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Found 6 papers, 3 papers with code
Enhancing Lidar-based Object Detection in Adverse Weather using Offset Sequences in Time
Our findings demonstrate that our novel method, involving temporal offset augmentation through randomized frame skipping in sequences, enhances object detection accuracy compared to both the baseline model (Pillar-based Object Detection) and no augmentation.
Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers
Furthermore, to intensify the importance of the boundary information, we impose an additional attention map by creating a Gaussian pyramid on top of the HF components.
Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review
The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision.
Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data Sets
Recent advances in computer vision have led to significant progress in the generation of realistic image data, with denoising diffusion probabilistic models proving to be a particularly effective method.
Deep Hashing with Hash Center Update for Efficient Image Retrieval
The first loss, maximizes the correlation between the hash centers and learned hash codes.
Optimized Feature Space Learning for Generating Efficient Binary Codes for Image Retrieval
Classical approach of Linear Discriminant Analysis (LDA) is generally used for generating an optimized low dimensional feature space for single-labeled images.
