Scaling Manifold Ranking Based Image Retrieval

Manifold Ranking is a graph-based ranking algorithm being successfully applied to retrieve images from multimedia databases. Given a query image, Manifold Ranking computes the ranking scores of images in the database by exploiting the relationships among them expressed in the form of a graph. Since Manifold Ranking effectively utilizes the global structure of the graph, it is significantly better at finding intuitive results compared with current approaches. Fundamentally, Manifold Ranking requires an inverse matrix to compute ranking scores and so needs O(n^3) time, where n is the number of images. Manifold Ranking, unfortunately, does not scale to support databases with large numbers of images. Our solution, Mogul, is based on two ideas: (1) It efficiently computes ranking scores by sparse matrices, and (2) It skips unnecessary score computations by estimating upper bounding scores. These two ideas reduce the time complexity of Mogul to O(n) from O(n^3) of the inverse matrix approach. Experiments show that Mogul is much faster and gives significantly better retrieval quality than a state-of-the-art approximation approach

Zero-Shot In-Distribution Detection in Multi-Object Settings Using Vision-Language Foundation Models

Removing out-of-distribution (OOD) images from noisy images scraped from the Internet is an important preprocessing for constructing datasets, which can be addressed by zero-shot OOD detection with vision language foundation models (CLIP). The existing zero-shot OOD detection setting does not consider the realistic case where an image has both in-distribution (ID) objects and OOD objects. However, it is important to identify such images as ID images when collecting the images of rare classes or ethically inappropriate classes that must not be missed. In this paper, we propose a novel problem setting called in-distribution (ID) detection, where we identify images containing ID objects as ID images, even if they contain OOD objects, and images lacking ID objects as OOD images. To solve this problem, we present a new approach, \textbf{G}lobal-\textbf{L}ocal \textbf{M}aximum \textbf{C}oncept \textbf{M}atching (GL-MCM), based on both global and local visual-text alignments of CLIP features, which can identify any image containing ID objects as ID images. Extensive experiments demonstrate that GL-MCM outperforms comparison methods on both multi-object datasets and single-object ImageNet benchmarks

LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning

We present a novel vision-language prompt learning approach for few-shot out-of-distribution (OOD) detection. Few-shot OOD detection aims to detect OOD images from classes that are unseen during training using only a few labeled in-distribution (ID) images. While prompt learning methods such as CoOp have shown effectiveness and efficiency in few-shot ID classification, they still face limitations in OOD detection due to the potential presence of ID-irrelevant information in text embeddings. To address this issue, we introduce a new approach called \textbf{Lo}cal regularized \textbf{Co}ntext \textbf{Op}timization (LoCoOp), which performs OOD regularization that utilizes the portions of CLIP local features as OOD features during training. CLIP's local features have a lot of ID-irrelevant nuisances (e.g., backgrounds), and by learning to push them away from the ID class text embeddings, we can remove the nuisances in the ID class text embeddings and enhance the separation between ID and OOD. Experiments on the large-scale ImageNet OOD detection benchmarks demonstrate the superiority of our LoCoOp over zero-shot, fully supervised detection methods and prompt learning methods. Notably, even in a one-shot setting -- just one label per class, LoCoOp outperforms existing zero-shot and fully supervised detection methods. The code will be available via \url{https://github.com/AtsuMiyai/LoCoOp}

Scaling Manifold Ranking Based Image Retrieval

Manifold Ranking is a graph-based ranking algorithm being successfully applied to retrieve images from multimedia databases. Given a query image, Manifold Ranking computes the ranking scores of images in the database by exploiting the relationships among them expressed in the form of a graph. Since Manifold Ranking effectively utilizes the global structure of the graph, it is significantly better at finding intuitive results compared with current approaches. Fundamentally, Manifold Ranking requires an inverse matrix to compute ranking scores and so needs O(n^3) time, where n is the number of images. Manifold Ranking, unfortunately, does not scale to support databases with large numbers of images. Our solution, Mogul, is based on two ideas: (1) It efficiently computes ranking scores by sparse matrices, and (2) It skips unnecessary score computations by estimating upper bounding scores. These two ideas reduce the time complexity of Mogul to O(n) from O(n^3) of the inverse matrix approach. Experiments show that Mogul is much faster and gives significantly better retrieval quality than a state-of-the-art approximation approach

An overview of and issues with sky radiometer technology and SKYNET

This paper is an overview of the progress in sky radiometer technology and the development of the network called SKYNET. It is found that the technology has produced useful on-site calibration methods, retrieval algorithms, and data analyses from sky radiometer observations of aerosol, cloud, water vapor, and ozone. A formula was proposed for estimating the accuracy of the sky radiometer calibration constant F0 using the improved Langley (IL) method, which was found to be a good approximation to observed monthly mean uncertainty in F0, around 0.5 % to 2.4 % at the Tokyo and Rome sites and smaller values of around 0.3 % to 0.5 % at the mountain sites at Mt. Saraswati and Davos. A new cross IL (XIL) method was also developed to correct an underestimation by the IL method in cases with large aerosol retrieval errors. The root-mean-square difference (RMSD) in aerosol optical thickness (AOT) comparisons with other networks took values of less than 0.02 for λ≥500 nm and a larger value of about 0.03 for shorter wavelengths in city areas and smaller values of less than 0.01 in mountain comparisons. Accuracies of single-scattering albedo (SSA) and size distribution retrievals are affected by the propagation of errors in measurement, calibrations for direct solar and diffuse sky radiation, ground albedo, cloud screening, and the version of the analysis software called the Skyrad pack. SSA values from SKYNET were up to 0.07 larger than those from AERONET, and the major error sources were identified as an underestimation of solid viewing angle (SVA) and cloud contamination. Correction of these known error factors reduced the SSA difference to less than 0.03. Retrievals of other atmospheric constituents by the sky radiometer were also reviewed. Retrieval accuracies were found to be about 0.2 cm for precipitable water vapor amount and 13 DU (Dobson Unit) for column ozone amount. Retrieved cloud optical properties still showed large deviations from validation data, suggesting a need to study the causes of the differences. It is important that these recent studies on improvements presented in the present paper are introduced into the existing operational systems and future systems of the International SKYNET Data Center

RNase E and the High-Fidelity Orchestration of RNA Metabolism.

The bacterial endoribonuclease RNase E occupies a pivotal position in the control of gene expression, as its actions either commit transcripts to an irreversible fate of rapid destruction or unveil their hidden functions through specific processing. Moreover, the enzyme contributes to quality control of rRNAs. The activity of RNase E can be directed and modulated by signals provided through regulatory RNAs that guide the enzyme to specific transcripts that are to be silenced. Early in its evolutionary history, RNase E acquired a natively unfolded appendage that recruits accessory proteins and RNA. These accessory factors facilitate the activity of RNase E and include helicases that remodel RNA and RNA-protein complexes, and polynucleotide phosphorylase, a relative of the archaeal and eukaryotic exosomes. RNase E also associates with enzymes from central metabolism, such as enolase and aconitase. RNase E-based complexes are diverse in composition, but generally bear mechanistic parallels with eukaryotic machinery involved in RNA-induced gene regulation and transcript quality control. That these similar processes arose independently underscores the universality of RNA-based regulation in life. Here we provide a synopsis and perspective of the contributions made by RNase E to sustain robust gene regulation with speed and accuracy.Wellcome Trus