In recent years, CNNs have demonstrated remarkable success in medical image processing due to their powerful feature extraction capabilities. However, their inherent limitation hampers their ability to learn global features. In contrast, Transformers excel at modeling long-range dependencies, offering a solution to this limitation. Yet, the computational demands of self-attention make high-resolution medical image processing-particularly for 3D data-extremely resource-intensive. To address these challenges, we introduce DeTransNet, a hybrid segmentation method that harnesses the strengths of both CNNs and deformable Transformer. First, we introduce a multi-channel feature fusion module (MCFF) that efficiently integrates multi-scale features from CNNs to enhance the input for the Transformer network. Additionally, we propose a deformable attention mechanism to improve feature extraction, enabling the model to capture global information while reducing self-attention complexity. Extensive experiments on the public BCV dataset demonstrate that the proposed method outperforms existing methods.
ChatGPT faces significant challenges in realising the dynamic construction of reality, cognition and language emphasised in Embodied-cognitive translatology. This study first examines the translation application of ChatGPT, defines hallucination phenomena, and reviews related research. It then explores the hallucination phenomena in ChatGPT's translation tasks, categorizing them into semantic, pragmatic, syntactic, and cultural hallucinations. To quantify these manifestations, we manually annotate ChatGPT's English-Chinese translations on the Flores-101 dataset, providing statistical insights into the frequency and severity of each type of hallucination. This analysis offers clarity on the prevalence of various hallucinations and suggests directions for improvement. Finally, this study summarises the current research on hallucination mitigation strategies in a targeted way, including the improvement of corpus quality, model architecture development, and prompt engineering techniques, offering novel insights for advancing machine translation technology towards greater intelligence.
Alzheimer's disease (AD) is a common and dangerous disorder that primarily impacts older adults, early detection is crucial, and diagnostic tools like PET and MRI have an impact on offering detailed anatomical and metabolic insights, respectively. However, traditional methods usually directly concatenate the two modal data by channel, which fails to fully utilize the complementary information provided by MRI and PET data. Hence, this paper introduces a new multi-modal dynamic information selection framework to enhance the accuracy of AD classification. It includes three main modules: a dual ResNet50-based feature extraction module; a modal fusion module containing a feature pyramid for finer detail extraction and a cross-attention mechanism to integrate modal information; a dynamic information selection module that evaluates the data content across modalities to optimize decision-making. The outcome on the ADNI dataset confirm the efficiency of the proposed approach.
Research on adversarial attacks for facial recognition identifies and mitigates vulnerabilities in facial recognition systems, enhancing their security. Current adversarial attacks targeting facial attributes lack precision in controlling specific attributes, often altering multiple facial features instead of a single target attribute. Such imprecise modifications reduce the attack's effectiveness and stealthiness. To address these issues, this paper proposes a method for generating adversarial examples based on multi-layer feature map fusion. First, we design a framework called StarAdv, which uses fused multi-layer feature maps with high concealment to achieve realistic facial image transformations under different attributes. This approach generates adversarial images visually similar to the originals while possessing specific misleading properties, enabling transferable attacks on facial recognition systems. Second, we introduce a Multi-layer Fusion module that captures weight information from each layer of the network's residual blocks to adaptively fuse feature maps from different layers, producing high-concealment feature maps. Finally, we propose a Bilinear Feature Map Interpolation (BFMI) algorithm to interpolate the high-concealment features with the original image features, ensuring the final decoded adversarial samples maintain a natural appearance, further reducing the likelihood of detection. We conduct comprehensive experiments on the CelebA dataset, demonstrating that the adversarial facial images generated by our method possess semantic plausibility and authenticity in appearance and achieve a high attack success rate in both white-box and black-box settings.
In response to the current challenges of large parameter size and low detection accuracy in SAR ship detection models, this paper proposes an improved YOLOv8 model. The model integrates the Swin-Transformer architecture into YOLOv8 through an adaptive feature fusion method, enhancing the model’s global information perception and detection accuracy. Additionally, it employs a top-down unidirectional semantic pyramid and a lightweight detection head structure to achieve model lightweighting. Experimental comparisons on the HRSID dataset show that, compared to the benchmark algorithm, the proposed algorithm increases mAP50 by 1.2
