The rapid advancement of deep neural network (DNN) models has enabled their widespread application across various domains, including face recognition and natural language processing. However, data-driven DNN models are prone to erroneous behavior when inadequately trained, necessitating extensive predictive labeling of test data to identify and mitigate defects. Manual labeling, however, remains both labor-intensive and inefficient.
The scarcity of multimodal datasets in remote sensing, particularly those combining high-resolution imagery with descriptive textual annotations, limits advancements in context-aware analysis. To address this, we introduce a novel dataset comprising 12,473 aerial and satellite images sourced from established benchmarks (RSSCN7, DLRSD, iSAID, LoveDA, and WHU), enriched with automatically generated pseudo-captions and semantic tags.
A total of 1035 color Doppler US images of heart patients, who were suffering from MR has been collected from the department of cardiology, Swami Rama Himalayan University (SHRU), Dehradun, India. The US images (800 × 600 pixels) used for the analysis of MR were recorded by Philips US machine equipped with multi-frequency transducers of 2-5 MHz range. The images were collected in three different views, i.e., A2C, A4C and PLAX view.
This paper introduces the Chinese Social Media Autism Children Dataset (CSMACD), a novel resource for autism spectrum disorder (ASD) research. CSMACD compiles high-definition, unobstructed frontal facial images of Chinese children (aged 6 months to 15 years) with ASD, sourced from mainstream social media platforms (e.g., Bilibili, Douyin, and Tencent Video). Videos were identified using ASD-related keywords (e.g., "autism," "Star Baby") and recommendation algorithms.
Autism Spectrum disorder brain sMRI image
The rapid advancement of generative neural networks has facilitated the creation of photorealistic images, raising concerns about the proliferation of misinformation. Detecting AI-generated fakes has become crucial, given their potential impact on public opinion and various sectors. This dataset presents a comparative analysis of real and AI-generated images, focusing on building a novel dataset named Realistic AI-Generated Image (RealAIGI) dataset.
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Two images from Sentinel - 2 and Gaofen - 2, with resolutions of 10 m and 0.8 m respectively, underwent orthorectification and geometric correction. Then, using the Sentinel - 2 image as the reference, the Gaofen - 2 image was coregistered.Two images from Sentinel - 2 and Gaofen - 2, with resolutions of 10 m and 0.8 m respectively, underwent orthorectification and geometric correction.
Two images from Sentinel - 2 and Gaofen - 2, with resolutions of 10 m and 0.8 m respectively, underwent orthorectification and geometric correction. Then, using the Sentinel - 2 image as the reference, the Gaofen - 2 image was coregistered.
Two images from Sentinel - 2 and Gaofen - 2, with resolutions of 10 m and 0.8 m respectively, underwent orthorectification and geometric correction. Then, using the Sentinel - 2 image as the reference, the Gaofen - 2 image was coregistered.
Two images from Sentinel - 2 and Gaofen - 2, with resolutions of 10 m and 0.8 m respectively, underwent orthorectification and geometric correction. Then, using the Sentinel - 2 image as the reference, the Gaofen - 2 image was coregistered.
Two images from Sentinel - 2 and Gaofen - 2, with resolutions of 10 m and 0.8 m respectively, underwent orthorectification and geometric correction. Then, using the Sentinel - 2 image as the reference, the Gaofen - 2 image was coregistered.
