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BIOCLIP: A vision Foundation Model for the Tree of Life

BIOCLIP: A Vision Foundation Model for the Tree of Life

引用

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

作者： Stevens, Samuel Wu, Jiaman Thompson, Matthew J. Campolongo, Elizabeth G. Song, Chan Hee Carlyle, David Edward Dong, Li Dahdul, Wasila M. Stewart, Charles Berger-Wolf, Tanya Chao, Wei-Lun Su, Yu Ohio State Univ Columbus OH 43210 USA Microsoft Res Mountain View CA USA Univ Calif Irvine Irvine CA USA Rensselaer Polytech Inst Troy NY USA

ISBN: (纸本)9798350353006

Images of the natural world, collected by a variety of cameras, from drones to individual phones, are increasingly abundant sources of biological information. There is an explosion of computational methods and tools, particularly computer vision, for extracting biologically relevant information from images for science and conservation. Yet most of these are bespoke approaches designed for a specific task and are not easily adaptable or extendable to new questions, contexts, and datasets. A vision model for general organismal biology questions on images is of timely need. To approach this, we curate and release TREEOFLIFE-10M, the largest and most diverse ML-ready dataset of biology images. We then develop BIOCLIP, a foundation model for the tree of life, leveraging the unique properties of biology captured by TREEOFLIFE-10M, namely the abundance and variety of images of plants, animals, and fungi, together with the availability of rich structured biological knowledge. We rigorously benchmark our approach on diverse fine-grained biology classification tasks and find that BIOCLIP consistently and substantially outperforms existing baselines (by 16% to 17% absolute). Intrinsic evaluation reveals that BIOCLIP has learned a hierarchical representation conforming to the tree of life, shedding light on its strong generalizability.(1)

关键词： computer vision evolutionary biology & ecology imageomics machine learning

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High-Resolution Image Harmonization via Collaborative Dual Transformations

High-Resolution Image Harmonization via Collaborative Dual T...

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

作者： Cong, Wenyan Tao, Xinhao Niu, Li Liang, Jing Gao, Xuesong Sun, Qihao Zhang, Liqing Shanghai Jiao Tong Univ Shanghai Peoples R China Harbin Inst Technol Harbin Peoples R China Tianjin Univ Tianjin Peoples R China Hisense Qingdao Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Given a composite image, image harmonization aims to adjust the foreground to make it compatible with the background. High -resolution image harmonization is in high demand, but still remains unexplored. Conventional image harmonization methods learn global RGB-to-RGB transformation which could effortlessly scale to high resolution, but ignore diverse local context. Recent deep learning methods learn the dense pixel-to-pixel transformation which could generate harmonious outputs, but are highly constrained in low resolution. In this work, we propose a high -resolution image harmonization network with Collaborative Dual Transformation (CDTNet) to combine pixelto-pixel transformation and RGB-to-RGB transformation coherently in an end-to-end network. Our CDTNet consists of a low-resolution generator for pixel-to-pixel transformation, a color mapping module for RGB-to-RGB transformation, and a refinement module to take advantage of both. Extensive experiments on high-resolution benchmark dataset and our created high -resolution real composite images demonstrate that our CDTNet strikes a good balance between efficiency and effectiveness.

关键词： Deep learning computer vision Image resolution Image color analysis Collaboration Generators pattern recognition

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Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization

Deep Spectral Methods: A Surprisingly Strong Baseline for Un...

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

作者： Melas-Kyriazi, Luke Rupprecht, Christian Laina, Iro Vedaldi, Andrea Univ Oxford Oxford England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Unsupervised localization and segmentation are longstanding computer vision challenges that involve decomposing an image into semantically meaningful segments without any labeled data. These tasks are particularly interesting in an unsupervised setting due to the difficulty and cost of obtaining dense image annotations, but existing unsupervised approaches struggle with complex scenes containing multiple objects. Differently from existing methods, which are purely based on deep learning, we take inspiration from traditional spectral segmentation methods by reframing image decomposition as a graph partitioning problem. Specifically, we examine the eigenvectors of the Laplacian of a feature affinity matrix from self-supervised networks. We find that these eigenvectors already decompose an image into meaningful segments, and can be readily used to localize objects in a scene. Furthermore, by clustering the features associated with these segments across a dataset, we can obtain well-delineated, nameable regions, i.e. semantic segmentations. Experiments on complex datasets (PASCAL VOC, MS-COCO) demonstrate that our simple spectral method outperforms the state-of-the-art in unsupervised localization and segmentation by a significant margin. Furthermore, our method can be readily used for a variety of complex image editing tasks, such as background removal and compositing.

关键词： Location awareness Deep learning Image segmentation computer vision Semantics Transformers Graph theory

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A Second-Order Approach to Learning with Instance-Dependent Label Noise

A Second-Order Approach to Learning with Instance-Dependent ...

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

作者： Zhu, Zhaowei Liu, Tongliang Liu, Yang Univ Calif Santa Cruz Comp Sci & Engn Santa Cruz CA 95064 USA Univ Sydney Trustworthy Machine Learning Lab Sydney NSW Australia

ISBN: (纸本)9781665445092

The presence of label noise often misleads the training of deep neural networks. Departing from the recent literature which largely assumes the label noise rate is only determined by the true label class, the errors in human-annotated labels are more likely to be dependent on the difficulty levels of tasks, resulting in settings with instance-dependent label noise. We first provide evidences that the heterogeneous instance-dependent label noise is effectively downweighting the examples with higher noise rates in a non-uniform way and thus causes imbalances, rendering the strategy of directly applying methods for class-dependent label noise questionable. Built on a recent work peer loss [24], we then propose and study the potentials of a second-order approach that leverages the estimation of several covariance terms defined between the instance-dependent noise rates and the Bayes optimal label. We show that this set of second-order statistics successfully captures the induced imbalances. We further proceed to show that with the help of the estimated second-order statistics, we identify a new loss function whose expected risk of a classifier under instance-dependent label noise is equivalent to a new problem with only class-dependent label noise. This fact allows us to apply existing solutions to handle this better-studied setting. We provide an efficient procedure to estimate these second-order statistics without accessing either ground truth labels or prior knowledge of the noise rates. Experiments on CIFAR10 and CIFAR100 with synthetic instance-dependent label noise and Clothing1M with real-world human label noise verify our approach.

关键词： Training Deep learning computer vision Estimation Rendering (computer graphics) pattern recognition Task analysis

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3D Scene Priors for Road Detection

3D Scene Priors for Road Detection

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23rd ieee conference on computer vision and pattern recognition (CVPR)

作者： Alvarez, Jose M. Gevers, Theo Lopez, Antonio M. Univ Autonoma Barcelona Comp Vis Ctr Barcelona Spain Univ Amsterdam Fac Sci NL-1012 WX Amsterdam Netherlands

ISBN: (纸本)9781424469840

vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low-level cues. Low-level photometric invariant cues are derived from the appearance of roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D road stages. Moreover, temporal road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify road sequences. Large scale experiments on road sequences show that the road detection method is robust to varying imaging conditions, road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest road detection accuracy when compared to state-of-the-art methods.

关键词： Three dimensional computer graphics

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Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond

Towards Robust Rain Removal Against Adversarial Attacks: A C...

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

作者： Yu, Yi Yang, Wenhan Tan, Yap-Peng Kot, Alex C. Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Nanyang Technol Univ Interdisciplinary Grad Programme ROSE Lab Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Rain removal aims to remove rain streaks from images/videos and reduce the disruptive effects caused by rain. It not only enhances image/video visibility but also allows many computer vision algorithms to function properly. This paper makes the first attempt to conduct a comprehensive study on the robustness of deep learning-based rain removal methods against adversarial attacks. Our study shows that, when the image/video is highly degraded, rain removal methods are more vulnerable to the adversarial attacks as small distortions/perturbations become less noticeable or detectable. In this paper, we first present a comprehensive empirical evaluation of various methods at different levels of attacks and with various losses/targets to generate the perturbations from the perspective of human perception and machine analysis tasks. A systematic evaluation of key modules in existing methods is performed in terms of their robustness against adversarial attacks. From the insights of our analysis, we construct a more robust deraining method by integrating these effective modules. Finally, we examine various types of adversarial attacks that are specific to deraining problems and their effects on both human and machine vision tasks, including 1) rain region attacks, adding perturbations only in the rain regions to make the perturbations in the attacked rain images less visible;2) object-sensitive attacks, adding perturbations only in regions near the given objects. Code is available at https://***/yuyidad/Robust_Rain_Removal.

关键词： computer vision Rain Systematics Codes Perturbation methods Machine vision Benchmark testing

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Image-to-image Translation via Hierarchical Style Disentanglement

Image-to-image Translation via Hierarchical Style Disentangl...

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

作者： Li, Xinyang Zhang, Shengchuan Hu, Jie Cao, Liujuan Hong, Xiaopeng Mao, Xudong Huang, Feiyue Wu, Yongjian Ji, Rongrong Xiamen Univ Sch Informat Xiamen Peoples R China Xi An Jiao Tong Univ Xian Peoples R China Peng Cheng Lab Shenzhen Peoples R China Xiamen Univ Tencent Youtu Lab Xiamen Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen Peoples R China

ISBN: (纸本)9781665445092

Recently, image-to-image translation has made significant progress in achieving both multi-label (i.e., translation conditioned on different labels) and multi-style (i.e., generation with diverse styles) tasks. However, due to the unexplored independence and exclusiveness in the labels, existing endeavors are defeated by involving uncontrolled manipulations to the translation results. In this paper, we propose Hierarchical Style Disentanglement (HiSD) to address this issue. Specifically, we organize the labels into a hierarchical tree structure, in which independent tags, exclusive attributes, and disentangled styles are allocated from top to bottom. Correspondingly, a new translation process is designed to adapt the above structure, in which the styles are identified for controllable translations. Both qualitative and quantitative results on the CelebA-HQ dataset verify the ability of the proposed HiSD. The code has been released at https://***/imlixinyang/HiSD.

关键词： Training computer vision Codes Scalability Process control Semisupervised learning pattern recognition

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Fair-VPT: Fair Visual Prompt Tuning for Image Classification

Fair-VPT: Fair Visual Prompt Tuning for Image Classification

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

作者： Park, Sungho Byun, Hy Eran Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350353006

Despite the remarkable success of vision Transformers (ViT) across diverse fields in computer vision, they have a clear drawback of expensive adaption cost for downstream tasks due to the increased scale. To address this, Visual Prompt Tuning (VPT) incorporates learnable parameters in the input space of ViT. While freezing the ViT backbone and tuning only the prompts, it exhibits superior performances to full fine-tuning. However, despite the outstanding advantage, we point out that VPT may lead to serious unfairness in downstream classification. Initially, we investigate the causes of unfairness in VPT, identifying the biasedly pre-trained ViT as a principal factor. Motivated by this observation, we propose a Fair Visual Prompt Tuning (Fair-VPT) which removes biased information in the pre-trained ViT while adapting it to downstream classification tasks. To this end, we categorize prompts into "cleaner prompts" and "target prompts". Based on this, we encode the class token in two different ways by either masking or not masking the target prompts in the self-attention process. These encoded tokens are trained with distinct objective functions, resulting in the inclusion of different information in the target and cleaner prompts. Moreover, we introduce a disentanglement loss based on contrastive learning to further decorrelate them. In experiments across diverse benchmarks, the proposed method demonstrates the most superior performance in terms of balanced classification accuracy and fairness.

关键词： FAI Fair-VPT Fairness Large vision Model vision Transformer Visual Prompt Tuning ViT VPT

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Artistic Style Discovery with Independent Components

Artistic Style Discovery with Independent Components

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

作者： Xie, Xin Li, Yi Huang, Huaibo Fu, Haiyan Wang, Wanwan Guo, Yanqing Dalian Univ Technol Sch Informat & Commun Engn Dalian Peoples R China Dalian Univ Technol Sch Artificial Intelligence Dalian Peoples R China CASIA Ctr Res Intelligent Percept & Comp Beijing Peoples R China InsightOne Tech Co Ltd Beijing 100028 Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Style transfer has been well studied in recent years with excellent performance processed. While existing methods usually choose CNNs as the powerful tool to accomplish superb stylization, less attention was paid to the latent style space. Rare exploration of underlying dimensions results in the poor style controllability and the limited practical application. In this work, we rethink the internal meaning of style features, further proposing a novel unsupervised algorithm for style discovery and achieving personalized manip-ulation. In particular, we take a closer look into the mechanism of style transfer and obtain different artistic style components from the latent space consisting of different style features. Then fresh styles can be generated by linear combination according to various style components. Experimental results have shown that our approach is superb in 1) restylizing the original output with the diverse artistic styles discovered from the latent space while keeping the content unchanged, and 2) being generic and compatible for various style transfer methods.

关键词： Photography Deep learning computer vision Codes computer architecture Aerospace electronics Controllability

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Spectral and Polarization vision: Spectro-polarimetric Real-world Dataset

Spectral and Polarization Vision: Spectro-polarimetric Real-...

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

作者： Jeon, Yujin Cho, Eunsue Kim, Youngchan Moon, Yunseong Omer, Khalid Heide, Felix Baek, Seung-Hwan POSTECH Pohang South Korea Meta Menlo Pk CA USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350353006

Image datasets are essential not only in validating existing methods in computer vision but also in developing new methods. Many image datasets exist, consisting of trichromatic intensity images taken with RGB cameras, which are designed to replicate human vision. However, polarization and spectrum, the wave properties of light that animals in harsh environments and with limited brain capacity often rely on, remain underrepresented in existing datasets. Although there are previous spectro-polarimetric datasets, they have insufficient object diversity, limited illumination conditions, linear-only polarization data, and inadequate image count. Here, we introduce two spectro-polarimetric datasets, consisting of trichromatic Stokes images and hyperspectral Stokes images. These datasets encompass both linear and circular polarization;they introduce multiple spectral channels;and they feature a broad selection of real-world scenes. With our dataset in hand, we analyze the spectro-polarimetric image statistics, develop efficient representations of such high-dimensional data, and evaluate spectral dependency of shape-from-polarization methods. As such, the proposed dataset promises a foundation for data-driven spectro-polarimetric imaging and vision research.

关键词： Computational Imaging

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