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Abstract Visual Reasoning: An Algebraic Approach for Solving Raven's Progressive Matrices

Abstract Visual Reasoning: An Algebraic Approach for Solving...

引用

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

作者： Xu, Jingyi Vaidya, Tushar Wu, Yufei Chandra, Saket Lai, Zhangsheng Chong, Kai Fong Ernest Singapore Univ Technol & Design Singapore Singapore Nanyang Technol Univ Singapore Singapore Singapore Polytech Singapore Singapore

ISBN: (纸本)9798350301298

We introduce algebraic machine reasoning, a new reasoning framework that is well-suited for abstract reasoning. Effectively, algebraic machine reasoning reduces the difficult process of novel problem-solving to routine algebraic computation. The fundamental algebraic objects of interest are the ideals of some suitably initialized polynomial ring. We shall explain how solving Raven's Progressive Matrices (RPMs) can be realized as computational problems in algebra, which combine various well-known algebraic subroutines that include: Computing the Grobner basis of an ideal, checking for ideal containment, etc. Crucially, the additional algebraic structure satisfied by ideals allows for more operations on ideals beyond set-theoretic operations. Our algebraic machine reasoning framework is not only able to select the correct answer from a given answer set, but also able to generate the correct answer with only the question matrix given. Experiments on the I-RAVEN dataset yield an overall 93.2% accuracy, which significantly outperforms the current state-of-the-art accuracy of 77.0% and exceeds human performance at 84.4% accuracy.

关键词： and reasoning language 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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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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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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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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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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SOOD: Towards Semi-Supervised Oriented Object Detection

SOOD: Towards Semi-Supervised Oriented Object Detection

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

作者： Hua, Wei Liang, Dingkang Li, Jingyu Liu, Xiaolong Zou, Zhikang Ye, Xiaoqing Bai, Xiang Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Baidu Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

Semi-Supervised Object Detection (SSOD), aiming to explore unlabeled data for boosting object detectors, has become an active task in recent years. However, existing SSOD approaches mainly focus on horizontal objects, leaving multi-oriented objects that are common in aerial images unexplored. This paper proposes a novel Semi-supervised Oriented Object Detection model, termed SOOD, built upon the mainstream pseudo-labeling framework. Towards oriented objects in aerial scenes, we design two loss functions to provide better supervision. Focusing on the orientations of objects, the first loss regularizes the consistency between each pseudo-label-prediction pair (includes a prediction and its corresponding pseudo label) with adaptive weights based on their orientation gap. Focusing on the layout of an image, the second loss regularizes the similarity and explicitly builds the many-to-many relation between the sets of pseudo-labels and predictions. Such a global consistency constraint can further boost semi-supervised learning. Our experiments show that when trained with the two proposed losses, SOOD surpasses the state-of-the-art SSOD methods under various settings on the DOTA-v1.5 benchmark. The code will be available at https://***/HamPerdredes/SOOD.

关键词： detection recognition: Categorization retrieval

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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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Deep Discriminative Spatial and Temporal Network for Efficient Video Deblurring

Deep Discriminative Spatial and Temporal Network for Efficie...

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

作者： Pan, Jinshan Xu, Boming Dong, Jiangxin Ge, Jianjun Tang, Jinhui Nanjing Univ Sci & Technol Nanjing Peoples R China China Elect Technol Grp Corp Beijing Peoples R China

ISBN: (纸本)9798350301298

How to effectively explore spatial and temporal information is important for video deblurring. In contrast to existing methods that directly align adjacent frames without discrimination, we develop a deep discriminative spatial and temporal network to facilitate the spatial and temporal feature exploration for better video deblurring. We first develop a channel-wise gated dynamic network to adaptively explore the spatial information. As adjacent frames usually contain different contents, directly stacking features of adjacent frames without discrimination may affect the latent clear frame restoration. Therefore, we develop a simple yet effective discriminative temporal feature fusion module to obtain useful temporal features for latent frame restoration. Moreover, to utilize the information from long-range frames, we develop a wavelet-based feature propagation method that takes the discriminative temporal feature fusion module as the basic unit to effectively propagate main structures from long-range frames for better video deblurring. We show that the proposed method does not require additional alignment methods and performs favorably against state-of-the-art ones on benchmark datasets in terms of accuracy and model complexity.

关键词： Low-level vision

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