Due to the significant differences between unmanned aerial vehicle (UAV) images and natural scene images in terms of lighting, scale, and viewing angle, existing multispectral detection techniques often fail to fully utilize the remote dependencies between global and local information, resulting in poor performance in complex UAV scenarios. In this paper, we propose a novel two-branch cross-modal fusion network that integrates a dual cross attention transformer fusion block (CTF) for global feature dependency and an adaptive mask convolution fusion block (MCF) for underlying feature extraction. This achieves a unified representation with both global and local receptive fields. Our local-global training strategy utilizes a shallow global fusion network and a deep local fusion network, which operate on the entire image while also focusing on detailed local features. Additionally, we integrate an asymptotic feature pyramid network that employs adaptive spatial fusion to refine features, enhancing the accuracy of small object detection in UAV scenes. Evaluating our work with the DroneVehicle dataset for vehicle detection using infrared and visible light, our network outperformed existing methods, improving [email protected] by 7.01% compared to CAL-Net. APs for YOLOv8m-based single-modal infrared detection and visible light detection increased by 3.1% and 11.6%, respectively.

Recent parameter-efficient fine-tuning (PEFT) techniques have enabled large language models (LLMs) to be efficiently fine-tuned for specific tasks, while maintaining model performance with minimal additional trainable parameters. However, existing PEFT techniques continue to face challenges in balancing both accuracy and efficiency, especially when addressing scalability and the demands of lightweight deployment for LLMs. In this paper, we propose an efficient fine-tuning method of LLMs based on dual quantized Tensor-Train adaptation with decoupling magnitude-direction (DuQTTA). The proposed DuQTTA method employs Tensor-Train decomposition and dual-stage quantization to minimize model size and resource consumption. Additionally, it employs an adaptive optimization strategy and a decoupled update mechanism to improve model performance, thereby minimizing suboptimal outcomes and ensuring alignment with the full-parameter fine-tuning goals. Experimental results indicate that the proposed DuQTTA method outperforms existing PEFT methods, achieving up to a $65 \times$ compression rate compared to the LLaMA2-7B models, meanwhile delivering improvements of $4.44 \%, 3.14 \%$, and 0.97% over LoRA on LLaMA2-7B, LLaMA3-8B, and LLaMA2-13B, respectively. The proposed DuQTTA method is effective in compressing LLMs for deployment on resource-constrained edge devices.

Text-to-image (T2I) models have garnered significant attention for generating high-quality images aligned with text prompts. However, rapid T2I model advancements reveal limitations in early benchmarks, lacking comprehensive evaluations, especially for text rendering and style. Notably, recent state-of-the-art models, with their rich knowledge modeling capabilities, show potential in reasoning-driven image generation, yet existing evaluation systems have not adequately addressed this frontier. To systematically address these gaps, we introduce $\textbf{OneIG-Bench}$, a meticulously designed comprehensive benchmark framework for fine-grained evaluation of T2I models across multiple dimensions, including subject-element alignment, text rendering precision, reasoning-generated content, stylization, and diversity. By structuring the evaluation, this benchmark enables in-depth analysis of model performance, helping researchers and practitioners pinpoint strengths and bottlenecks in the full pipeline of image generation. Our codebase and dataset are now publicly available to facilitate reproducible evaluation studies and cross-model comparisons within the T2I research community.

Anomaly detection in videos is a long-standing and challenging problem. Previous methods often adopt deep and large neural networks to achieve the best detection accuracy; however, the high computational costs prevent them from being used in real-world applications with constrained computational resources. In this paper, we develop a mem ory- a ugmented tr ansformer named MemATr, which is capable of detecting video anomalies effectively. The proposed network is lightweight and can be easily deployed on mobile devices. Furthermore, we propose a memory transformer module to make predictions that are closer to normal inputs, thereby leading to a higher error for abnormal input patterns. Memory-attention is the main component of the proposed memory transformer, which can retrieve the features from learnable values rather than from the backbone like previous methods. Extensive experiments on the UCSD Ped2, CUHK Avenue, and ShanghaiTech benchmarks can demonstrate that our model has a significantly smaller model size while still achieving competitive detection accuracy. Our model has only 1/12 the number of parameters of the baseline model. Besides, our model achieves a 4.6% increase in accuracy on the ShanghaiTech dataset and has roughly the same accuracy compared with the baseline on the other two datasets. We validate the performance of the proposed model on the mobile device and the result shows it only has 49.8ms latency. The effectiveness of the proposed method on mobile devices is further supported by experimental results. A new quantitative parameter AMD (Applicability for Mobile Devices) is proposed to offer a novel approach to assist in making trade-offs for mobile devices. The proposed model obtains state-of-the-art results in terms of AMD.

With the reducing size of fingerprint collection modules in mobile devices, partial fingerprints are increasingly characterized by smaller overlapping areas and higher self-similarity. Existing methods either aggregate similarity scores from individual Single-to-Single recognition or directly employ a Single-to-Multiple network to verify the match between the query and templates. However, these methods either lack sufficient interaction between templates, or fail to provide adequate supervision for the alignment process, a crucial step in fingerprint recognition, thereby limiting overall accuracy. In this paper, we propose a novel partial fingerprint recognition strategy termed Calibrated Single-to-Multiple (CaS2M), which first calibrates template fingerprints individually, then combines them with the query fingerprint in a matcher network for feature fusion. Building upon this strategy, we develop a dual-stage framework tailored to real-world applications. During enrollment, a lightweight patch-based feature indexing algorithm and a template selection strategy are employed accounting for limited hardware resources. For authentication, independent calibration is first applied, followed by an attention-based matcher network to verify identity consistency. Experimental results on multiple public datasets (NIST 302, NIST SD4, SpoofGAN, FVC2002 DB1A & DB3A) and a self-build dataset demonstrate that our framework achieves superior performance over state-of-the-art algorithms, providing new insights for multi-template partial fingerprint recognition.