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VMRNN: Integrating vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting

VMRNN: Integrating Vision Mamba and LSTM for Efficient and A...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yujin Tang Peijie Dong Zhenheng Tang Xiaowen Chu Junwei Liang AI Thrust The Hong Kong University of Science and Technology (Guangzhou) DSA Thrust The Hong Kong University of Science and Technology (Guangzhou) Department of Computer Science Hong Kong Baptist University Department of Computer Science and Engineering The Hong Kong University of Science and Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Combining Convolutional Neural Networks (CNNs) or vision Transformers(ViTs) with Recurrent Neural Networks (RNNs) for spatiotemporal forecasting has yielded unparalleled results in predicting temporal and spatial dynamics. However, modeling extensive global information remains a formidable challenge; CNNs are limited by their narrow receptive fields, and ViTs struggle with the intensive computational demands of their attention mechanisms. The emergence of recent Mamba-based architectures has been met with enthusiasm for their exceptional long-sequence modeling capabilities, surpassing established vision models in efficiency and accuracy, which motivates us to develop an innovative architecture tailored for spatiotemporal forecasting. In this paper, we propose the VMRNN cell, a new recurrent unit that integrates the strengths of vision Mamba blocks with LSTM. We construct a network centered on VMRNN cells to tackle spatiotemporal prediction tasks effectively. Our extensive evaluations show that our proposed approach secures competitive results on a variety of tasks while maintaining a smaller model size. Our code is available at https://***/yyyujintang/VMRNN-PyTorch.

关键词： Recurrent neural networks Accuracy Computational modeling Microprocessors computer architecture Predictive models Transformers

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Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution

Training Transformer Models by Wavelet Losses Improves Quant...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Cansu Korkmaz A. Murat Tekalp College of Engineering and KUIS AI Center Koc University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Transformer-based models have achieved remarkable results in low-level vision tasks including image super-resolution (SR). However, early Transformer-based approaches that rely on self-attention within non-overlapping windows encounter challenges in acquiring global information. To activate more input pixels globally, hybrid attention models have been proposed. Moreover, training by solely minimizing pixel-wise RGB losses, such as l 1 , have been found inadequate for capturing essential high-frequency details. This paper presents two contributions: i) We introduce convolutional non-local sparse attention (NLSA) blocks to extend the hybrid transformer architecture in order to further enhance its receptive field. ii) We employ wavelet losses to train Transformer models to improve quantitative and subjective performance. While wavelet losses have been explored previously, showing their power in training Transformer-based SR models is novel. Our experimental results demonstrate that the proposed model provides state-of-the-art PSNR results as well as superior visual performance across various benchmark datasets.

关键词： Wavelet transforms Training Visualization Computational modeling conferences Superresolution computer architecture

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Contrastive Domain Adaptation

Contrastive Domain Adaptation

引用

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

作者： Thota, Mamatha Leontidis, Georgios Univ Lincoln Sch Comp Sci Lincoln LN6 7TS England Univ Aberdeen Dept Comp Sci Aberdeen AB24 3UE Scotland

ISBN: (纸本)9781665448994

Recently, contrastive self-supervised learning has become a key component for learning visual representations across many computer vision tasks and benchmarks. However, contrastive learning in the context of domain adaptation remains largely underexplored. In this paper, we propose to extend contrastive learning to a new domain adaptation setting, a particular situation occurring where the similarity is learned and deployed on samples following different probability distributions without access to labels. Contrastive learning learns by comparing and contrasting positive and negative pairs of samples in an unsupervised setting without access to source and target labels. We have developed a variation of a recently proposed contrastive learning framework that helps tackle the domain adaptation problem, further identifying and removing possible negatives similar to the anchor to mitigate the effects of false negatives. Extensive experiments demonstrate that the proposed method adapts well, and improves the performance on the downstream domain adaptation task.

关键词： Training computer vision Adaptation models Visualization Machine learning algorithms Pipelines Machine learning

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SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-Image Translation while Maintaining Stereo Constraint

SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Vasudha Venkatesan Daniel Panangian Mario Fuentes Reyes Ksenia Bittner University of Freiburg German Aerospace Center (DLR)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of remote sensing, the scarcity of stereo-matched and particularly lack of accurate ground truth data often hinders the training of deep neural networks. The use of synthetically generated images as an alternative, alleviates this problem but suffers from the problem of domain generalization. Unifying the capabilities of image-to-image translation and stereo-matching presents an effective solution to address the issue of domain generalization. Current methods involve combining two networks—an unpaired image-to-image translation network and a stereo-matching network—while jointly optimizing them. We propose an edge-aware GAN-based network that effectively tackles both tasks simultaneously. We obtain edge maps of input images from the Sobel operator and use it as an additional input to the encoder in the generator to enforce geometric consistency during translation. We additionally include a warping loss calculated from the translated images to maintain the stereo consistency. We demonstrate that our model produces qualitatively and quantitatively superior results than existing models, and its applicability extends to diverse domains, including autonomous driving.

关键词： Training computer vision Accuracy Image edge detection conferences Generative adversarial networks Generators

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One Embedding to Predict Them All: Visible and Thermal Universal Face Representations for Soft Biometric Estimation via vision Transformers

One Embedding to Predict Them All: Visible and Thermal Unive...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nelida Mirabet-Herranz Chiara Galdi Jean-Luc Dugelay Campus SophiaTech EURECOM France

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Human faces encode a vast amount of information including not only uniquely distinctive features of the individual but also demographic information such as a person’s age, gender, and weight. Such information is referred to as soft-biometrics, which are physical, behavioral or adhered human characteristics, classifiable in pre–defined human compliant categories. As we often say ’one look is worth a thousand words’. vision Transformers have emerged as a powerful deep learning architecture able to achieve accurate classifications for different computer vision tasks, but these models have not been yet applied to soft-biometrics. In this work, we propose the Bidirectional Encoder Face representation from image Transformers (BEFiT), a model that leverages the multi-attention mechanisms to capture local and global features and produce a multi-purpose face embedding. This unique embedding enables the estimation of different demographics without having to re-train the model for each soft-biometric trait, ensuring high efficiency without compromising accuracy. Our approach explores the use of visible and thermal images to achieve powerful face embedding in different light spectra. We demonstrate that the BEFiT embeddings can capture essential information for gender, age, and weight estimation, surpassing the performance of dedicated deep learning structures for the estimation of a single soft biometric trait. The code of BEFiT implementation is publicly available 1

关键词： Biometrics Training computer vision Accuracy Computational modeling Estimation Transformers

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Dot Distance for Tiny Object Detection in Aerial Images

Dot Distance for Tiny Object Detection in Aerial Images

引用

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

作者： Xu, Chang Wang, Jinwang Yang, Wen Yu, Lei Wuhan Univ Sch Elect Informat Wuhan Peoples R China

ISBN: (纸本)9781665448994

Object detection has achieved great progress with the development of anchor-based and anchor-free detectors. However, the detection of tiny objects is still challenging due to the lack of appearance information. In this paper, we observe that Intersection over Union (IoU), the most widely used metric in object detection, is sensitive to slight offsets between predicted bounding boxes and ground truths when detecting tiny objects. Although some new metrics such as GIoU, DIoU and CIoU are proposed, their performance on tiny object detection is still below the expected level by a large margin. In this paper, we propose a simple but effective new metric called Dot Distance (DotD) for tiny object detection where DotD is defined as normalized Euclidean distance between the center points of two bounding boxes. Extensive experiments on tiny object detection dataset show that anchor-based detectors' performance is highly improved over their baselines with the application of DotD.

关键词： Measurement computer vision conferences Object detection Detectors Euclidean distance pattern recognition

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Learning Surface Terrain Classifications from Ground Penetrating Radar

Learning Surface Terrain Classifications from Ground Penetra...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Anja Sheppard Jason Brown Nilton Renno Katherine A. Skinner The University of Michigan Ann Arbor MI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Terrain classification is an important problem for mobile robots operating in extreme environments as it can aid downstream tasks such as autonomous navigation and planning. While RGB cameras are widely used for terrain identification, vision-based methods can suffer due to poor lighting conditions and occlusions. In this paper, we propose the novel use of Ground Penetrating Radar (GPR) for terrain characterization for mobile robot platforms. Our approach leverages machine learning for surface terrain classification from GPR data. We collect a new dataset consisting of four different terrain types, and present qualitative and quantitative results. Our results demonstrate that classification networks can learn terrain categories from GPR signals. Additionally, we integrate our GPR-based classification approach into a multimodal semantic mapping framework to demonstrate a practical use case of GPR for surface terrain classification on mobile robots. Overall, this work extends the usability of GPR sensors deployed on robots to enable terrain classification in addition to GPR’s existing scientific use cases.

关键词： Ground penetrating radar Surface waves Semantics Robot vision systems Sensor systems Sensors Planning

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EBSR: Feature Enhanced Burst Super-Resolution with Deformable Alignment

EBSR: Feature Enhanced Burst Super-Resolution with Deformabl...

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

作者： Luo, Ziwei Yu, Lei Mo, Xuan Li, Youwei Jia, Lanpeng Fan, Haoqiang Sun, Jian Liu, Shuaicheng Megvii Technol Beijing Peoples R China Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

ISBN: (纸本)9781665448994

We propose a novel architecture to handle the problem of multi frame super-resolution (MFSR). The proposed framework is known as Enhanced Burst Super-Resolution (EBSR), which divides the MFSR problem into three parts: alignment, fusion, and reconstruction. We propose a Feature Enhanced Pyramid Cascading and Deformable convolution (FEPCD) module to align multiple low-resolution burst images in the feature level. And then the aligned features are fused by a Cross Non-Local Fusion (CNLF) module. Finally, the SR image is reconstructed by the Long Range Concatenation Network (LRCN). In addition, we build a cascading residual pathway structure (CR) to improve the performance. We conduct several experiments to analyze and demonstrate these modules. Our EBSR model won the champion in the real track and second place in the synthetic track in the NTIRE21 Burst Super-Resolution Challenge.

关键词： Training computer vision Correlation Tracking Convolution conferences Superresolution

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NTIRE 2024 Challenge on HR Depth from Images of Specular and Transparent Surfaces

NTIRE 2024 Challenge on HR Depth from Images of Specular and...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Pierluigi Zama Ramirez Fabio Tosi Luigi Di Stefano Radu Timofte Alex Costanzino Matteo Poggi Samuele Salti Stefano Mattoccia Yangyang Zhang Cailin Wu Zhuangda He Shuangshuang Yin Jiaxu Dong Yangchenxu Liu Hao Jiang Jun Shi Yong A Yixiang Jin Dingzhe Li Bingxin Ke Anton Obukhov Tinafu Wang Nando Metzger Shengyu Huang Konrad Schindler Yachuan Huang Jiaqi Li Junrui Zhang Yiran Wang Zihao Huang Tianqi Liu Zhiguo Cao Pengzhi Li Jui-Lin Wang Wenjie Zhu Hui Geng Yuxin Zhang Long Lan Kele Xu Tao Sun Qisheng Xu Sourav Saini Aashray Gupta Sahaj K. Mistry Aryan Shukla Vinit Jakhetiya Sunil Jaiswal Yuejin Sun Zhuofan Zheng Yi Ning Jen-Hao Cheng Hou-I Liu Hsiang-Wei Huang Cheng-Yen Yang Zhongyu Jiang Yi-Hao Peng Aishi Huang Jenq-Neng Hwang

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reports on the NTIRE 2024 challenge on HR Depth From images of Specular and Transparent surfaces, held in conjunction with the New Trends in Image Restoration and Enhancement (NTIRE) workshop at CVPR 2024. This challenge aims to advance the research on depth estimation, specifically to address two of the main open issues in the field: high-resolution and non-Lambertian surfaces. The challenge proposes two tracks on stereo and single-image depth estimation, attracting about 120 registered participants. In the final testing stage, 2 and 8 participating teams submitted their models and fact sheets for the two tracks.

关键词： computer vision conferences Computational modeling Estimation Market research pattern recognition Image restoration

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Private-Shared Disentangled Multimodal VAE for Learning of Latent Representations

Private-Shared Disentangled Multimodal VAE for Learning of L...

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

作者： Lee, Mihee Pavlovic, Vladimir Rutgers State Univ Piscataway NJ 08854 USA

ISBN: (纸本)9781665448994

Multi-modal generative models represent an important family of deep models, whose goal is to facilitate representation learning on data with multiple views or modalities. However, current deep multi-modal models focus on the inference of shared representations, while neglecting the important private aspects of data within individual modalities. In this paper, we introduce a disentangled multi-modal variational autoencoder (DMVAE) that utilizes disentangled VAE strategy to separate the private and shared latent spaces of multiple modalities. We demonstrate the utility of DMVAE two image modalities of MNIST and Google Street View House Number (SVHN) datasets as well as image and text modalities from the Oxford-102 Flowers dataset. Our experiments indicate the essence of retaining the private representation as well as the private-shared disentanglement to effectively direct the information across multiple analysis-synthesis conduits.

关键词： computer vision conferences Computational modeling Data models pattern recognition Internet Task analysis

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