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3D Point Cloud Instance Segmentation of Lettuce Based on PartNet

3D Point Cloud Instance Segmentation of Lettuce Based on Par...

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

作者： Wang, Luhan Zheng, Lihua Wang, Minjuan China Agr Univ Key Lab Smart Agr Syst Minist Educ Beijing 100083 Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Organ level instance segmentation (e.g., individual leaves) based on computer vision techniques is a key step in the measurement of plant phenotypes. Since plant organs, especially leaves, are self-occluded and emerged-occluded, single-view images affect the acquisition of some effective information. However, 3D global images contain much more plant morphological information than single-view images, and it is of great significance for plant phenotype research. In this paper, lettuce was taken as the research object, its 3D point cloud images were obtained and instance segmentation was carried out based on the deep learning method. The result showed that the 3D point cloud of each leaf was segmented and identified accurately. Specifically, we constructed a lettuce point cloud dataset consisting of 620 real and synthetic point clouds and fused them together to train a 3D instance segmentation network-PartNet, which directly takes 3D point clouds as input and its output is the instance segmentation results of leaves. The experimental results showed that, when tested with 40 point clouds in the validation set, the metric Average Precision (%) with IoU threshold being 0.25 reached 97.2%, and with IoU threshold being 0.5 reached 92.4% respectively, indicating that the constructed PartNet network has the potential to accurately segment the 3D point cloud leaf instances for lettuce.

关键词： Point cloud compression Training Image segmentation Solid modeling computer vision Three-dimensional displays Pipelines

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Exploring Joint Embedding Architectures and Data Augmentations for Self-Supervised Representation Learning in Event-Based vision

Exploring Joint Embedding Architectures and Data Augmentatio...

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

作者： Barchid, Sami Mennesson, José Djéraba, Chaabane Univ. Lille Cnrs Centrale Lille Umr 9189 CRIStAL France Univ. Lille Centre for Digital Systems Imt Nord Europe Institut Mines-Télécom LilleF-59000 France

ISBN: (纸本)9798350302493

This paper proposes a self-supervised representation learning (SSRL) framework for event-based vision, which leverages various lightweight convolutional neural networks (CNNs) including 2D-, 3D-, and Spiking CNNs. The method uses a joint embedding architecture to maximize the agreement between features extracted from different views of the same event sequence. Popular event data augmentation techniques are employed to design an efficient augmentation policy for event-based SSRL, and we provide novel data augmentation methods to enhance the pretraining pipeline. Given the novelty of SSRL for event-based vision, we elaborate standard evaluation protocols and use them to evaluate our approach. Our study demonstrates that pretrained CNNs acquire effective and transferable features, enabling them to achieve competitive performance in object or action recognition across various commonly used event-based datasets, even in a low-data regime. This paper also conducts an experimental analysis of the extracted features regarding the Uniformity-Tolerance tradeoff to assess their quality, and measure the similarity of representations using linear Center Kernel Alignement. These quantitative measurements reinforce our observations from the performance benchmarks and show substantial differences between the learned representations of all types of CNNs despite being optimized with the same approach. © 2023 IEEE.

关键词： Embeddings

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CelebV-Text: A Large-Scale Facial Text-Video Dataset

CelebV-Text: A Large-Scale Facial Text-Video Dataset

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

作者： Yu, Jianhui Zhu, Hao Jiang, Liming Loy, Chen Change Cai, Weidong Wu, Wayne Univ Sydney Sydney NSW 2006 Australia SenseTime Res Hong Kong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Text-driven generation models are flourishing in video generation and editing. However, face-centric text-to-video generation remains a challenge due to the lack of a suitable dataset containing high-quality videos and highly relevant texts. This paper presents CelebV-Text, a large-scale, diverse, and high-quality dataset of facial text-video pairs, to facilitate research on facial text-to-video generation tasks. CelebV-Text comprises 70,000 in-the-wild face video clips with diverse visual content, each paired with 20 texts generated using the proposed semi-automatic text generation strategy. The provided texts are of high quality, describing both static and dynamic attributes precisely. The superiority of CelebV-Text over other datasets is demonstrated via comprehensive statistical analysis of the videos, texts, and text-video relevance. The effectiveness and potential of CelebV-Text are further shown through extensive self-evaluation. A benchmark is constructed with representative methods to standardize the evaluation of the facial text-to-video generation task. All data and models are publicly available.

关键词： Datasets and evaluation

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Cross-modal Image Synthesis within Dual-Energy X-ray Security Imagery

Cross-modal Image Synthesis within Dual-Energy X-ray Securit...

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

作者： Isaac-Medina, Brian K. S. Bhowmik, Neelanjan Willcocks, Chris G. Breckon, Toby P. Univ Durham Dept Comp Sci Durham England Univ Durham Dept Engn Durham England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Dual-energy X-ray scanners are used for aviation security screening given their capability to discriminate materials inside passenger baggage. To facilitate manual operator inspection, a pseudo-colouring is assigned to the effective composition of the material. Recently, paired image to image translation models based on conditional Generative Adversarial Networks (cGAN) have shown to be effective for image colourisation. In this work, we investigate the use of such a model to translate from the raw X-ray energy responses (high, low, effective-Z) to the pseudo-coloured images and vice versa. Specifically, given N X-ray modalities, we train a cGAN conditioned in N - m domains to generate the remaining m representation. Our method achieves a mean squared error (MSE) of 16.5 and a structural similarity index (SSIM) of 0.9815 when using the raw modalities to generate the pseudo-colour representation. Additionally, raw X-ray high energy, low energy and effective-Z projections were generated given the pseudo-colour image with minimum MSE of 2.57, 5.63 and 1.43, and maximum SSIM of 0.9953, 0.9901 and 0.9921. Furthermore, we assess the quality of our synthesised pseudo-colour reconstructions by measuring the performance of two object detection models originally trained on real X-ray pseudo-colour images over our generated pseudo-colour images. Interestingly, our generated pseudo-colour images obtain marginally improved detection performance than the corresponding real X-ray pseudo-colour images, showing that meaningful representations are synthesized and that these reconstructions are applicable for differing aviation security tasks.

关键词： computer architecture Predictive models Generative adversarial networks Generators Data models pattern recognition Security

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Unpaired Face Restoration via Learnable Cross-Quality Shift

Unpaired Face Restoration via Learnable Cross-Quality Shift

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

作者： Dong, Yangyi Zhang, Xiaoyun Wang, Zhixin Zhang, Ya Chen, Siheng Wang, Yanfeng Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Face restoration aims to recover high-quality (HQ) face images from low-quality (LQ) ones with various unknown degradations. Unpaired face restoration approaches focus on the adaptation to unseen degradations, which is a more challenging setting. Recently, generative facial priors of StyleGAN are used to improve the restoration capability of paired face restoration methods. For unpaired methods, however, using face priors is a challenge due to the lack of paired supervision. To address this issue, we take advantage of the editing capabilities of StyleGAN's latent code and propose a novel learnable cross-quality shift. The proposed learnable cross-quality shift not only introduces the generative facial priors into the unpaired framework, but also enables the straight-forward addition/subtraction in the latent feature space to achieve quality conversion. Furthermore, we design a two-branch framework with the proposed cross-quality shift to deal with unpaired data and improve the fidelity of restoration. With the unpaired framework, our method can be fine-tuned on images with unseen degradation. Experimental results show that (i) compared to state-of-the-art methods, our method improves performances under moderate and severe degradation situations;and (ii) both the proposed learnable cross-quality shift and the two-branch framework benefit the restoration performance.

关键词： Degradation computer vision Codes Face recognition conferences Image restoration Faces

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Beyond AUROC & co. for evaluating out-of-distribution detection performance

Beyond AUROC & co. for evaluating out-of-distribution detect...

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

作者： Humblot-Renaux, Galadrielle Escalera, Sergio Moeslund, Thomas B. Aalborg University Visual Analysis and Perception Lab Denmark Universitat Autònoma de Barcelona Computer Vision Center Spain Universitat de Barcelona Dept. of Mathematics and Informatics Spain

ISBN: (纸本)9798350302493

While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated. Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs. In this work, we take a closer look at the go-to metrics for evaluating OOD detection, and question the approach of exclusively reducing OOD detection to a binary classification task with little consideration for the detection threshold. We illustrate the limitations of current metrics (AUROC & its friends) and propose a new metric - Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples. Scripts and data are available at https://***/glhr/beyond-auroc © 2023 IEEE.

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ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing

ConZIC: Controllable Zero-shot Image Captioning by Sampling-...

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

作者： Zeng, Zequn Zhang, Hao Lu, Ruiying Wang, Dongsheng Chen, Bo Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

ISBN: (纸本)9798350301298

Zero-shot capability has been considered as a new revolution of deep learning, letting machines work on tasks without curated training data. As a good start and the only existing outcome of zero-shot image captioning (IC), ZeroCap abandons supervised training and sequentially searches every word in the caption using the knowledge of large-scale pre-trained models. Though effective, its autoregressive generation and gradient-directed searching mechanism limit the diversity of captions and inference speed, respectively. Moreover, ZeroCap does not consider the controllability issue of zero-shot IC. To move forward, we propose a framework for Controllable Zero-shot IC, named ConZIC. The core of ConZIC is a novel sampling-based non-autoregressive language model named GibbsBERT, which can generate and continuously polish every word. Extensive quantitative and qualitative results demonstrate the superior performance of our proposed ConZIC for both zero-shot IC and controllable zero-shot IC. Especially, ConZIC achieves about 5x faster generation speed than ZeroCap, and about 1.5x higher diversity scores, with accurate generation given different control signals. Our code is available at https://***/joeyz0z/ConZIC.

关键词： language reasoning vision

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Does Interference Exist When Training a Once-For-All Network?

Does Interference Exist When Training a Once-For-All Network...

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

作者： Shipard, Jordan Wiliem, Arnold Fookes, Clinton Queensland Univ Technol Signal Proc Artificial Intelligence & Vis Technol Brisbane Qld Australia Sentient Vis Syst Port Melbourne Vic Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The Once-For-All (OFA) method offers an excellent pathway to deploy a trained neural network model into multiple target platforms by utilising the supernet-subnet architecture. Once trained, a subnet can be derived from the supernet (both architecture and trained weights) and deployed directly to the target platform with little to no retraining or fine-tuning. To train the subnet population, OFA uses a novel training method called Progressive Shrinking (PS) which is designed to limit the negative impact of interference during training. It is believed that higher interference during training results in lower subnet population accuracies. In this work we take a second look at this interference effect. Surprisingly, we find that interference mitigation strategies do not have a large impact on the overall subnet population performance. Instead, we find the subnet architecture selection bias during training to be a more important aspect. To show this, we propose a simple-yet-effective method called Random Subnet Sampling (RSS), which does not have mitigation on the interference effect. Despite no mitigation, RSS is able to produce a better performing subnet population than PS in four small-to-medium-sized datasets;suggesting that the interference effect does not play a pivotal role in these datasets. Due to its simplicity, RSS provides a 1.9x reduction in training times compared to PS. A 6.1x reduction can also be achieved with a reasonable drop in performance when the number of RSS training epochs are reduced.

关键词： Training computer vision Codes conferences Sociology Neural networks Interference

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Making the V in Text-VQA Matter

Making the V in Text-VQA Matter

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

作者： Hegde, Shamanthak Jahagirdar, Soumya Gangisetty, Shankar Kle Technological University Hubballi India Cvit Iiit Hyderabad Hyderabad India Iiit Hyderabad Hyderabad India

ISBN: (纸本)9798350302493

Text-based VQA aims at answering questions by reading the text present in the images. It requires a large amount of scene-text relationship understanding compared to the VQA task. Recent studies have shown that the question-answer pairs in the dataset are more focused on the text present in the image but less importance is given to visual features and some questions do not require understanding the image. The models trained on this dataset predict biased answers due to the lack of understanding of visual context. For example, in questions like "What is written on the signboard?", the answer predicted by the model is always "STOP"which makes the model to ignore the image. To address these issues, we propose a method to learn visual features (making V matter in TextVQA) along with the OCR features and question features using VQA dataset as external knowledge for Text-based VQA. Specifically, we combine the TextVQA dataset and VQA dataset and train the model on this combined dataset. Such a simple, yet effective approach increases the understanding and correlation between the image features and text present in the image, which helps in the better answering of questions. We further test the model on different datasets and compare their qualitative and quantitative results. © 2023 IEEE.

关键词： computer vision

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UIGR: Unified Interactive Garment Retrieval

UIGR: Unified Interactive Garment Retrieval

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

作者： Han, Xiao He, Sen Zhang, Li Song, Yi-Zhe Xiang, Tao Univ Surrey CVSSP Guildford Surrey England iFlyTek Surrey Joint Res Ctr Artificial Intellige Guildford Surrey England Fudan Univ Sch Data Sci Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Interactive garment retrieval (IGR) aims to retrieve a target garment image based on a reference garment image along with user feedback on what to change on the reference garment. Two IGR tasks have been studied extensively: text-guided garment retrieval (TGR) and visually compatible garment retrieval (VCR). The user feedback for the former indicates what semantic attributes to change with the garment category preserved, while the category is the only thing to be changed explicitly for the latter, with an implicit requirement on style preservation. Despite the similarity between these two tasks and the practical need for an efficient system tackling both, they have never been unified and modeled jointly. In this paper, we propose a Unified Interactive Garment Retrieval (UIGR) framework to unify TGR and VCR. To this end, we first contribute a large-scale benchmark suited for both problems. We further propose a strong baseline architecture to integrate TGR and VCR in one model. Extensive experiments suggest that unifying two tasks in one framework is not only more efficient by requiring a single model only, it also leads to better performance. Code and datasets are available at GitHub.

关键词： Computational modeling Clothing Semantics computer architecture Benchmark testing Multitasking pattern recognition

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