Locating and continuously tracking individuals in videos using natural-language descriptions is essential for human-AI collaboration, surveillance analytics, and video-based question answering. However, there are still three gaps: (i) although existing methods can reliably associate trajectories in most scenarios, they still fail to capture semantic understanding; (ii) large vision–language models (VLMs) grasp semantics but lack temporal identity stability; and (iii) person re-identification (ReID) excels at identity discrimination but ignores linguistic intent and often discards contextual cues. We present InstructTrack, an instruction-driven tracking agent that bridges these gaps. Using VLM backbone as a semantic hub, the video frame is parsed to localize the referred target, decide whether contextual cues are required, and extract initial semantic embeddings. The system then aligns VLM proposals with a lightweight detector via Hungarian matching to initialize or update track IDs. Subsequently, a context-gated ReID head learns identity and instruction relevant context embeddings and fuses them under language control; a tailored triplet objective jointly optimizes identity and context consistency. Integrated into an online MOT loop, InstructTrack delivers instruction-controllable, long-term person tracking, and single-video ReID. On MOT17 and MOT20, our method outperforms strong online baselines, achieving HOTA 68.4/68.4 and IDF1 86.1/81.6 while halving identity switches.

In this article, we study a distributed privacy-preserving learning problem in social networks with general topology. The agents can communicate with each other over the network, which may result in privacy disclosure, since the trustworthiness of the agents cannot be guaranteed. Given a set of options which yield unknown stochastic rewards, each agent is required to learn the best one, aiming at maximizing the resulting expected average cumulative reward. To serve the above goal, we propose a four-staged distributed algorithm which efficiently exploits the collaboration among the agents while preserving the local privacy for each of them. In particular, our algorithm proceeds iteratively, and in every round, each agent i) randomly perturbs its adoption for the privacy-preserving purpose, ii) disseminates the perturbed adoption over the social network in a nearly uniform manner through random walking, iii) selects an option by referring to the perturbed suggestions received from its peers, and iv) decides whether or not to adopt the selected option as preference according to its latest reward feedback. Through solid theoretical analysis, we quantify the trade-off among the number of agents (or communication overhead), privacy preserving and learning utility. We also perform extensive simulations to verify the efficacy of our proposed social learning algorithm.

In this paper, we propose a novel unsupervised color spatial pyramid matching (UCSPM) approach for person re-identification. It is well motivated by our study on spatial pyramid to build effective structural object representation for person re-identification. Through the combination of illumination invariance color feature, UCSPM can well cope with the variations of viewpoint, illumination and pose. First, local superpixel regions are divided to accurately represent the color feature. Second, human body are divided into increasing fine vertical sub-regions to construct the spatial pyramid matching scheme. Third, the color feature and its spatial distribution information are used in a pyramid match kernel for calculating the similarity between person and person. The effectiveness of our approach is validated on the VIPeR dataset and CUHK campus dataset. Comparing with other approaches, our UCSPM improves the best unsupervised rank-1 matching rate on the VIPeR dataset by 3.08% with only one kind of feature—color.

In this paper, we present a depth-extracting method on the scenes of 4D light fields. The method is based on image segmentation and epipolar plane images. We extract disparity map and reliability map from the original image of 4D light fields. Then this information is applied to image segmentation, in which a large number of planes are produced, so that the disparity map which is consist of pixels can be transferred to the disparity map which is consist of planes. In the resulting optimization problem, graph-cut technique is used to assign a corresponding disparity plane to each segment. Our method is tested on a number of synthetic and real-world examples captured with a light field camera, and compared to ground truth where available. Furthermore, an approach to optimize the method to reduce the running time is also proposed.