PRISM: Diversifying Dataset Distillation by Decoupling Architectural Priors

A new framework named CoEvo has been proposed for zero-shot out-of-distribution (OOD) detection in vision-language models, addressing the challenges posed by the absence of labeled negatives. CoEvo employs a bidirectional adaptation mechanism for both textual and visual proxies, dynamically refining them based on contextual information from test images. This innovation aims to enhance the reliability of OOD detection in open-world applications.

A new method called PRISM has been introduced to optimize the selection of training data for Multimodal Large Language Models (MLLMs), addressing the redundancy in rapidly growing datasets that increases computational costs. This self-pruning intrinsic selection method aims to enhance efficiency without the need for extensive training or proxy-based inference techniques.

The introduction of the Diffusion-Guided Autoencoder (DGAE) marks a significant advancement in latent representation learning, enhancing the decoder's expressiveness and effectively addressing training instability associated with GANs. This model achieves state-of-the-art performance while utilizing a latent space that is twice as compact, thus improving efficiency in image and video generative tasks.