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ContextBLIP: Doubly Contextual Alignment for Contrastive Image Retrieval from Linguistically Complex Descriptions
The main challenges lie in aligning key contextual cues in two modalities, where these subtle cues are concealed in tiny areas of multiple contrastive images and within the complex linguistics of textual descriptions.
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis
The high dimensionality and complexity of neuroimaging data necessitate large datasets to develop robust and high-performing deep learning models.
Clicking Matters:Towards Interactive Human Parsing
In this work, we focus on Interactive Human Parsing (IHP), which aims to segment a human image into multiple human body parts with guidance from users' interactions.
Improved Detection of Adversarial Images Using Deep Neural Networks
Machine learning techniques are immensely deployed in both industry and academy.
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