Action-oriented Scene Understanding

In order to allow robots to act autonomously it is crucial that they do not only describe their environment accurately but also identify how to interact with their surroundings. While we witnessed tremendous progress in descriptive computer vision, approaches that explicitly target action are scarcer. This cumulative dissertation approaches the goal of interpreting visual scenes “in the wild” with respect to actions implied by the scene. We call this approach action-oriented scene understanding. It involves identifying and judging opportunities for interaction with constituents of the scene (e.g. objects and their parts) as well as understanding object functions and how interactions will impact the future. All of these aspects are addressed on three levels of abstraction: elements, perception and reasoning. On the elementary level, we investigate semantic and functional grouping of objects by analyzing annotated natural image scenes. We compare object label-based and visual context definitions with respect to their suitability for generating meaningful object class representations. Our findings suggest that representations generated from visual context are on-par in terms of semantic quality with those generated from large quantities of text. The perceptive level concerns action identification. We propose a system to identify possible interactions for robots and humans with the environment (affordances) on a pixel level using state-of-the-art machine learning methods. Pixel-wise part annotations of images are transformed into 12 affordance maps. Using these maps, a convolutional neural network is trained to densely predict affordance maps from unknown RGB images. In contrast to previous work, this approach operates exclusively on RGB images during both, training and testing, and yet achieves state-of-the-art performance. At the reasoning level, we extend the question from asking what actions are possible to what actions are plausible. For this, we gathered a dataset of household images associated with human ratings of the likelihoods of eight different actions. Based on the judgement provided by the human raters, we train convolutional neural networks to generate plausibility scores from unseen images. Furthermore, having considered only static scenes previously in this thesis, we propose a system that takes video input and predicts plausible future actions. Since this requires careful identification of relevant features in the video sequence, we analyze this particular aspect in detail using a synthetic dataset for several state-of-the-art video models. We identify feature learning as a major obstacle for anticipation in natural video data. The presented projects analyze the role of action in scene understanding from various angles and in multiple settings while highlighting the advantages of assuming an action-oriented perspective. We conclude that action-oriented scene understanding can augment classic computer vision in many real-life applications, in particular robotics

GPT4GEO: How a Language Model Sees the World's Geography

Large language models (LLMs) have shown remarkable capabilities across a broad range of tasks involving question answering and the generation of coherent text and code. Comprehensively understanding the strengths and weaknesses of LLMs is beneficial for safety, downstream applications and improving performance. In this work, we investigate the degree to which GPT-4 has acquired factual geographic knowledge and is capable of using this knowledge for interpretative reasoning, which is especially important for applications that involve geographic data, such as geospatial analysis, supply chain management, and disaster response. To this end, we design and conduct a series of diverse experiments, starting from factual tasks such as location, distance and elevation estimation to more complex questions such as generating country outlines and travel networks, route finding under constraints and supply chain analysis. We provide a broad characterisation of what GPT-4 (without plugins or Internet access) knows about the world, highlighting both potentially surprising capabilities but also limitations

Self-supervised Representation Learning of Neuronal Morphologies

Understanding the diversity of cell types and their function in the brain is one of the key challenges in neuroscience. The advent of large-scale datasets has given rise to the need of unbiased and quantitative approaches to cell type classification. We present GraphDINO, a purely data-driven approach to learning a low dimensional representation of the 3D morphology of neurons. GraphDINO is a novel graph representation learning method for spatial graphs utilizing self-supervised learning on transformer models. It combines attention-based global interaction between nodes and classic graph convolutional processing. We show, in two different species and cortical areas, that this method is able to yield morphological cell type clustering that is comparable to manual feature-based classification and shows a good correspondence to expert-labeled cell types. Our method is applicable beyond neuroscience in settings where samples in a dataset are graphs and graph-level embeddings are desired.Comment: Added code ur

One-shot multi-path planning for robotic applications using fully convolutional networks

INSPEC Accession Number: 19986957Path planning is important for robot action execution, since a path or a motion trajectory for a particular action has to be defined first before the action can be executed. Most of the current approaches are iterative methods where the trajectory is generated by predicting the next state based on the current state. Here we propose a novel method by utilising a fully convolutional neural network, which allows generation of complete paths even for several agents with one network prediction iteration. We demonstrate that our method is able to successfully generate optimal or close to optimal paths (less than 10% longer) in more than 99% of the cases for single path predictions in 2D and 3D environments. Furthermore, we show that the network is - without specific training on such cases - able to create (close to) optimal paths in 96% of the cases for two and in 84% of the cases for three simultaneously generated pathsSistemų analizės katedraVytauto Didžiojo universiteta

An Exploratory Search User Interface Concept Supporting Vague Querying and a Novel Result Representation

Common search engines deliver quite good results when the user has a precise notion of what he is looking for. However, the user might have in mind additional prior information regarding the importance of specific terms. Consequently, it seems desirable to avoid the latter and incorporate the knowledge into the query instead. Therefore, we propose a search user interface concept that supports users in modelling their uncertainty in a comfortable way, foster exploratory search and provide a compact yet informative representation of results. An implemented prototype demonstrates the feasibility of the concept. We also present results of a first twostep usability study. The results indicate a good usability of the concept and show that even this novel concept meets user’s expectations.

Distributional semantics of objects in visual scenes in comparison to text

The distributional hypothesis states that the meaning of a concept is defined through the contexts it occurs in. In practice, often word co-occurrence and proximity are analyzed in text corpora for a given word to obtain a real-valued semantic word vector, which is taken to (at least partially) encode the meaning of this word. Here we transfer this idea from text to images, where pre-assigned labels of other objects or activations of convolutional neural networks serve as context. We propose a simple algorithm that extracts and processes object contexts from an image database and yields semantic vectors for objects. We show empirically that these representations exhibit on par performance with state-of-the-art distributional models over a set of conventional objects. For this we employ well-known word benchmarks in addition to a newly proposed object-centric benchmarkTaikomosios informatikos katedraVytauto Didžiojo universiteta