has started to focus on identifying a target person in a target scene simultaneously. In this paper, we name this kind of task as P-S INS (Person-Scene Instance Search). To find out P-S instances, most approaches search person and scene separately, and then directly combine the results together by addition or multiplication. However, we find that person and scene INS modules are not always effective at the same time, or they may suppress each other in some situations. Aggregating the results shot after shot is not a good choice. Luckily, for the TV series, video shots are arranged in chronological order. We extend our focus from time point (single video shot) to time slice (multiple consecutive video shots) in the time-line. Through detecting salient time slices, we prune the data. Through evaluating the importance of salient time slices, we boost the aggregation results. Extensive experiments on the large-scale TRECVID INS dataset demonstrate the effectiveness of the proposed method.

Searching for specific persons from surveillance videos captured by different cameras, is a key yet under-addressed challenge in multimedia system. Related person retrieval works mainly focus on searching person by visual appearance, known as person re-identification. However, the initial visual image may not be available in some practical applications. For example, the criminal is described by a text description indirectly, "A young woman wearing a red casual with a backpack", the traditional methods can not conquer this issue. Based on a set of pre-defined attributes that the text description query can be transformed to an attribute vector, thus can be used to retrieval in the gallery set. And yet, the user-provided attributes are sometimes incomplete. This new issue is defined as Specific Person Retrieval via Incomplete Text Description. In this paper, we conduct a specific attribute completion to enrich the original text query and generate a more expressive attribute vector. Then, a pairwise-based metric learning is introduced for completed attribute vectors. Extensive experiments conducted on two benchmark datasets have shown our superior performance.