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Found 9 papers, 5 papers with code
Jailbreaking the Non-Transferable Barrier via Test-Time Data Disguising
Non-transferable learning (NTL) has been proposed to protect model intellectual property (IP) by creating a "non-transferable barrier" to restrict generalization from authorized to unauthorized domains.
Toward Robust Non-Transferable Learning: A Survey and Benchmark
While numerous methods have been proposed in this field, a comprehensive review of existing progress and a thorough analysis of current limitations remain lacking.
Visual-Augmented Dynamic Semantic Prototype for Generative Zero-Shot Learning
Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for unseen classes, which is an effective way to advance ZSL.
Your Transferability Barrier is Fragile: Free-Lunch for Transferring the Non-Transferable Learning
Motivated by these findings we uncover the potential risks of NTL by proposing a simple but effective method (dubbed as TransNTL) to recover the target domain performance with few source domain data.
Evolving Semantic Prototype Improves Generative Zero-Shot Learning
After alignment, synthesized sample features from unseen classes are closer to the real sample features and benefit DSP to improve existing generative ZSL methods by 8. 5\%, 8. 0\%, and 9. 7\% on the standard CUB, SUN AWA2 datasets, the significant performance improvement indicates that evolving semantic prototype explores a virgin field in ZSL.
MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning
Prior works either simply align the global features of an image with its associated class semantic vector or utilize unidirectional attention to learn the limited latent semantic representations, which could not effectively discover the intrinsic semantic knowledge e. g., attribute semantics) between visual and attribute features.
TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning
Analogously, VAT uses the similar feature augmentation encoder to refine the visual features, which are further applied in visual$\rightarrow$attribute decoder to learn visual-based attribute features.
TransZero: Attribute-guided Transformer for Zero-Shot Learning
Although some attention-based models have attempted to learn such region features in a single image, the transferability and discriminative attribute localization of visual features are typically neglected.
View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums
Different frequency bands in the trajectory spectrums could hierarchically reflect agents' motion preferences at different scales.
Ranked #2 on
Trajectory Prediction
on ETH/UCY
