Machine Learning

Advancements in Medical Vision-Language Pre-training (Medical-VLP) progress rapidly by learning representations from paired radiology reports. Nevertheless, there still remain two issues that restrict the development of Medical-VLP: the scarcity of parallel image-report pair and monotony of pre-training tasks. Thus, we propose Multi-Grained Cross-Domain Report Searching (CDRS) strategy, and Multi-Task Driven Language-Image Pre-Training (MLIP) framework.

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25 Views

The detection of the collapse of landslides trigerred by intense natural hazards, such as earthquakes and rainfall, allows rapid response to hazards which turned into disasters. The use of remote sensing imagery is mostly considered to cover wide areas and assess even more rapidly the threats. Yet, since optical images are sensitive to cloud coverage, their use is limited in case of emergency response. The proposed dataset is thus multimodal and targets the early detection of landslides following the disastrous earthquake which occurred in Haiti in 2021.

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96 Views

With the advent of decentralized social media platforms such as Bluesky Social (hereafter referred to as Bluesky), there is official support for the public disclosure of user behaviors with millisecond-level timestamps. Rooted in Bluesky's principles of open-source and open-data, our study introduces the first integration of the temporal dynamics of user-driven social interactions into a multi-network. This includes interactions such as user-to-user (following and blocking) and user-to-community (creating and joining).

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642 Views

Machine learning (ML) in the medical domain faces challenges due to limited high-quality data. This study addresses the scarcity of echocardiography images (echoCG) by generating synthetic data using state-of-the-art generative models. We evaluated a cycle-consistent generative adversarial network (CycleGAN), contrastive unpaired translation (CUT) method, and latent diffusion model (Stable Diffusion 1.5).

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33 Views

A dataset related to a cable under anomalies conditions while a transmitter (AWG) sends a binary PAM signal to a receiver. The signals are acquired by an oscilloscope. The anomalies were manually forced on the cable under test: air-exposed, water-exposed conductors, and tapping. In the dataset, the signals are also available for normal cable.

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189 Views

We are pleased to introduce the Qilin Watermelon Dataset, a unique collection of data aimed at investigating the relationship between a watermelon's appearance, tapping sound, and sweetness. This dataset is the result of our dedicated efforts to capture and record various aspects of Qilin watermelons, a special variety known for its exceptional taste and quality.

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214 Views

In our work, we propose an innovative system to accurately infer and track occluded target locations using mmWave beat frequency signals. Our approach combines a classic direction-finding method with advanced deep learning techniques, specifically a convolutional neural network (CNN), to enhance detection capabilities. The dataset includes raw beat frequency signal data from the TI IWR6843ISK rev B with TI mmWAVEICBOOST and the TI DCA1000EVM capture board. Corresponding ground truth data (target position) from the Realsense L515 RGB-D camera is also provided.

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112 Views

As with most AI methods, a 3D deep neural network needs to be trained to properly interpret its input data. More specifically, training a network for monocular 3D point cloud reconstruction requires a large set of recognized high-quality data which can be challenging to obtain. Hence, this dataset contains the image of a known object alongside its corresponding 3D point cloud representation. To collect a large number of categorized 3D objects, we use the ShapeNetCore (https://shapenet.org) dataset.

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385 Views

The dataset exemplifies land vehicle targets, tanks, and comprises 1000 time-frequency representation (TFR) images in jpg format with a resolution of 875x656 pixels. Each image is accompanied by labels containing 14 parameters for geometric parameter prediction.

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17 Views

The advancement of machine and deep learning methods in traffic sign detection is critical for improving road safety and developing intelligent transportation systems. However, the scarcity of a comprehensive and publicly available dataset on Indian traffic has been a significant challenge for researchers in this field. To reduce this gap, we introduced the Indian Road Traffic Sign Detection dataset (IRTSD-Datasetv1), which captures real-world images across diverse conditions.

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326 Views

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