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Found 5 papers, 3 papers with code
Human-imperceptible, Machine-recognizable Images
Massive human-related data is collected to train neural networks for computer vision tasks.
Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification
This could significantly reduce the efficiency of a large family of few-shot learning algorithms, which have limited data and highly rely on the comparison of image patches.
Text-to-Image Synthesis Based on Object-Guided Joint-Decoding Transformer
Object-guided text-to-image synthesis aims to generate images from natural language descriptions built by two-step frameworks, i. e., the model generates the layout and then synthesizes images from the layout and captions.
Collect and Select: Semantic Alignment Metric Learning for Few-Shot Learning
Empirical results demonstrate that semantic alignment is achieved.
Anchor-based Nearest Class Mean Loss for Convolutional Neural Networks
Discriminative features are obtained by constraining the deep CNNs to map training samples to the corresponding anchors as close as possible.
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