In this paper, we propose an effective and robust decentralized tracking scheme based on the square root cubature information filter (SRCIF) to balance the energy consumption and tracking accuracy in wireless camera sensor networks (WCNs). More specifically, regarding the characteristics and constraints of camera nodes in WCNs, some special mechanisms are put forward and integrated in this tracking scheme. First, a decentralized tracking approach is adopted so that the tracking can be implemented energy-efficiently and steadily. Subsequently, task cluster nodes are dynamically selected by adopting a greedy on-line decision approach based on the defined contribution decision (CD) considering the limited energy of camera nodes. Additionally, we design an efficient cluster head (CH) selection mechanism that casts such selection problem as an optimization problem based on the remaining energy and distance-to-target. Finally, we also perform analysis on the target detection probability when selecting the task cluster nodes and their CH, owing to the directional sensing and observation limitations in field of view (FOV) of camera nodes in WCNs. From simulation results, the proposed tracking scheme shows an obvious improvement in balancing the energy consumption and tracking accuracy over the existing methods.

Creating realistic, fully animatable whole-body avatars from a single portrait is challenging due to limitations in capturing subtle expressions, body movements, and dynamic backgrounds. Current evaluation datasets and metrics fall short in addressing these complexities. To bridge this gap, we introduce the Whole-Body Benchmark Dataset (WB-DH), an open-source, multi-modal benchmark designed for evaluating whole-body animatable avatar generation. Key features include: (1) detailed multi-modal annotations for fine-grained guidance, (2) a versatile evaluation framework, and (3) public access to the dataset and tools at https://github.com/deepreasonings/WholeBodyBenchmark.

Currently, various studies have been exploring generation of long videos. However, the generated frames in these videos often exhibit jitter and noise. Therefore, in order to generate the videos without these noise, we propose a novel framework composed of four modules: separate tuning module, average fusion module, combined tuning module, and inter-frame consistency module. By applying our newly proposed modules subsequently, the consistency of the background and foreground in each video frames is optimized. Besides, the experimental results demonstrate that videos generated by our method exhibit a high quality in comparison of the state-of-the-art methods.

Acoustic and image sensors in unattended ground sensor systems often operate with different sampling rates, trigger mechanisms, and fields of view, so their observations are asynchronous and lack instance-level correspondence. In addition, web images used to supplement scarce field annotations exhibit a substantial domain shift from target-domain images. This paper proposes a three-stream joint learning framework that integrates reliability-weighted image-domain adaptation, reliability-aware dynamic bidirectional prototype distillation, and balanced complementary modality dropout. A lightweight shared backbone processes labeled acoustic spectra, labeled mixed-source images, and unlabeled target-domain images. Target pseudo-label reliability is estimated by a main classifier and two auxiliary classifiers and is used to weight marginal and class-conditional distribution alignment. Acoustic and image class prototypes transfer category-level knowledge without constructing artificial instance pairs, while direction-specific distillation weights are dynamically assigned according to classification reliability, prototype availability, and prototype stability. Experiments on 148,038 acoustic spectra and 167,754 images yield acoustic, image, and overall accuracies of 92.02\%, 95.26\%, and 93.79\%, respectively, exceeding the best corresponding baseline results by 1.49, 1.24, and 2.47 percentage points. The deployment model contains 123.40 K parameters and requires 0.47 MiB in FP32, supporting resource-constrained field deployment.

Acoustic and image sensors in unattended ground sensor systems often operate at different sampling rates and use different trigger mechanisms and fields of view, so their observations are asynchronous and lack instance-level correspondence. In addition, web images used to supplement scarce field annotations exhibit a substantial domain shift from target-domain images. This paper proposes a three-stream joint learning framework that integrates reliability-weighted image-domain adaptation, reliability-aware dynamic bidirectional prototype distillation, and balanced complementary modality dropout. A lightweight shared backbone processes labeled acoustic spectra, labeled mixed-source images, and unlabeled target-domain images. A main classifier and two auxiliary classifiers estimate target pseudo-label confidence and agreement. Their consensus is used to construct reliability weights for marginal alignment, while confidence guides target-sample selection and weighting for class-conditional alignment. Acoustic and image class prototypes transfer category-level knowledge without constructing artificial instance pairs, while direction-specific distillation weights are dynamically assigned according to classification reliability, prototype availability, and prototype stability. Across three independent runs with different random seeds, experiments on datasets comprising 148,038 acoustic spectra and 167,754 images yield acoustic, image, and overall accuracies of $91.97\pm 0.08$%, $95.16\pm 0.14$%, and $93.72\pm 0.07$%, respectively, exceeding the highest corresponding baseline mean accuracies by 1.52, 1.30, and 2.31 percentage points. The deployment model contains 123.40 K parameters and requires 0.47 MiB in FP32, making it a compact classification model with potential for deployment in resource-constrained field environments.