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Found 5 papers, 2 papers with code
Good Questions Help Zero-Shot Image Reasoning
QVix enables a wider exploration of visual scenes, improving the LVLMs' reasoning accuracy and depth in tasks such as visual question answering and visual entailment.
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach
In experiments, we show that our method consistently brings non-trivial improvements to the three aforementioned learning tasks from both efficiency and final performance, either or not combined with strong pre-defined augmentations, e. g., on medical images when domain knowledge is unavailable and the existing augmentation techniques perform poorly.
Class-Disentanglement and Applications in Adversarial Detection and Defense
In this paper, we propose ''class-disentanglement'' that trains a variational autoencoder $G(\cdot)$ to extract this class-dependent information as $x - G(x)$ via a trade-off between reconstructing $x$ by $G(x)$ and classifying $x$ by $D(x-G(x))$, where the former competes with the latter in decomposing $x$ so the latter retains only necessary information for classification in $x-G(x)$.
Identity-Disentangled Adversarial Augmentation for Self-supervised Learning
We then adversarially perturb $G(x)$ in the VAE's bottleneck space and adds it back to the original $R(x)$ as an augmentation, which is therefore sufficiently challenging for contrastive learning and meanwhile preserves the sample identity intact.
Domain-Class Correlation Decomposition for Generalizable Person Re-Identification
Domain adversarial learning is a promising domain generalization method that aims to remove domain information in the latent representation through adversarial training.
Domain Generalization
Generalizable Person Re-identification
