Spatial-and-Frequency-aware Restoration method for Images based on Diffusion Models

The paper presents a novel approach to unsupervised image classification, focusing on clustering unlabeled images into meaningful categories. The method, named Image Clustering through Cluster Ensembles (ICCE), enhances clustering performance by integrating adaptive nearest neighbor selection and cluster ensembling strategies. This approach allows for the training of multiple clustering heads on a fixed backbone, resulting in diverse clusterings that are consolidated into a unified consensus clustering.

BioBench is introduced as an open ecology vision benchmark that addresses the limitations of ImageNet in predicting performance on scientific imagery. It encompasses 9 application-driven tasks, 4 taxonomic kingdoms, and 6 acquisition modalities, totaling 3.1 million images. The benchmark aims to enhance ecological research by providing a unified platform for evaluating visual representation quality in ecological tasks.

The paper presents a novel approach to Visual Auto-Regressive (VAR) modeling, introducing VARiant, which optimizes memory usage by employing progressive training strategies. This method allows for flexible depth adjustments in the network, addressing the limitations of traditional multi-scale generation. By processing early scales with a full network and later scales with subnets, VARiant enhances efficiency while maintaining performance.

The paper presents ANTS, an innovative method for enhancing Out-of-Distribution (OOD) detection by utilizing Adaptive Negative Textual Space. By leveraging multimodal large language models (MLLMs), the approach generates expressive negative sentences that accurately characterize OOD distributions. This method addresses the limitations of existing techniques, particularly in near-OOD detection, by caching images likely to be OOD samples and prompting MLLMs for detailed descriptions.

InvFusion is a novel approach that integrates supervised and zero-shot diffusion methods for solving inverse problems. It addresses the limitations of existing models by providing a degradation-aware posterior sampler that enhances accuracy while maintaining flexibility. This innovation is significant as it combines the strengths of both training-based and zero-shot techniques, marking a step forward in the application of diffusion models in various fields.

The paper introduces Expanding Autoregressive Representation (EAR), a new paradigm for visual generation that mimics the human visual system's center-outward perception. This method improves efficiency by unfolding image tokens in a spiral order, allowing for parallel decoding and preserving spatial continuity. Additionally, a length-adaptive decoding strategy is proposed to enhance flexibility and speed, ultimately reducing computational costs and improving generation quality.