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An Analytical Framework for Trusted Machine Learning and computer vision Running with Blockchain

An Analytical Framework for Trusted Machine Learning and Com...

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

作者： Wang, Tao Du, Maggie Wu, Xinmin He, Taiping SAS Inst Inc Cary NC 27513 USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Machine learning algorithms often use data from databases that are mutable;therefore, the data and the results of machine learning cannot be fully trusted. Also, the learning process is often difficult to automate. A unified analytical framework for trusted machine learning has been presented in the literature to address both issues. It is proposed building a trusted machine learning system by using blockchain technology, which can store data in a permanent and immutable way. In addition, smart contracts on blockchain are used to automate the machine learning process. However, in such a blockchain framework, data efficiency is a big concern, because it is very expensive to store a large amount of data on blockchain. On the other hand, machine learning based computer vision systems often rely on a lot of data. Therefore, to fully leverage a blockchain-based machine learning framework for computer vision systems, data efficiency issues must be addressed. This paper investigates how to enhance data efficiency in such a framework to bring computer vision systems to the edge. It presents a three-step approach. First, a lightweight machine learning model is trained on the server layer. Second, the trained model is saved in a special binary data format for data efficiency. Finally, the streaming layer takes these binary data as input and scores incoming new data in an online fashion. Real-time semantic segmentation for autonomous driving is used as an example to demonstrate how this approach works. This paper makes the following contributions. First, it improves the analytical framework for trusted computer vision systems based on blockchain. Second, the real-time semantic segmentation example shows how data-efficient learning for computer vision can be performed on the edge.

关键词： Machine learning computer vision Computational modeling Servers Contracts Automation

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IFSeg: Image-free Semantic Segmentation via vision-Language Model

IFSeg: Image-free Semantic Segmentation via Vision-Language ...

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conference on computer vision and pattern recognition (CVPR)

作者： Sukmin Yun Seong Hyeon Park Paul Hongsuck Seo Jinwoo Shin Korea Advanced Institute of Science and Technology (KAIST) Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Google Research

vision-language (VL) pre-training has recently gained much attention for its transferability and flexibility in novel concepts (e.g., cross-modality transfer) across various visual tasks. However, VL-driven segmentation has been under-explored, and the existing approaches still have the burden of acquiring additional training images or even segmentation annotations to adapt a VL model to downstream segmentation tasks. In this paper, we introduce a novel image-free segmentation task where the goal is to perform semantic segmentation given only a set of the target semantic categories, but without any task-specific images and annotations. To tackle this challenging task, our proposed method, coined IFSeg, generates VL-driven artificial image-segmentation pairs and updates a pretrained VL model to a segmentation task. We construct this artificial training data by creating a 2D map of random semantic categories and another map of their corresponding word tokens. Given that a pretrained VL model projects visual and text tokens into a common space where tokens that share the semantics are located closely, this artificially generated word map can replace the real image inputs for such a VL model. Through an extensive set of experiments, our model not only establishes an effective baseline for this novel task but also demonstrates strong performances compared to existing methods that rely on stronger supervision, such as task-specific images and segmentation masks. Code is available at https://***/alinlab/ifseg.

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Position-Guided Text Prompt for vision-Language Pre-Training

Position-Guided Text Prompt for Vision-Language Pre-Training

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jinpeng Wang Pan Zhou Mike Zheng Shou Shuicheng Yan Show Lab National University of Singapore Sea AI Lab

vision-Language Pre-Training (VLP) has shown promising capabilities to align image and text pairs, facilitating a broad variety of cross-modal learning tasks. However, we observe that VLP models often lack the visual grounding/localization capability which is critical for many downstream tasks such as visual reasoning. In this work, we propose a novel Position-guided Text Prompt (PTP) paradigm to enhance the visual grounding ability of cross-modal models trained with VLP. Specifically, in the VLP phase, PTP divides the image into N x N blocks, and identifies the objects in each block through the widely used object detector in VLP. It then reformulates the visual grounding task into a fill-in-the-blank problem given a PTP by encouraging the model to predict the objects in the given blocks or regress the blocks of a given object, e.g. filling “[P]” or “[O]” in a PTP “The block [P] has a [O]”. This mechanism improves the visual grounding capability of VLP models and thus helps them better handle various downstream tasks. By introducing PTP into several state-of-the-art VLP frameworks, we observe consistently significant improvements across representative cross-modal learning model architectures and several benchmarks, e.g. zero-shot Flickr30K Retrieval (+4.8 in average recall@1) for ViLT [16] baseline, and COCO Captioning (+5.3 in CIDEr) for SOTA BLIP [19] baseline. Moreover, PTP achieves comparable results with object-detector based methods [8, 23, 45], and much faster inference speed since PTP discards its object detector for inference while the later cannot.

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Self-supervised vision Transformers for Land-cover Segmentation and Classification

Self-supervised Vision Transformers for Land-cover Segmentat...

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

作者： Linus Scheibenreif Joë lle Hanna Michael Mommert Damian Borth AIML Lab School of Computer Science University of St. Gallen St. Gallen Switzerland

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Transformer models have recently approached or even surpassed the performance of ConvNets on computer vision tasks like classification and segmentation. To a large degree, these successes have been enabled by the use of large-scale labelled image datasets for supervised pre-training. This poses a significant challenge for the adaption of vision Transformers to domains where datasets with millions of labelled samples are not available. In this work, we bridge the gap between ConvNets and Transformers for Earth observation by self-supervised pre-training on large-scale unlabelled remote sensing data 1 . We show that self-supervised pre-training yields latent task-agnostic representations that can be utilized for both land cover classification and segmentation tasks, where they significantly outperform the fully supervised baselines. Additionally, we find that subsequent fine-tuning of Transformers for specific downstream tasks performs on-par with commonly used ConvNet architectures. An ablation study further illustrates that the labelled dataset size can be reduced to one-tenth after self-supervised pre-training while still maintaining the performance of the fully supervised approach.

关键词： Training Earth computer vision Image segmentation Computational modeling conferences Transformers

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GCFAgg: Global and Cross-View Feature Aggregation for Multi-View Clustering

GCFAgg: Global and Cross-View Feature Aggregation for Multi-...

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conference on computer vision and pattern recognition (CVPR)

作者： Weiqing Yan Yuanyang Zhang Chenlei Lv Chang Tang Guanghui Yue Liang Liao Weisi Lin School of Computer and Control Engineering Yantai University Yantai China School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer China University of Geosciences Wuhan China School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China

Multi-view clustering can partition data samples into their categories by learning a consensus representation in unsupervised way and has received more and more attention in recent years. However, most existing deep clustering methods learn consensus representation or view-specific representations from multiple views via view-wise aggregation way, where they ignore structure relationship of all samples. In this paper, we propose a novel multi-view clustering network to address these problems, called Global and Cross-view Feature Aggregation for Multi-View Clustering (GCFAggMVC). Specifically, the consensus data presentation from multiple views is obtained via cross-sample and cross-view feature aggregation, which fully explores the complementary of similar samples. Moreover, we align the consensus representation and the view-specific representation by the structure-guided contrastive learning module, which makes the view-specific representations from different samples with high structure relationship similar. The proposed module is a flexible multi-view data representation module, which can be also embedded to the incomplete multi-view data clustering task via plugging our module into other frameworks. Extensive experiments show that the proposed method achieves excellent performance in both complete multi-view data clustering tasks and incomplete multi-view data clustering tasks.

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A New Path: Scaling vision-and-Language Navigation with Synthetic Instructions and Imitation Learning

A New Path: Scaling Vision-and-Language Navigation with Synt...

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conference on computer vision and pattern recognition (CVPR)

作者： Aishwarya Kamath Peter Anderson Su Wang Jing Yu Koh Alexander Ku Austin Waters Yinfei Yang Jason Baldridge Zarana Parekh New York University Google Research Carnegie Mellon University Apple

Recent studies in vision-and-Language Navigation (VLN) train RL agents to execute natural-language navigation instructions in photorealistic environments, as a step towards robots that can follow human instructions. However, given the scarcity of human instruction data and limited diversity in the training environments, these agents still struggle with complex language grounding and spatial language understanding. Pretraining on large text and image-text datasets from the web has been extensively explored but the improvements are limited. We investigate large-scale augmentation with synthetic instructions. We take 500+ indoor environments captured in densely-sampled 360 ° panoramas, construct navigation trajectories through these panoramas, and generate a visually-grounded instruction for each trajectory using Marky [63], a high-quality multilingual navigation instruction generator. We also synthesize image observations from novel viewpoints using an image-to-image GAN [27]. The resulting dataset of 4.2M instruction-trajectory pairs is two orders of magnitude larger than existing human-annotated datasets, and contains a wider variety of environments and viewpoints. To efficiently leverage data at this scale, we train a simple transformer agent with imitation learning. On the challenging RxR dataset, our approach outperforms all existing RL agents, improving the state-of-the-art NDTW from 71.1 to 79.1 in seen environments, and from 64.6 to 66.8 in unseen test environments. Our work points to a new path to improving instruction-following agents, emphasizing large-scale training on near-human quality synthetic instructions.

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CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion

CDDFuse: Correlation-Driven Dual-Branch Feature Decompositio...

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conference on computer vision and pattern recognition (CVPR)

作者： Zixiang Zhao Haowen Bai Jiangshe Zhang Yulun Zhang Shuang Xu Zudi Lin Radu Timofte Luc Van Gool Xi'an Jiaotong University Computer Vision Lab ETH Zürich Research and Development Institute of Northwestern Polytechnical University in Shenzhen Northwestern Polytechnical University Harvard University University of Würzburg

Multi-modality (MM) image fusion aims to render fused images that maintain the merits of different modalities, e.g., functional highlight and detailed textures. To tackle the challenge in modeling cross-modality features and decomposing desirable modality-specific and modality-shared features, we propose a novel Correlation-Driven feature Decomposition Fusion (CDDFuse) network. Firstly, CDDFuse uses Restormer blocks to extract cross-modality shallow features. We then introduce a dual-branch Transformer-CNN feature extractor with Lite Transformer (LT) blocks leveraging long-range attention to handle low-frequency global features and Invertible Neural Networks (INN) blocks focusing on extracting high-frequency local information. A correlation-driven loss is further proposed to make the low-frequency features correlated while the high-frequency features uncorrelated based on the embedded information. Then, the LT-based global fusion and INN-based local fusion layers output the fused image. Extensive experiments demonstrate that our CDDFuse achieves promising results in multiple fusion tasks, including infrared-visible image fusion and medical image fusion. We also show that CDDFuse can boost the performance in downstream infrared-visible semantic segmentation and object detection in a unified benchmark. The code is available at https://***/haozixiang1228/MMIF-CDDFuse.

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Distilling Cross-Temporal Contexts for Continuous Sign Language recognition

Distilling Cross-Temporal Contexts for Continuous Sign Langu...

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conference on computer vision and pattern recognition (CVPR)

作者： Leming Guo Wanli Xue Qing Guo Bo Liu Kaihua Zhang Tiantian Yuan Shengyong Chen School of Computer Science and Engineering Tianjin University of Technology Centre for Frontier AI Research (CFAR) A*STAR Singapore Walmart Global Tech Sunnyvale CA USA School of Computer and Software Nanjing University of Information Science and Technology Technical College for the Deaf Tianjin University of Technology

Continuous sign language recognition (CSLR) aims to recognize glosses in a sign language video. State-of-the-art methods typically have two modules, a spatial perception module and a temporal aggregation module, which are jointly learned end-to-end. Existing results in [9, 20, 25, 36] have indicated that, as the frontal component of the over-all model, the spatial perception module used for spatial feature extraction tends to be insufficiently trained. In this paper, we first conduct empirical studies and show that a shallow temporal aggregation module allows more thor-ough training of the spatial perception module. However, a shallow temporal aggregation module cannot well capture both local and global temporal context information in sign language. To address this dilemma, we propose a cross-temporal context aggregation (CTCA) model. Specifically, we build a dual-path network that contains two branches for perceptions of local temporal context and global temporal context. We further design a cross-context knowledge distil-lation learning objective to aggregate the two types of con-text and the linguistic prior. The knowledge distillation en-ables the resultant one-branch temporal aggregation mod-ule to perceive local-global temporal and semantic context. This shallow temporal perception module structure facili-tates spatial perception module learning. Extensive exper-iments on challenging CSLR benchmarks demonstrate that our method outperforms all state-of-the-art methods.

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Cross-dataset Learning for Generalizable Land Use Scene Classification

Cross-dataset Learning for Generalizable Land Use Scene Clas...

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

作者： Dimitri Gominski Valé rie Gouet-Brunet Liming Chen Geography University of Copenhagen Copenhagen Denmark LaSTIG IGN Saint-Mand&#x00E9 France LIRIS Ecole Centrale Lyon Ecully France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Few-shot and cross-domain land use scene classification methods propose solutions to classify unseen classes or un-seen visual distributions, but are hardly applicable to real-world situations due to restrictive assumptions. Few-shot methods involve episodic training on restrictive training subsets with small feature extractors, while cross-domain methods are only applied to common classes. The underlying challenge remains open: can we accurately classify new scenes on new datasets? In this paper, we propose a new framework for few-shot, cross-domain classification. Our retrieval-inspired approach 1 exploits the interrelations in both the training and testing data to output class labels using compact descriptors. Results show that our method can accurately produce land-use predictions on unseen datasets and unseen classes, going beyond the traditional few-shot or cross-domain formulation, and allowing cross-dataset training.

关键词： Training Visualization computer vision Image analysis conferences Image retrieval Feature extraction

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Deep Stereo Video Inpainting

Deep Stereo Video Inpainting

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conference on computer vision and pattern recognition (CVPR)

作者： Zhiliang Wu Changchang Sun Hanyu Xuan Yan Yan School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Illinois Institute of Technology USA School of Big Data and Statistics Anhui University China

Stereo video inpainting aims to fill the missing regions on the left and right views of the stereo video with plausible content simultaneously. Compared with the single video inpainting that has achieved promising results using deep convolutional neural networks, inpainting the missing regions of stereo video has not been thoroughly explored. In essence, apart from the spatial and temporal consistency that single video inpainting needs to achieve, another key challenge for stereo video inpainting is to maintain the stereo consistency between left and right views and hence alleviate the 3D fatigue for viewers. In this paper, we propose a novel deep stereo video inpainting network named SVINet, which is the first attempt for stereo video inpainting task utilizing deep convolutional neural networks. SVINet first utilizes a self-supervised flow-guided deformable temporal alignment module to align the features on the left and right view branches, respectively. Then, the aligned features are fed into a shared adaptive feature aggregation module to generate missing contents of their respective branches. Finally, the parallax attention module (PAM) that uses the cross-view information to consider the significant stereo correlation is introduced to fuse the completed features of left and right views. Furthermore, we develop a stereo consistency loss to regularize the trained parameters, so that our model is able to yield high-quality stereo video inpainting results with better stereo consistency. Experimental results demonstrate that our SVINet outperforms state-of-the-art single video inpainting models.

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