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COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification

COOD: Combined out-of-distribution detection using multiple ...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Hogeweg, Laurens E. Gangireddy, Rajesh Brunink, Django Kalkman, Vincent J. Cornelissen, Ludo Kamminga, Jacob W. Intel Eindhoven Netherlands Nat Biodivers Ctr Leiden Netherlands Univ Twente Enschede Netherlands

ISBN: (纸本)9798350365474

High-performing out-of-distribution (OOD) detection, both anomaly and novel class, is an important prerequisite for the practical use of classification models. In this paper we focus on the species recognition task in images, concerned with large databases, a large number of fine-grained hierarchical classes, severe class imbalance, and varying image quality. We propose a framework for combining individual OOD measures into one combined OOD (COOD) measure using a supervised model. The individual measures are several existing state-of-the-art measures and several novel OOD measures developed with novel class detection and hierarchical class structure in mind. COOD was extensively evaluated on three large-scale (500k+ images) biodiversity datasets in the context of anomaly and novel class detection. We show that COOD outperforms individual, including state-of-the-art, OOD measures by a large margin in terms of TPR@1%FPR in the majority of experiments, e.g., improving detecting ImageNet images (OOD) from 54.3% to 83.3% for the iNaturalist 2018 dataset. SHAP (feature contribution) analysis shows that different individual OOD measures are essential for various tasks, indicating that multiple OOD measures and combinations are needed to generalize. Additionally, we show that explicitly considering ID images that are incorrectly classified for the original (species) recognition task is important for constructing high-performing OOD detection methods and for practical applicability. The framework can easily be extended or adapted to other tasks and media modalities.

关键词： anomaly image recognition novel class

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HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing

HarvestNet: A Dataset for Detecting Smallholder Farming Acti...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Xu, Jonathan Elmustafa, Amna Weldegebriel, Liya Negash, Emnet Lee, Richard Meng, Chenlin Ermon, Stefano Lobell, David Stanford Univ Stanford CA 94305 USA Univ Waterloo Waterloo ON Canada Univ Ghent Ghent Belgium Mekelle Univ Mekele Ethiopia

ISBN: (纸本)9798350365474

Small farms contribute to a large share of the productive land in developing countries. In regions such as subSaharan Africa, where 80% of farms are small (under 2 ha in size), the task of mapping smallholder cropland is an important part of tracking sustainability measures such as crop productivity. However, the visually diverse and nuanced appearance of small farms has limited the effectiveness of traditional approaches to cropland mapping. Here we introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems throughout the world. We present HarvestNet, a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023, collected using expert knowledge and satellite images, totaling 7k hand-labeled images and 2k ground-collected labels. We also benchmark a set of baselines, including SOTA models in remote sensing, with our best models having around 80% classification performance on hand labelled data and 90% and 98% accuracy on ground truth data for Tigray and Amhara, respectively. We also perform a visual comparison with a widely used pre-existing coverage map and show that our model detects an extra 56,621 hectares of cropland in Tigray. We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions. The dataset can be accessed through https://***/s/45a7b45556b90a9a11d2, while the code for the dataset and benchmarks is publicly available at https://***/jonxuxu/harvestpiles.

关键词： agriculture computer vision dataset harvest piles machine learning Remote sensing sustainability

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Uncovering the Hidden Cost of Model Compression

Uncovering the Hidden Cost of Model Compression

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Misra, Diganta Chaudhary, Muawiz Goyal, Agam Runwal, Bharat Chen, Pin Yu Carnegie Mellon Univ Pittsburgh PA 15213 USA Landskape AI Leiden Netherlands Mila Quebec AI Inst Montreal PQ Canada Concordia Univ Montreal PQ Canada Univ Wisconsin Madison Madison WI USA IBM Res Yorktown Hts NY USA

ISBN: (纸本)9798350365474

In an age dominated by resource-intensive foundation models, the ability to efficiently adapt to downstream tasks is crucial. Visual Prompting (VP), drawing inspiration from the prompting techniques employed in Large Language Models (LLMs), has emerged as a pivotal method for transfer learning in the realm of computer vision. As the importance of efficiency continues to rise, research into model compression has become indispensable in alleviating the computational burdens associated with training and deploying over-parameterized neural networks. A primary objective in model compression is to develop sparse and/or quantized models capable of matching or even surpassing the performance of their over-parameterized, full-precision counterparts. Although previous studies have explored the effects of model compression on transfer learning, its impact on visual prompting-based transfer remains unclear. This study aims to bridge this gap, shedding light on the fact that model compression detrimentally impacts the performance of visual prompting-based transfer, particularly evident in scenarios with low data volume. Furthermore, our findings underscore the adverse influence of sparsity on the calibration of downstream visual-prompted models. However, intriguingly, we also illustrate that such negative effects on calibration are not present when models are compressed via quantization. This empirical investigation underscores the need for a nuanced understanding beyond mere accuracy in sparse and quantized settings, thereby paving the way for further exploration in Visual Prompting techniques tailored for sparse and quantized models.

关键词： Compression Quantization Sparsity vision prompting

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ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding

ULIP-2: Towards Scalable Multimodal Pre-training for 3D Unde...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Xue, Le Yu, Ning Zhang, Shu Panagopoulou, Artemis Li, Junnan Martin-Martin, Roberto Wu, Jiajun Xiong, Caiming Xu, Ran Niebles, Juan Carlos Savarese, Silvio Salesforce AI Res San Francisco CA 94105 USA Stanford Univ Stanford CA USA Univ Penn Philadelphia PA USA Univ Texas Austin Austin TX USA

ISBN: (纸本)9798350353006

Recent advancements in multimodal pre-training have shown promising efficacy in 3D representation learning by aligning multimodal features across 3D shapes, their 2D counterparts, and language descriptions. However, the methods used by existing frameworks to curate such multimodal data, in particular language descriptions for 3D shapes, are not scalable, and the collected language descriptions are not diverse. To address this, we introduce ULIP-2, a simple yet effective tri-modal pre-training framework that leverages large multimodal models to automatically generate holistic language descriptions for 3D shapes. It only needs 3D data as input, eliminating the need for any manual 3D annotations, and is therefore scalable to large datasets. ULIP-2 is also equipped with scaled-up backbones for better multi-modal representation learning. We conduct experiments on two large-scale 3D datasets, Objaverse and ShapeNet, and augment them with tri-modal datasets of 3D point clouds, images, and language for training ULIP-2. Experiments show that ULIP-2 demonstrates substantial benefits in three downstream tasks: zero-shot 3D classification, standard 3D classification with fine-tuning, and 3D captioning (3D-to-language generation). It achieves a new SOTA of 50.6% (top-1) on Objaverse-LVIS and 84.7% (top-1) on ModelNet40 in zero-shot classification. In the ScanObjectNN benchmark for standard fine-tuning, ULIP-2 reaches an overall accuracy of 91.5% with a compact model of only 1.4 million parameters. ULIP-2 sheds light on a new paradigm for scalable multimodal 3D representation learning without human annotations and shows significant improvements over existing baselines. The code and datasets are released at https://***/salesforce/ULIP.

关键词： 3D vision Multimodal learning

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SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation

SegFormer3D: an Efficient Transformer for 3D Medical Image S...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Perera, Shehan Navard, Pouyan Yilmaz, Alper Ohio State Univ Photogrammetr Comp Vis Lab Columbus OH 43210 USA

ISBN: (纸本)9798350365474

The adoption of vision Transformers (ViTs) based architectures represents a significant advancement in 3D Medical Image (MI) segmentation, surpassing traditional Convolutional Neural Network (CNN) models by enhancing global contextual understanding. While this paradigm shift has significantly enhanced 3D segmentation performance, state-of-the-art architectures require extremely large and complex architectures with large scale computing resources for training and deployment. Furthermore, in the context of limited datasets, often encountered in medical imaging, larger models can present hurdles in both model generalization and convergence. In response to these challenges and to demonstrate that lightweight models are a valuable area of research in 3D medical imaging, we present SegFormer3D, a hierarchical Transformer that calculates attention across multiscale volumetric features. Additionally, SegFormer3D avoids complex decoders and uses an all-MLP decoder to aggregate local and global attention features to produce highly accurate segmentation masks. The proposed memory efficient Transformer preserves the performance characteristics of a significantly larger model in a compact design. SegFormer3D democratizes deep learning for 3D medical image segmentation by offering a model with 33x less parameters and a 13x reduction in GFLOPS compared to the current state-of-the-art (SOTA). We benchmark SegFormer3D against the current SOTA models on three widely used datasets Synapse, BRaTs, and ACDC, achieving competitive results. Code: https://***/OSUPCVLab/***

关键词： 3D Medical Image Segmentation ACDC Attention BraTs Deep Learning Efficient Attention Segmentation Synapse Transformers vision Transformers

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Multi-Modal Fusion of Event and RGB for Monocular Depth Estimation Using a Unified Transformer-based Architecture

Multi-Modal Fusion of Event and RGB for Monocular Depth Esti...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Devulapally, Anusha Khan, Md Fahim Faysal Advani, Siddharth Narayanan, Vijaykrishnan Penn State Univ University Pk PA 16802 USA Samsung Elect Amer Ridgefield Pk NJ USA

ISBN: (纸本)9798350365474

In the field of robotics and autonomous navigation, accurate pixel-level depth estimation has gained significant importance. Event cameras or dynamic vision sensors, capture asynchronous changes in brightness at the pixel level, offering benefits such as high temporal resolution, no motion blur, and a wide dynamic range. However, unlike traditional cameras that measure absolute intensity, event cameras lack the ability to provide scene context. Efficiently combining the advantages of both asynchronous events and synchronous RGB images to enhance depth estimation remains a challenge. In our study, we introduce a unified transformer that combines both event and RGB modalities to achieve precise depth prediction. In contrast to individual transformers for input modalities, a unified transformer model captures inter-modal dependencies and uses self-attention to enhance event-RGB contextual interactions. This approach exceeds the performance of recurrent neural network (RNN) methods used in state-of-the-art models. To encode the temporal information from events, convLSTMs are used before the transformer to improve depth estimation. Our proposed architecture outperforms the existing approaches in terms of absolute mean depth error, achieving state-of-the-art results in most cases. Additionally, the performance is also seen in other metrics like RMSE, absolute relative difference and depth thresholds compared to the existing approaches. The source code is available at:https://***/anusha-devulapally/ER-F2D.

关键词： Event Cameras Monocular Depth Estimation Multi-Modal Fusion vision Transformer

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IrrNet: Advancing Irrigation Mapping with Incremental Patch Size Training on Remote Sensing Imagery

IrrNet: Advancing Irrigation Mapping with Incremental Patch ...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Hoque, Oishee Bintey Swarup, Samarth Adiga, Abhijin Nouwakpo, Sayjro Kossi Marathe, Madhav Univ Virginia Dept Comp Sci Charlottesville VA 22903 USA Univ Virginia Biocomplex Inst Charlottesville VA 22903 USA ARS USDA Kimberly ID USA

ISBN: (纸本)9798350365474

Irrigation mapping plays a crucial role in effective water management, essential for preserving both water quality and quantity, and is key to mitigating the global issue of water scarcity. The complexity of agricultural fields, adorned with diverse irrigation practices, especially when multiple systems coexist in close quarters, poses a unique challenge. This complexity is further compounded by the nature of Landsat's remote sensing data, where each pixel is rich with densely packed information, complicating the task of accurate irrigation mapping. In this study, we introduce an innovative approach that employs a progressive training method, which strategically increases patch sizes throughout the training process, utilizing datasets from Landsat 5 and 7, labeled with the WRLU dataset for precise labeling. This initial focus allows the model to capture detailed features, progressively shifting to broader, more general features as the patch size enlarges. Remarkably, our method enhances the performance of existing state-of-the-art models by approximately 20%. Furthermore, our analysis delves into the significance of incorporating various spectral bands into the model, assessing their impact on performance. The findings reveal that additional bands are instrumental in enabling the model to discern finer details more effectively. This work sets a new standard for leveraging remote sensing imagery in irrigation mapping.

关键词： computer vision Irrigation Mapping Patch Scaling Remote Sensing Segmentation Transfer learning

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Orientation-conditioned Facial Texture Mapping for Video-based Facial Remote Photoplethysmography Estimation

Orientation-conditioned Facial Texture Mapping for Video-bas...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Cantrill, Sam Ahmedt-Aristizabal, David Petersson, Lars Suominen, Hanna Armin, Mohammad Ali Australian Natl Univ Canberra ACT Australia Commonwealth & Sci Ind Res Org Data61 Canberra ACT Australia Univ Turku Turku Finland

ISBN: (纸本)9798350365474

Camera-based remote photoplethysmography (rPPG) enables contactless measurement of important physiological signals such as pulse rate (PR). However, dynamic and unconstrained subject motion introduces significant variability into the facial appearance in video, confounding the ability of video-based methods to accurately extract the rPPG signal. In this study, we leverage the 3D facial surface to construct a novel orientation-conditioned facial texture video representation which improves the motion robustness of existing video-based facial rPPG estimation methods. Our proposed method achieves a significant 18.2% performance improvement in cross-dataset testing on MMPD over our baseline using the PhysNet model trained on PURE, highlighting the efficacy and generalization benefits of our designed video representation. We demonstrate significant performance improvements of up to 29.6% in all tested motion scenarios in cross-dataset testing on MMPD, even in the presence of dynamic and unconstrained subject motion. Emphasizing the benefits the benefits of disentangling motion through modeling the 3D facial surface for motion robust facial rPPG estimation. We validate the efficacy of our design decisions and the impact of different video processing steps through an ablation study. Our findings illustrate the potential strengths of exploiting the 3D facial surface as a general strategy for addressing dynamic and unconstrained subject motion in videos. The code is available at https://***/orientation-uv-rppg/.

关键词： computer vision motion rppg

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H³Net: Irregular Posture Detection by Understanding Human Character and Core Structures

H<SUP>3</SUP>Net: Irregular Posture Detection by Understandi...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Noh, Seungha Bae, Kangmin Bae, Yuseok Lee, Byong-Dai Kyonggi Univ Suwon Kyonggi South Korea ETRI Daejeon South Korea

ISBN: (纸本)9798350365474

This paper proposes H3Net that considers detecting people in irregular postures by utilizing human structures and characters. To handle both features, we introduce two attention modules: 1) Human Structure Attention Module (HSAM), which is introduced to focus on the spatial aspects of a person, and 2) Human Character Attention Module (HCAM), which is designed to address the issue of repetitive appearance. HSAM effectively handles both foreground and background information about a human instance and utilizes keypoints to provide additional guidance to predict irregular postures. Meanwhile, HCAM employs ID information obtained from the tracking head, enriching the posture prediction with high-level semantic information. Furthermore, gathering images of people in irregular postures is a challenging task. Therefore, many conventional datasets consist of images with the same actors simulating varying postures in distinct images. To address this problem, we propose a Human ID Dependent Posture (HID2) loss that handles repeated instances. The HID2 loss generates a regularization term by considering duplicated instances to reduce bias. Our experiments demonstrate the effectiveness of H(3)Net compared to existing algorithms on irregular posture datasets. Furthermore, we show the qualitative results using color-coded masks and bounding boxes. We also provide ablation studies to highlight the significance of our proposed methods.

关键词： Character recognition

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LOFI: LOng-tailed FIne-Grained Network for Food recognition

LOFI: LOng-tailed FIne-Grained Network for Food Recognition

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Rodriguez-de-Vera, Jesus M. Estepa, Imanol G. Bolanos, Marc Nagarajan, Bhalaji Radeva, Petia Univ Barcelona Barcelona Spain AIGecko Technol SL Barcelona Spain

ISBN: (纸本)9798350365474

Food recognition plays a crucial role in several healthcare applications. Nevertheless, it presents significant computer vision challenges such as long-tailed and fine-grained distributions that hinder its progress. In this work, we propose LOFI, a Long-tailed Fine-grained Network aimed specifically at tackling these food recognition challenges by improving the feature learning capabilities of food recognition models. Specifically, we improve vanilla R-CNN architecture by tailoring it for food recognition. We design an efficient multi-task framework for fine-grained food recognition, which exploits the lexical similarity of dishes during training to improve the discriminative ability of the network. Secondly, we include a Graph Confidence Propagation module based on graph neural networks to aggregate the information of overlapping detections and refine the final prediction of the network. Extensive analysis and ablations of different components of LOFI highlight that it successfully addresses the targeted problems and leads to noticeable gains in performance. Remarkably, the proposed method achieves competitive results and outperforms the current state-of-the-art methods in three public food benchmarks: UECFood-256, AiCrowd Food Challenge 2022, and UECFood-100 segmented.

关键词： Fine-grained Food visual recognition Graph Neural Networks Instance segmentation Long-tailed Object detection

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