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Decoupled Semantic Prototypes enable learning from diverse annotation types for semi-weakly segmentation in expert-driven domains

Decoupled Semantic Prototypes enable learning from diverse a...

引用

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

作者： Reiss, Simon Seibold, Constantin Freytag, Alexander Rodner, Erik Stiefelhagen, Rainer Karlsruhe Inst Technol Karlsruhe Germany Carl Zeiss Oberkochen Germany Univ Appl Sci Berlin Berlin Germany

ISBN: (纸本)9798350301298

A vast amount of images and pixel-wise annotations allowed our community to build scalable segmentation solutions for natural domains. However, the transfer to expert-driven domains like microscopy applications or medical healthcare remains difficult as domain experts are a critical factor due to their limited availability for providing pixel-wise annotations. To enable affordable segmentation solutions for such domains, we need training strategies which can simultaneously handle diverse annotation types and are not bound to costly pixel-wise annotations. In this work, we analyze existing training algorithms towards their flexibility for different annotation types and scalability to small annotation regimes. We conduct an extensive evaluation in the challenging domain of organelle segmentation and find that existing semi- and semi-weakly supervised training algorithms are not able to fully exploit diverse annotation types. Driven by our findings, we introduce Decoupled Semantic Prototypes (DSP) as a training method for semantic segmentation which enables learning from annotation types as diverse as image-level-, point-, bounding box-, and pixel-wise annotations and which leads to remarkable accuracy gains over existing solutions for semi-weakly segmentation.

关键词： cell microscopy Medical and biological vision

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Spatial-Frequency Mutual Learning for Face Super-Resolution

Spatial-Frequency Mutual Learning for Face Super-Resolution

引用

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

作者： Wang, Chenyang Jiang, Junjun Zhong, Zhiwei Liu, Xianming Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9798350301298

Face super-resolution (FSR) aims to reconstruct high-resolution (HR) face images from the low-resolution (LR) ones. With the advent of deep learning, the FSR technique has achieved significant breakthroughs. However, existing FSR methods either have a fixed receptive field or fail to maintain facial structure, limiting the FSR performance. To circumvent this problem, Fourier transform is introduced, which can capture global facial structure information and achieve image-size receptive field. Relying on the Fourier transform, we devise a spatial-frequency mutual network (SFMNet) for FSR, which is the first FSR method to explore the correlations between spatial and frequency domains as far as we know. To be specific, our SFMNet is a two-branch network equipped with a spatial branch and a frequency branch. Benefiting from the property of Fourier transform, the frequency branch can achieve image-size receptive field and capture global dependency while the spatial branch can extract local dependency. Considering that these dependencies are complementary and both favorable for FSR, we further develop a frequency-spatial interaction block (FSIB) which mutually amalgamates the complementary spatial and frequency information to enhance the capability of the model. Quantitative and qualitative experimental results show that the proposed method outperforms state-of-the-art FSR methods in recovering face images. The implementation and model will be released at https://***/wcy-cs/SFMNet.

关键词： Low-level vision

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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...

引用

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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Stitchable Neural Networks

Stitchable Neural Networks

引用

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

作者： Pan, Zizheng Cai, Jianfei Zhuang, Bohan Monash Univ ZIP Lab Clayton Vic Australia

ISBN: (纸本)9798350301298

The public model zoo containing enormous powerful pretrained model families (e.g., ResNet/DeiT) has reached an unprecedented scope than ever, which significantly contributes to the success of deep learning. As each model family consists of pretrained models with diverse scales (e.g., DeiT-Ti/S/B), it naturally arises a fundamental question of how to efficiently assemble these readily available models in a family for dynamic accuracy-efficiency trade-offs at runtime. To this end, we present Stitchable Neural Networks (SN-Net), a novel scalable and efficient framework for model deployment. It cheaply produces numerous networks with different complexity and performance trade-offs given a family of pretrained neural networks, which we call anchors. Specifically, SN-Net splits the anchors across the blocks/layers and then stitches them together with simple stitching layers to map the activations from one anchor to another. With only a few epochs of training, SN-Net effectively interpolates between the performance of anchors with varying scales. At runtime, SN-Net can instantly adapt to dynamic resource constraints by switching the stitching positions. Extensive experiments on ImageNet classification demonstrate that SN-Net can obtain on-par or even better performance than many individually trained networks while supporting diverse deployment scenarios. For example, by stitching Swin Transformers, we challenge hundreds of models in Timm model zoo with a single network. We believe this new elastic model framework can serve as a strong baseline for further research in wider communities.

关键词： Efficient and scalable vision

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Boundary-aware Backward-Compatible Representation via Adversarial Learning in Image Retrieval

Boundary-aware Backward-Compatible Representation via Advers...

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

作者： Pan, Tan Xu, Furong Yang, Xudong He, Sifeng Jiang, Chen Guo, Qingpei Qian, Feng Zhang, Xiaobo Cheng, Yuan Yang, Lei Chu, Wei Ant Grp Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Image retrieval plays an important role in the Internet world. Usually, the core parts of mainstream visual retrieval systems include an online service of the embedding model and a large-scale vector database. For traditional model upgrades, the old model will not be replaced by the new one until the embeddings of all the images in the database are re-computed by the new model, which takes days or weeks for a large amount of data. Recently, backward-compatible training (BCT) enables the new model to be immediately deployed online by making the new embeddings directly comparable to the old ones. For BCT, improving the compatibility of two models with less negative impact on retrieval performance is the key challenge. In this paper, we introduce AdvBCT, an Adversarial Backward-Compatible Training method with an elastic boundary constraint that takes both compatibility and discrimination into consideration. We first employ adversarial learning to minimize the distribution disparity between embeddings of the new model and the old model. Meanwhile, we add an elastic boundary constraint during training to improve compatibility and discrimination efficiently. Extensive experiments on GLDv2, Revisited Oxford (ROxford), and Revisited Paris (RParis) demonstrate that our method outperforms other BCT methods on both compatibility and discrimination. The implementation of AdvBCT will be publicly available at https://***/Ashespt/AdvBCT.

关键词： detection recognition: Categorization retrieval

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Intrinsic Physical Concepts Discovery with Object-Centric Predictive Models

Intrinsic Physical Concepts Discovery with Object-Centric Pr...

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

作者： Tang, Qu Zhu, Xiangyu Lei, Zhen Zhang, Zhaoxiang Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Automat MAIS Beijing Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot Beijing Peoples R China

ISBN: (纸本)9798350301298

The ability to discover abstract physical concepts and understand how they work in the world through observing lies at the core of human intelligence. The acquisition of this ability is based on compositionally perceiving the environment in terms of objects and relations in an unsupervised manner. Recent approaches learn object-centric representations and capture visually observable concepts of objects, e.g., shape, size, and location. In this paper, we take a step forward and try to discover and represent intrinsic physical concepts such as mass and charge. We introduce the PHYsical Concepts Inference NEtwork (PHYCINE), a system that infers physical concepts in different abstract levels without supervision. The key insights underlining PHYCINE are two-fold, commonsense knowledge emerges with prediction, and physical concepts of different abstract levels should be reasoned in a bottom-up fashion. Empirical evaluation demonstrates that variables inferred by our system work in accordance with the properties of the corresponding physical concepts. We also show that object representations containing the discovered physical concepts variables could help achieve better performance in causal reasoning tasks, i.e., ComPhy.

关键词： vision applications and systems

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Event-based Video Frame Interpolation with Cross-Modal Asymmetric Bidirectional Motion Fields

Event-based Video Frame Interpolation with Cross-Modal Asymm...

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

作者： Kim, Taewoo Chae, Yujeong Jang, Hyun-Kurl Yoon, Kuk-Jin Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350301298

Video Frame Interpolation (VFI) aims to generate intermediate video frames between consecutive input frames. Since the event cameras are bio-inspired sensors that only encode brightness changes with a micro-second temporal resolution, several works utilized the event camera to enhance the performance of VFI. However, existing methods estimate bidirectional inter-frame motion fields with only events or approximations, which can not consider the complex motion in real-world scenarios. In this paper, we propose a novel event-based VFI framework with cross-modal asymmetric bidirectional motion field estimation. In detail, our EIF-BiOFNet utilizes each valuable characteristic of the events and images for direct estimation of inter-frame motion fields without any approximation methods. Moreover, we develop an interactive attention-based frame synthesis network to efficiently leverage the complementary warping-based and synthesis-based features. Finally, we build a large-scale event-based VFI dataset, ERF-X170FPS, with a high frame rate, extreme motion, and dynamic textures to overcome the limitations of previous event-based VFI datasets. Extensive experimental results validate that our method shows significant performance improvement over the state-of-the-art VFI methods on various datasets. Our project pages are available at: https://***/intelpro/CBMNet

关键词： Low-level vision

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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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BiasAdv: Bias-Adversarial Augmentation for Model Debiasing

BiasAdv: Bias-Adversarial Augmentation for Model Debiasing

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

作者： Lim, Jongin Kim, Youngdong Kim, Byungjai Ahn, Chanho Shin, Jinwoo Yang, Eunho Han, Seungju Samsung Adv Inst Technol SAIT Seoul South Korea Korea Adv Inst Sci & Technol KAIST Seoul South Korea

ISBN: (纸本)9798350301298

Neural networks are often prone to bias toward spurious correlations inherent in a dataset, thus failing to generalize unbiased test criteria. A key challenge to resolving the issue is the significant lack of bias-conflicting training data (i.e., samples without spurious correlations). In this paper, we propose a novel data augmentation approach termed Bias-Adversarial augmentation (BiasAdv) that supplements bias-conflicting samples with adversarial images. Our key idea is that an adversarial attack on a biased model that makes decisions based on spurious correlations may generate synthetic bias-conflicting samples, which can then be used as augmented training data for learning a debiased model. Specifically, we formulate an optimization problem for generating adversarial images that attack the predictions of an auxiliary biased model without ruining the predictions of the desired debiased model. Despite its simplicity, we find that BiasAdv can generate surprisingly useful synthetic bias-conflicting samples, allowing the debiased model to learn generalizable representations. Furthermore, BiasAdv does not require any bias annotations or prior knowledge of the bias type, which enables its broad applicability to existing debiasing methods to improve their performances. Our extensive experimental results demonstrate the superiority of BiasAdv, achieving state-of-the-art performance on four popular benchmark datasets across various bias domains.

关键词： detection recognition: Categorization retrieval

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Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution

Local Implicit Normalizing Flow for Arbitrary-Scale Image Su...

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

作者： Yao, Jie-En Tsao, Li-Yuan Lo, Yi-Chen Tseng, Roy Chang, Chia-Che Lee, Chun-Yi Natl Tsing Hua Univ 1 ElsaLab Hsinchu Taiwan MediaTek Inc Hsinchu Taiwan

ISBN: (纸本)9798350301298

Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow. However, these methods can only perform a predefined fixed-scale SR, limiting their potential in real-world applications. Meanwhile, arbitrary-scale SR has gained more attention and achieved great progress. Nonetheless, previous arbitrary-scale SR methods ignore the ill-posed problem and train the model with per-pixel L1 loss, leading to blurry SR outputs. In this work, we propose "Local Implicit Normalizing Flow" (LINF) as a unified solution to the above problems. LINF models the distribution of texture details under different scaling factors with normalizing flow. Thus, LINF can generate photo-realistic HR images with rich texture details in arbitrary scale factors. We evaluate LINF with extensive experiments and show that LINF achieves the state-of-the-art perceptual quality compared with prior arbitrary-scale SR methods.

关键词： Low-level vision

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