Best of CVPR

As AI becomes more prevalent in fields like healthcare, ensuring its reliability under unexpected inputs is essential. We present OpenMIBOOD, a benchmarking framework for evaluating out-of-distribution (OOD) detection methods in medical imaging. It includes 14 datasets across three medical domains and categorizes them into in-distribution, near-OOD, and far-OOD groups to assess 24 post-hoc methods. Results show that OOD detection approaches effective in natural images often fail in medical contexts, highlighting the need for domain-specific benchmarks to ensure trustworthy AI in healthcare.

Semi-supervised medical image segmentation often suffers from class imbalance and high uncertainty due to pathology variability. We propose DyCON, a Dynamic Uncertainty-aware Consistency and Contrastive Learning framework that addresses these challenges via two novel losses: UnCL and FeCL. UnCL adaptively weights voxel-wise consistency based on uncertainty, initially focusing on uncertain regions and gradually shifting to confident ones. FeCL improves local feature discrimination under imbalance by applying dual focal mechanisms and adaptive entropy-based weighting to contrastive learning.

The choice of representation for geographic location significantly impacts the accuracy of models for a broad range of geospatial tasks, including fine-grained species classification, population density estimation, and biome classification. Recent works learn such representations by contrastively aligning geolocation[lat,lon] with co-located images. While these methods work exceptionally well, in this paper, we posit that the current training strategies fail to fully capture the important visual features. We provide an information-theoretic perspective on why the resulting embeddings from these methods discard crucial visual information that is important for many downstream tasks. To solve this problem, we propose a novel retrieval-augmented strategy called RANGE. We build our method on the intuition that the visual features of a location can be estimated by combining the visual features from multiple similar-looking locations. We show this retrieval strategy outperforms the existing state-of-the-art models with significant margins in most tasks.