Self-Supervised Pretraining and Controlled Augmentation Improve Rare Wildlife Recognition in UAV Images

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   Scene and Environment Monitoring Using Aerial Imagery and Deep Learning

   Mahdi Maktabdar Oghaz, Manzoor Razaak, Hamideh Kerdegari et al. · 2019 · IoTI4 Workshop 2019 · AI summary available

   Abstract

   Unmanned Aerial vehicles (UAV) are a promising technology for smart farming related applications. Aerial monitoring of agriculture farms with UAV enables key decision-making pertaining to crop monitoring. Advancements in deep learning techniques have further enhanced the precision and reliability of aerial imagery based analysis. The capabilities to mount various kinds of sensors (RGB, spectral cameras) on UAV allows remote crop analysis applications such as vegetation classification and segmentation, crop counting, yield monitoring and prediction, crop mapping, weed detection, disease and nutrient deficiency detection and others. A significant amount of studies are found in the literature t…

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   DINOv2: Learning Robust Visual Features without Supervision

   Maxime Oquab, Timothée Darcet, Théo Moutakanni et al. · 2023 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision. These models could greatly simplify the use of images in any system by producing all-purpose visual features, i.e., features that work across image distributions and tasks without finetuning. This work shows that existing pretraining methods, especially self-supervised methods, can produce such features if trained on enough curated data from diverse sources. We revisit existing approaches and combine different techniques to scale our pretraining in terms of data and model size. Most of the technical contributions aim a…

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   Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes

   Hassan Abu Alhaija, Siva Karthik Mustikovela, Lars Mescheder et al. · 2017 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   The success of deep learning in computer vision is based on availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation and object detection models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap…

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   MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining

   Di Wang, Jing Zhang, Minqiang Xu et al. · 2024 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing various image interpretation tasks. Pretraining is an active research topic, encompassing supervised and self-supervised learning methods to initialize model weights effectively. However, transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks. In this study, we explore the Multi-Task Pretraining (MTP) paradigm for RS foundation models to address this issue. Using a shared encoder and task-specific decoder architecture, we conduct multi-task supervised pretraining on the SAMRS dataset, en…

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   Cross-modal learning for SAR target recognition using optical vision foundation models

   Lucas Hirsch, James R. Hopgood, Javid Khan et al. · 2026 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   Synthetic Aperture Radar (SAR) is an important modality in a wide range of imaging applications due to its versatile, long range and near all weather operating capabilities. However, Automatic Target Recognition (ATR) remains a challenging problem due to limited labelled data, the strong speckle in SAR images and the significant domain gap between SAR and more abundant optical imagery. In contrast, electro-optical (EO) imagery benefits from massive datasets, clearer visual structure and powerful foundation models. In this work, we investigate how vision foundation models trained on optical data can provide class level supervision for SAR classification. We propose a cross-modal EO to SAR pro…

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   MDT3D: Multi-Dataset Training for LiDAR 3D Object Detection Generalization

   Louis Soum-Fontez, Jean-Emmanuel Deschaud, François Goulette · 2023 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   Supervised 3D Object Detection models have been displaying increasingly better performance in single-domain cases where the training data comes from the same environment and sensor as the testing data. However, in real-world scenarios data from the target domain may not be available for finetuning or for domain adaptation methods. Indeed, 3D object detection models trained on a source dataset with a specific point distribution have shown difficulties in generalizing to unseen datasets. Therefore, we decided to leverage the information available from several annotated source datasets with our Multi-Dataset Training for 3D Object Detection (MDT3D) method to increase the robustness of 3D object…

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   Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning

   Aral Hekimoglu, Michael Schmidt, Alvaro Marcos-Ramiro · 2023 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   We propose a novel semi-supervised active learning (SSAL) framework for monocular 3D object detection with LiDAR guidance (MonoLiG), which leverages all modalities of collected data during model development. We utilize LiDAR to guide the data selection and training of monocular 3D detectors without introducing any overhead in the inference phase. During training, we leverage the LiDAR teacher, monocular student cross-modal framework from semi-supervised learning to distill information from unlabeled data as pseudo-labels. To handle the differences in sensor characteristics, we propose a data noise-based weighting mechanism to reduce the effect of propagating noise from LiDAR modality to mono…

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   Anomaly Detection by Adapting a pre-trained Vision Language Model

   Yuxuan Cai, Xinwei He, Dingkang Liang et al. · 2024 · Computer Vision and Pattern Recognition · AI summary available

   Abstract

   Recently, large vision and language models have shown their success when adapting them to many downstream tasks. In this paper, we present a unified framework named CLIP-ADA for Anomaly Detection by Adapting a pre-trained CLIP model. To this end, we make two important improvements: 1) To acquire unified anomaly detection across industrial images of multiple categories, we introduce the learnable prompt and propose to associate it with abnormal patterns through self-supervised learning. 2) To fully exploit the representation power of CLIP, we introduce an anomaly region refinement strategy to refine the localization quality. During testing, the anomalies are localized by directly calculating …