Vision-Language Models (VLMs) learn joint representations by mapping images and text into a shared latent space. However, recent research highlights that deterministic embeddings from standard VLMs often struggle to capture the uncertainties arising from the ambiguities in visual and textual descriptions and the multiple possible correspondences between images and texts. Existing approaches tackle this by learning probabilistic embeddings during VLM training, which demands large datasets and does not leverage the powerful representations already learned by large-scale VLMs like CLIP. In this paper, we propose GroVE, a post-hoc approach to obtaining probabilistic embeddings from frozen VLMs. GroVE builds on Gaussian Process Latent Variable Model (GPLVM) to learn a shared low-dimensional latent space where image and text inputs are mapped to a unified representation, optimized through single-modal embedding reconstruction and cross-modal alignment objectives. Once trained, the Gaussian Process model generates uncertainty-aware probabilistic embeddings. Evaluation shows that GroVE achieves state-of-the-art uncertainty calibration across multiple downstream tasks, including cross-modal retrieval, visual question answering, and active learning.

Convolutional Neural Networks (CNNs) are able to reliably classify objects in images if they are clearly visible and only slightly affected by small occlusions. However, heavy occlusions can strongly deteriorate the performance of CNNs, which is critical for tasks where correct identification is paramount. For many real-world applications, images are taken in unconstrained environments under suboptimal conditions, where occluded objects are inevitable. We propose a novel data augmentation method called Inverted Cutout, which can be used for training a CNN by showing only small patches of the images. Together with this augmentation method, we present several ways of making the network robust against occlusion. On the one hand, we utilize a spatial aggregation module without modifying the base network and on the other hand, we achieve occlusion-robustness with appropriate fine-tuning in conjunction with Inverted Cutout. In our experiments, we compare two different aggregation modules and two loss functions on the Occluded-Vehicles and Occluded-COCO-Vehicles datasets, showing that our approach outperforms existing state-of-the-art methods for object categorization under varying levels of occlusion.

Data from the Soil Moisture and Ocean Salinity (SMOS) mission were exploited to explore its potential in providing ecosystem information. Here the strength of an innovative relationship, between SMOS V620 algorithm data and Ecosystem Functional Properties (EFPs) derived from flux towers data, was preliminary investigated for Africa and South America forests. Correlation was also explored for above ground forest biomass. High correlation values were found between SMOS vegetation optical depth (VOD) and a AGB reference map, and with EFPs at year level (2014). Different VOD-EFPs trends were found for different latitudinal belts, characterizing wet and dry forests. The results suggest that SMOS VOD data represent a tool able to provide repeated information on forest biomass and forest processes in tropical and subtropical ranges.

Current visual recognition algorithms are “hungry” for data but massive annotation is extremely costly. Therefore, active learning algorithms are required that reduce labeling efforts to a minimum by selecting examples that are most valuable for labeling. In active learning, all categories occurring in collected data are usually assumed to be known in advance and experts should be able to label every requested instance. But do these assumptions really hold in practice? Could you name all categories in every image?

The accurate estimation of carbon and heat fluxes at global scale is paramount for future policy decisions in the context of global climate change. This paper analyzes the relative relevance of potential remote sensing and meteorological drivers of global carbon and energy fluxes over land. The study is done in an indirect way via upscaling both Gross Primary Production (GPP) and latent energy (LE) using Gaussian Process regression (GPR). In summary, GPR is successfully compared to multivariate linear regression (RMSE gain of +4.17% in GPP and +7.63% in LE) and kernel ridge regression (+2.91% in GPP and +3.07% in LE). The best GP models are then studied in terms of explanatory power based on the analysis of the lengthscales of the anisotropic covariance function, sensitivity maps of the predictive mean, and the robustness to distortions in the input variables. It is concluded that GPP is predominantly mediated by several vegetation indices and land surface temperature (LST), while LE is mostly driven by LST, global radiation and vegetation indices.

In this paper, we propose using local learning for multiclass novelty detection, a framework that we call local novelty detection. Estimating the novelty of a new sample is an extremely challenging task due to the large variability of known object categories. The features used to judge on the novelty are often very specific for the object in the image and therefore we argue that individual novelty models for each test sample are important. Similar to human experts, it seems intuitive to first look for the most related images thus filtering out unrelated data. Afterwards, the system focuses on discovering similarities and differences to those images only. Therefore, we claim that it is beneficial to solely consider training images most similar to a test sample when deciding about its novelty. Following the principle of local learning, for each test sample a local novelty detection model is learned and evaluated. Our local novelty score turns out to be a valuable indicator for deciding whether the sample belongs to a known category from the training set or to a new, unseen one. With our local novelty detection approach, we achieve state-of-the-art performance in multi-class novelty detection on two popular visual object recognition datasets, Caltech-256 and Image Net. We further show that our framework: (i) can be successfully applied to unknown face detection using the Labeled-Faces-in-the-Wild dataset and (ii) outperforms recent work on attribute-based unfamiliar class detection in fine-grained recognition of bird species on the challenging CUB-200-2011 dataset.

Detecting samples from previously unknown classes is a crucial task in object recognition, especially when dealing with real-world applications where the closed-world assumption does not hold. We present how to apply a null space method for novelty detection, which maps all training samples of one class to a single point. Beside the possibility of modeling a single class, we are able to treat multiple known classes jointly and to detect novelties for a set of classes with a single model. In contrast to modeling the support of each known class individually, our approach makes use of a projection in a joint subspace where training samples of all known classes have zero intra-class variance. This subspace is called the null space of the training data. To decide about novelty of a test sample, our null space approach allows for solely relying on a distance measure instead of performing density estimation directly. Therefore, we derive a simple yet powerful method for multi-class novelty detection, an important problem not studied sufficiently so far. Our novelty detection approach is assessed in comprehensive multi-class experiments using the publicly available datasets Caltech-256 and Image Net. The analysis reveals that our null space approach is perfectly suited for multi-class novelty detection since it outperforms all other methods.

We present an information theoretic framework for one-class classification, which allows for deriving several new novelty scores. With these scores, we are able to rank samples according to their novelty and to detect outliers not belonging to a learnt data distribution. The key idea of our approach is to measure the impact of a test sample on the previously learnt model. This is carried out in a probabilistic manner using Jensen-Shannon divergence and reclassification results derived from the Gaussian pro-cess regression framework. Our method is evaluated using well-known machine learning datasets as well as large-scale image categorisation experiments showing its ability to achieve state-of-the-art performance. 1