1,720,962 research outputs found
Addressing Distributional Shift challenges in Computer Vision for Real-World Applications
Get PDFL'abstract è presente nell'allegato / the abstract is in the attachmen
3DOS: Towards 3D Open Set Learning - Benchmarking and Understanding Semantic Novelty Detection on Point Clouds
No full textIn recent years there has been significant progress in the field of 3D learning on classification, detection and segmentation problems. The vast majority of the existing studies focus on canonical closed-set conditions, neglecting the intrinsic open nature of the real-world. This limits the abilities of robots and autonomous systems involved in safety-critical applications that require managing novel and unknown signals. In this context exploiting 3D data can be a valuable asset since it provides rich information about the geometry of perceived objects and scenes. With this paper we provide the first broad study on 3D Open Set learning. We introduce 3DOS: a novel testbed for semantic novelty detection that considers several settings with increasing difficulties in terms of semantic (category) shift, and covers both in-domain (synthetic-to-synthetic, real-to-real) and cross-domain (synthetic-to-real) scenarios. Moreover, we investigate the related 2D Open Set literature to understand if and how its recent improvements are effective on 3D data. Our extensive benchmark positions several algorithms in the same coherent picture, revealing their strengths and limitations. The results of our analysis may serve as a reliable foothold for future tailored 3D Open Set methods
Domain Generalization vs Data Augmentation: An Unbiased Perspective
Get PDFIn domain generalization the target domain is not known at training time. We show that a style transfer based data augmentation strategy can be implemented easily and outperforms the current state of the art domain generalization methods. Moreover, we observe that those methods, even if combined with the described data augmentation, do not take advantage of it, indicating the need of new generalization solutions
Contrastive Learning for Cross-Domain Open World Recognition
Get PDFThe ability to evolve is fundamental for any valuable autonomous agent whose
knowledge cannot remain limited to that injected by the manufacturer. Consider
for example a home assistant robot: it should be able to incrementally learn
new object categories when requested, but also to recognize the same objects in
different environments (rooms) and poses (hand-held/on the floor/above
furniture), while rejecting unknown ones. Despite its importance, this scenario
has started to raise interest in the robotic community only recently and the
related research is still in its infancy, with existing experimental testbeds
but no tailored methods. With this work, we propose the first learning approach
that deals with all the previously mentioned challenges at once by exploiting a
single contrastive objective. We show how it learns a feature space perfectly
suitable to incrementally include new classes and is able to capture knowledge
which generalizes across a variety of visual domains. Our method is endowed
with a tailored effective stopping criterion for each learning episode and
exploits a self-paced thresholding strategy that provides the classifier with a
reliable rejection option. Both these novel contributions are based on the
observation of the data statistics and do not need manual tuning. An extensive
experimental analysis confirms the effectiveness of the proposed approach in
establishing the new state-of-the-art. The code is available at
https://github.com/FrancescoCappio/Contrastive_Open_World.Comment: This work has been accepted for publication at IROS 202
Semantic Novelty Detection via Relational Reasoning
Get PDFSemantic novelty detection aims at discovering unknown categories in the test data. This task is particularly relevant in safety-critical applications, such as autonomous driving or healthcare, where it is crucial to recognize unknown objects at deployment time and issue a warning to the user accordingly. Despite the impressive advancements of deep learning research, existing models still need a fine-tuning stage on the known categories in order to recognize the unknown ones. This could be prohibitive when privacy rules limit data access, or in case of strict memory and computational constraints (e.g. edge computing). We claim that a tailored representation learning strategy may be the right solution for effective and efficient semantic novelty detection. Besides extensively testing state-of-the-art approaches for this task, we propose a novel representation learning paradigm based on relational reasoning. It focuses on learning how to measure semantic similarity rather than recognizing known categories. Our experiments show that this knowledge is directly transferable to a wide range of scenarios, and it can be exploited as a plug-and-play module to convert closed-set recognition models into reliable open-set ones
Rethinking Domain Generalization Baselines
Get PDFDespite being very powerful in standard learning settings, deep learning models can be extremely brittle when deployed in scenarios different from those on which they were trained. Domain generalization methods investigate this problem and data augmentation strategies have shown to be helpful tools to increase data variability, supporting model robustness across domains. In our work we focus on style transfer data augmentation and we present how it can be implemented with a simple and inexpensive strategy to improve generalization. Moreover, we analyze the behavior of current state of the art domain generalization methods when integrated with this augmentation solution: our thorough experimental evaluation shows that their original effect almost always disappears with respect to the augmented baseline. This issue open new scenarios for domain generalization research, highlighting the need of novel methods properly able to take advantage of the introduced data variability
Foundation Models and Fine-Tuning: A Benchmark for Out of Distribution Detection
Get PDFThe rise of foundation models is pushing Computer Vision research towards a paradigm shift, in the wake of what already happened in the Natural Language Processing field. These models, trained at scale on huge data collections, provide high-quality representations that generalize well enough to be applied directly to downstream tasks, often outperforming task-specific models. The Out Of Distribution (OOD) detection problem, which involves the ability to recognize when test samples come from a previously unseen semantic category, represents one of the research fields in which this paradigm shift could have the greatest impact. However, existing testbeds are limited in scale and scope and get easily saturated when adopting foundation-based pretrainings. With this work, we introduce a new benchmark covering realistic yet harder OOD detection tasks to properly assess the performance of large pretrained models. We design an experimental framework to analyze specific choices in the model learning and use (which dataset, pretraining objective, OOD scoring function) and extensively evaluate the comparison to standard approaches that leverage a training phase on the available In Distribution (ID) data. The results highlight the actual performance benefits of leveraging foundation models in this context without any further learning effort, and identify situations where task-specific fine-tuning remains the best choice
Self-Supervision & Meta-Learning for One-Shot Unsupervised Cross-Domain Detection
No full textDeep detection approaches are powerful in controlled conditions, but appear
brittle and fail when source models are used off-the-shelf on unseen domains.
Most of the existing works on domain adaptation simplify the setting and access
jointly both a large source dataset and a sizable amount of target samples.
However this scenario is unrealistic in many practical cases as when monitoring
image feeds from social media: only a pretrained source model is available and
every target image uploaded by the users belongs to a different domain not
foreseen during training. We address this challenging setting by presenting an
object detection algorithm able to exploit a pre-trained source model and
perform unsupervised adaptation by using only one target sample seen at test
time. Our multi-task architecture includes a self-supervised branch that we
exploit to meta-train the whole model with single-sample cross-domain episodes,
and prepare to the test condition. At deployment time the self-supervised task
is iteratively solved on any incoming sample to one-shot adapt on it. We
introduce a new dataset of social media image feeds and present a thorough
benchmark with the most recent cross-domain detection methods showing the
advantages of our approach.Comment: Accepted for Publication at Computer Vision and Image Understanding
(CVIU) Journa
One-Shot Unsupervised Cross-Domain Detection
Get PDFDespite impressive progress in object detection over the last years, it is still an open challenge to reliably detect objects across visual domains. All current approaches access a sizable amount of target data at training time. This is a heavy assumption, as often it is not possible to anticipate the domain where a detector will be used, nor to access it in advance for data acquisition. Consider for instance the task of monitoring image feeds from social media: as every image is uploaded by a different user it belongs to a different target domain that is impossible to foresee during training. Our work addresses this setting, presenting an object detection algorithm able to perform unsupervised adaptation across domains by using only one target sample, seen at test time. We introduce a multi-task architecture that one-shot adapts to any incoming sample by iteratively solving a self-supervised task on it. We further enhance this auxiliary adaptation with cross-task pseudo-labeling. A thorough benchmark analysis against the most recent cross-domain detection methods and a detailed ablation study show the advantage of our approach
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