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Pseudo-label Generation for Agricultural Robotics Applications

Pseudo-label Generation for Agricultural Robotics Applicatio...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ciarfuglia, Thomas A. Motoi, Ionut Marian Saraceni, Leonardo Nardi, Daniele Sapienza Univ Rome Dept Comp Sci Management & Automat Engn DIAG Rome Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In the context of table grape cultivation there is rising interest in robotic solutions for harvesting, pruning, precision spraying and other agronomic tasks. Perception algorithms at the core of these systems require large amounts of labelled data, which in this context is often not available. In this work, we propose a semi-supervised solution to reduce the data needed to get state-of-the-art detection and segmentation of fruits in orchards. We present the case of table grape vineyards in southern Lazio (Italy) since grapes are a difficult fruit to segment due to occlusion, color and general illumination conditions. We consider the concrete scenario where the source labelled data is wine grape, while the target data is table grape, with considerable covariate shift. Starting from a simple video input, our method generates first bounding box labels, leveraging the structure from motion information, then segmentation masks, using the same weakly generated bounding box labels and a refining step based on Grabcut. This system is able to produce labels that considerably reduce the covariate shift from source to target data and that requires very limited data acquisition effort. Comparisons with State-of-the-art supervised solutions show how our methods are able to train new models that achieve high performances with few labelled images, with very simple labelling.

关键词： Structure from motion Motion segmentation Pipelines Refining Spraying Lighting pattern recognition

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Localized Latent Updates for Fine-Tuning vision-Language Models

Localized Latent Updates for Fine-Tuning Vision-Language Mod...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Ibing, Moritz Lim, Isaak Kobbelt, Leif Rwth Aachen University Visual Computing Institute Germany

ISBN: (纸本)9798350302493

Although massive pre-trained vision-language models like CLIP show impressive generalization capabilities for many tasks, still it often remains necessary to fine-tune them for improved performance on specific datasets. When doing so, it is desirable that updating the model is fast and that the model does not lose its capabilities on data outside of the dataset, as is often the case with classical fine-tuning approaches. In this work we suggest a lightweight adapter that only updates the models predictions close to seen datapoints. We demonstrate the effectiveness and speed of this relatively simple approach in the context of few-shot learning, where our results both on classes seen and unseen during training are comparable with or improve on the state of the art. © 2023 ieee.

关键词： computer vision

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Nerfels: Renderable Neural Codes for Improved Camera Pose Estimation

Nerfels: Renderable Neural Codes for Improved Camera Pose Es...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Avraham, Gil Straub, Julian Shen, Tianwei Yang, Tsun-Yi Germain, Hugo Sweeney, Chris Balntas, Vasileios Novotny, David DeTone, Daniel Newcombe, Richard Monash Univ Clayton Vic Australia Ecole Ponts Champs Sur Marne France Facebook Real Labs Menlo Pk CA USA Facebook AI Res Menlo Pk CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents a framework that combines traditional keypoint-based camera pose optimization with an invertible neural rendering mechanism. Our proposed 3D scene representation, Nerfels, is locally dense yet globally sparse. As opposed to existing invertible neural rendering systems which overfit a model to the entire scene, we adopt a feature-driven approach for representing scene-agnostic, local 3D patches with renderable codes. By modelling a scene only where local features are detected, our framework effectively generalizes to unseen local regions in the scene via an optimizable code conditioning mechanism in the neural renderer, all while maintaining the low memory footprint of a sparse 3D map representation. Our model can be incorporated to existing state-of-the-art hand-crafted and learned local feature pose estimators, yielding improved performance when evaluating on ScanNet for wide camera baseline scenarios.

关键词： Solid modeling Three-dimensional displays Codes conferences Pose estimation Rendering (computer graphics) Cameras

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Human vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scenes

Human Vision Based 3D Point Cloud Semantic Segmentation of L...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Yoo, Sunghwan Jeong, Yeonjeong Jameela, Maryam Sohn, Gunho York University Department of Earth and Space Science and Engineering TorontoONM3J 1P3 Canada

ISBN: (纸本)9798350302493

This paper proposes EyeNet, a novel semantic segmentation network for point clouds that addresses the critical yet often overlooked parameter of coverage area size. Inspired by human peripheral vision, EyeNet overcomes the limitations of conventional networks by introducing a simple but efficient multi-scale input and a parallel processing network with connection blocks between parallel streams. The proposed approach effectively addresses the challenges of dense point clouds, as demonstrated by our ablation studies and state-of-the-art performance on Large-Scale Outdoor datasets. © 2023 ieee.

关键词： Semantic Segmentation

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Few-Shot Image Classification Along Sparse Graphs

Few-Shot Image Classification Along Sparse Graphs

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Comer, Joseph F. Jacobson, Philip L. Hoffmann, Heiko HRL Labs LLC 3011 Malibu Canyon Rd Malibu CA 90265 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Few-shot learning remains a challenging problem, with unsatisfactory 1-shot accuracies for most real-world data. Here, we present a new perspective for data distributions in the feature space of a deep network and show how to exploit this perspective for few-shot learning. First, we observe that nearest neighbors in the feature space are with high probability members of the same class while generally two random points from one class are not much closer to each other than two points between classes. This observation suggests that classes in feature space form sparse, loosely connected graphs instead of dense clusters. To exploit this property, we propose using label propagation to the nearest unlabeled data and then using a kernel PCA reconstruction error as decision boundary in feature-space for the data distribution of each class. Using this method, which we call "K-Prop," we demonstrate largely improved few-shot learning performances (e.g., 83% accuracy for 1-shot 5-way classification on the RESISC45 satellite-images dataset) for datasets for which a backbone network can be trained to produce high within-class nearest-neighbor probabilities. We demonstrate this relationship using six different datasets.

关键词： Target tracking Limiting Shape Training data Streaming media pattern recognition Reliability

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Tragedy Plus Time: Capturing Unintended Human Activities from Weakly-labeled Videos

Tragedy Plus Time: Capturing Unintended Human Activities fro...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chakravarthy, Arnav Fang, Zhiyuan Yang, Yezhou Arizona State Univ Tempe AZ 85287 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In videos that contain actions performed unintentionally, agents do not achieve their desired goals. In such videos, it is challenging for computer vision systems to understand high-level concepts such as goal-directed behavior, an ability present in humans from a very early age. Inculcating this ability in artificially intelligent agents would make them better social learners by allowing them to evaluate human action under a teleological lens. To validate this ability of deep learning models to perform this task, we curate the W-Oops dataset, built upon the Oops dataset [11]. W-Oops consists of 2,100 unintentional human action videos, with 44 goal-directed and 30 unintentional video-level activity labels collected through human annotations. Due to the expensive segment annotation procedure, we propose a weakly supervised algorithm for localizing the goal-directed as well as unintentional temporal regions in the video leveraging solely video-level labels. In particular, we employ an attention mechanism based strategy that predicts the temporal regions which contributes the most to a classification task. Meanwhile, our designed overlap regularization allows the model to focus on distinct portions of the video for inferring the goal-directed and unintentional activity, while guaranteeing their temporal ordering. Extensive quantitative experiments verify the validity of our localization method. We further conduct a video captioning experiment which demonstrates that the proposed localization module does indeed assist teleological action understanding.

关键词： Location awareness computer vision Annotations Neural networks Prediction algorithms pattern recognition Intelligent agents

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Discriminability-enforcing loss to improve representation learning

Discriminability-enforcing loss to improve representation le...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Croitoru, Florinel-Alin Grigore, Diana-Nicoleta Ionescu, Radu Tudor Univ Bucharest Bucharest Romania

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

During the training process, deep neural networks implicitly learn to represent the input data samples through a hierarchy of features, where the size of the hierarchy is determined by the number of layers. In this paper, we focus on enforcing the discriminative power of the high-level representations, that are typically learned by the deeper layers (closer to the output). To this end, we introduce a new loss term inspired by the Gini impurity, which is aimed at minimizing the entropy (increasing the discriminative power) of individual high-level features with respect to the class labels. Although our Gini loss induces highly-discriminative features, it does not ensure that the distribution of the high-level features matches the distribution of the classes. As such, we introduce another loss term to minimize the Kullback-Leibler divergence between the two distributions. We conduct experiments on two image classification data sets (CIFAR-100 and Caltech 101), considering multiple neural architectures ranging from convolutional networks (ResNet-17, ResNet-18, ResNet-50) to transformers (CvT). Our empirical results show that integrating our novel loss terms into the training objective consistently outperforms the models trained with cross-entropy alone, without increasing the inference time at all.

关键词： Training Representation learning Impurities Neural networks computer architecture Transformers Entropy

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In-Domain Inversion for Improved 3D Face Alignment on Asymmetrical Expressions 18

In-Domain Inversion for Improved 3D Face Alignment on Asymme...

引用

18th International conference on Automatic Face and Gesture recognition (FG)

作者： Barros, Jilliam M. Diaz Rambach, Jason Murthy, Pramod Stricker, Didier German Res Ctr Artificial Intelligence DFKI Kaiserslautern Germany RPTU Kaiserslautern Kaiserslautern Germany

ISBN: (纸本)9798350394948;9798350394955

Facial landmark detection, often termed as face alignment, is a well-studied research problem in computer vision. Nonetheless, face alignment on asymmetrical expressions has been overlooked in the literature, particularly for unusual gestures observed in individuals with unilateral facial paralysis. In this paper, we explore in-domain inversion in a semi-supervised approach for face alignment and target the detection of 3D landmarks on symmetrical and extremely asymmetrical facial expressions due to paralysis. Our approach first leverages unlabeled face data to synthesize face images, while learning a compressed representation in the latent space. Then, it integrates in-domain inversion in the self-supervised stage, to make the latent space semantically meaningful. This is exploited in the supervised stage by a 2D face landmark detector, trained on labeled data. Finally, we extend the pipeline to 3D face alignment and regress the depth coordinate from the intermediate latent space and the predicted 2D landmarks. We evaluate and compare our method to related work on publicly available datasets, and demonstrate that our approach outperforms the state of the art in the detection of 3D facial landmarks in our newly introduced dataset of facial paralysis, ParFace. Our implementation and dataset are available at https://***/jilliam/ParFace.

关键词： computer vision

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Perfusion assessment via local remote photoplethysmography (rPPG)

Perfusion assessment via local remote photoplethysmography (...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kossack, Benjamin Wisotzky, Eric Eisert, Peter Schraven, Sebastian P. Globke, Brigitta Hilsmann, Anna Fraunhofer Heinrich Hertz Inst Berlin Germany Humboldt Univ Berlin Germany Rostock Univ Dept Otorhinolaryngol Head & Neck Surg Otto Korne Med Ctr Rostock Germany Charite Univ Med Berlin Dept Surg Berlin Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents an approach to assess the perfusion of visible human tissue from RGB video files. We propose metrics derived from remote photoplethysmography (rPPG) signals to detect whether a tissue is adequately supplied with blood. The perfusion analysis is done in three different scales, offering a flexible approach for different applications. We perform a plane-orthogonal-to-skin rPPG independently for locally defined regions of interest on each scale. From the extracted signals, we derive the signal-to-noise ratio, magnitude in the frequency domain, heart rate, perfusion index as well as correlation between specific rPPG signals in order to locally assess the perfusion of a specific region of human tissue. We show that locally resolved rPPG has a broad range of applications. As exemplary applications, we present results in intraoperative perfusion analysis and visualization during skin and organ transplantation as well as an application for liveliness assessment for the detection of presentation attacks to authentication systems.

关键词： Visualization Surgery Authentication Photoplethysmography Skin pattern recognition Monitoring

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iCLIP: Bridging Image Classification and Contrastive Language-Image Pre-training for Visual recognition

iCLIP: Bridging Image Classification and Contrastive Languag...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wei, Yixuan Cao, Yue Zhang, Zheng Peng, Houwen Yao, Zhuliang Xie, Zhenda Hue, Han Guo, Baining Tsinghua Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

This paper presents a method that effectively combines two prevalent visual recognition methods, i.e., image classification and contrastive language-image pre-training, dubbed iCLIP. Instead of naive multi-task learning that use two separate heads for each task, we fuse the two tasks in a deep fashion that adapts the image classification to share the same formula and the same model weights with the language-image pre-training. To further bridge these two tasks, we propose to enhance the category names in image classification tasks using external knowledge, such as their descriptions in dictionaries. Extensive experiments show that the proposed method combines the advantages of two tasks well: the strong discrimination ability in image classification tasks due to the clean category labels, and the good zero-shot ability in CLIP tasks ascribed to the richer semantics in the text descriptions. In particular, it reaches 82.9% top-1 accuracy on IN-1K, and meanwhile surpasses CLIP by 1.8%, with similar model size, on zero-shot recognition of Kornblith 12-dataset benchmark. The code and models are publicly available at https: //***/weiyx16/iCLIP.

关键词： language reasoning vision

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