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AnnoSearch: Image Auto-Annotation by Search

AnnoSearch: Image Auto-Annotation by Search

引用

conference on computer vision and pattern recognition (CVPR)

作者： Xin-Jing Wang Lei Zhang Feng Jing Wei-Ying Ma Microsoft Research Asia Beijing China

Although it has been studied for several years by computer vision and machine learning communities, image annotation is still far from practical. In this paper, we present AnnoSearch, a novel way to annotate images using search and data mining technologies. Leveraging the Web-scale images, we solve this problem in two-steps: 1) searching for semantically and visually similar images on the Web, 2) and mining annotations from them. Firstly, at least one accurate keyword is required to enable text-based search for a set of semantically similar images. Then content-based search is performed on this set to retrieve visually similar images. At last, annotations are mined from the descriptions (titles, URLs and surrounding texts) of these images. It worth highlighting that to ensure the efficiency, high dimensional visual features are mapped to hash codes which significantly speed up the content-based search process. Our proposed approach enables annotating with unlimited vocabulary, which is impossible for all existing approaches. Experimental results on real web images show the effectiveness and efficiency of the proposed algorithm.

关键词： Image databases computer vision Asia Machine learning Image retrieval Data mining Content based retrieval Uniform resource locators Vocabulary Digital images

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Distribution-Aware Multi-Label FixMatch for Semi-Supervised Learning on CheXpert

Distribution-Aware Multi-Label FixMatch for Semi-Supervised ...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sontje Ihler Felix Kuhnke Timo Kuhlgatz Thomas Seel Institute of Mechatronic Systems Leibniz Universität Hannover

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Semi-supervised learning (SSL) has achieved remarkable success for multiclass classification in recent years, yielding a promising solution for medical image classification where labeled data is scarce but unlabeled images are accessible. In the context of multi-label problems however, SSL is still under-explored. In this work we adapt Fix-Match to the multi-label scenario, specifically focusing on CheXpert, a multi-label chest X-ray classification dataset which is imbalanced and only partially labeled. Leveraging distribution alignment, our proposed method, ML-FixMatch+DA, achieves solid performance gains in SSL tasks (AUC: +2.6%) and in a missing label scenario (AUC: +1.9%). In contrast to previous work we achieve a performance gain on CheXpert using FixMatch. We show that in contrast to multiclass FixMatch, where distribution alignment is optional, it is essential for multi-label FixMatch to handle class imbalance and generate reliable (positive and negative) pseudo-labels. Our pseudo-label selection is based on a single threshold for all classes and handles imbalance with no prior knowledge on label distributions. Our adaptation keeps the simplicity of the original multi-class FixMatch with no added hyperparameters (even for imbalanced data) and demonstrates the feasibility of simple SSL for multi-label problems, filling a crucial gap in the literature.

关键词： Semisupervised learning Performance gain Solids Filling Robustness pattern recognition Mirrors

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Neighborhood Aided Implicit Active Contours

Neighborhood Aided Implicit Active Contours

引用

conference on computer vision and pattern recognition (CVPR)

作者： Huafeng Liu Yunmei Chen Wufan Chen State Key Laboratory of Modern Optical Instrumentation Zhejiang University Hangzhou China Department of Electrical and Electronic Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We have developed a geometric deformable model that employs neighborhood influence to achieve robust segmentation for noisy and broken edges. The fundamental power of this strategy rests with the explicitly combination of regional inter-point constraints, image forces, and a priori boundary information for each geometric contour point within its adaptively determined local influence domain. This formulation thus naturally unifies the essences of the geometric and parametric snakes through automatic local scale selection, and exhibits their respective fundamental strengths of allowing stable boundary detection when the edge information is weak and possibly discontinuous, while maintaining the abilities to handle topological changes during front evolution. In particular, this paper presents an implementation of the method through local integration of the level set function and the image/prior-driven evolution forces, where the resulting partial differential equation is solved numerically using standard finite difference method. Experimental results on synthetic and real images demonstrate its superior performance.

关键词： Active contours Shape Deformable models Laboratories Mathematics Robustness Image segmentation Image edge detection Level set computer vision

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Covert Video Classification by Codebook Growing pattern

Covert Video Classification by Codebook Growing Pattern

引用

IEEE conference on computer vision and pattern recognition workshops

作者： Liang Du Chiu C. Tan Haitao Lang Jie Wu Ying-Li Tian Haibin Ling Temple University Beijing University of Chemical Technology City University of New York

Recent advances in visual data acquisition and Internet technologies make it convenient and popular to collect and share videos. These activities, however, also raise the issue of privacy invasion. One potential privacy threat is the unauthorized capture and/or sharing of covert videos, which are recorded without the awareness of the subject(s) in the video. Automatic classification of such videos can provide an important basis toward addressing relevant privacy issues. The task is very challenging due to the large intra-class variation and between-class similarity, since there is no limit in the content of a covert video and it may share very similar content with a regular video. The challenge brings troubles when applying existing content-based video analysis methods to covert video classification. In this paper, we propose a novel descriptor, codebook growing pattern (CGP), which is derived from latent Dirichlet allocation (LDA) over optical flows. Given an input video V, we first represent it with a sequence of histograms of optical flow (HOF). After that, these HOFs are fed into LDA to dynamically generate the codebook for V. The CGP descriptor is then defined as the growing codebook sizes in the LDA procedure. CGP fits naturally for covert video representation since (1) optical flows can capture the camera motion that characterizes the covert video acquisition, and (2) CGP by itself is insensitive to video content. To evaluate the proposed approach, we collected a large covert video dataset, the first such dataset to our knowledge, and tested the proposed method on the dataset. The results show clearly the effectiveness of the proposed approach in comparison with other state-of-the-art video classification algorithms.

关键词： Privacy Resource management Cameras Kernel Visualization Histograms Surveillance

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Space-Time Video Montage

Space-Time Video Montage

引用

conference on computer vision and pattern recognition (CVPR)

作者： Hong-Wen Kang Y. Matsushita Xiaoou Tang Xue-Quan Chen University of Science and Technology Hefei China University of Science and Technology of China Hefei P.R. China Microsoft Research Asia Beijing P.R. China China

Conventional video summarization methods focus predominantly on summarizing videos along the time axis, such as building a movie trailer: The resulting video trailer tends to retain much empty space in the background of the video frames while discarding much informative video content due to size limit. In this paper we propose a novel spacetime video summarization method which we call space-time video montage. The method simultaneously analyzes both the spatial and temporal injbrmation distribution in a video sequence, and extracts the visually informative space-time portions of the input videos. The informative video porlions are represented in volumetric layers. The layers are then packrd together in a smull ouzput video volume such that the total amount of visual information in the video volume is maximized. To achieve the packing process, we develop a new algorithm based upon the first-fit and Graph cut optimization techniques. Since our method is uble to cut off spatially und temporally less informative portions, it is uble to generate much more compact yet highly informative output videos. The effecliveness of our method is validated by extensive experiments over a wide variety of videos.

关键词： Videoconference Video sequences computer vision pattern recognition Space technology Motion pictures Data security Asia Streaming media Data mining

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Knowledge Distillation for Efficient Instance Semantic Segmentation with Transformers

Knowledge Distillation for Efficient Instance Semantic Segme...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Maohui Li Michael Halstead Chris McCool University of Bonn Lamarr Institute for Machine Learning and Artificial Intelligence

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Instance-based semantic segmentation provides detailed per-pixel scene understanding information crucial for both computer vision and robotics applications. However, state-of-the-art approaches such as Mask2Former are computationally expensive and reducing this computational burden while maintaining high accuracy remains challenging. Knowledge distillation has been regarded as a potential way to compress neural networks, but to date limited work has explored how to apply this to distill information from the output queries of a model such as *** this paper, we match the output queries of the student and teacher models to enable a query-based knowledge distillation scheme. We independently match the teacher and the student to the groundtruth and use this to define the teacher to student relationship for knowledge distillation. Using this approach we show that it is possible to perform knowledge distillation where the student models can have a lower number of queries and the backbone can be changed from a Transformer architecture to a convolutional neural network architecture. Experiments on two challenging agricultural datasets, sweet pepper (BUP20) and sugar beet (SB20), and Cityscapes demonstrate the efficacy of our approach. Across the three datasets the student models obtain an average absolute performance improvement in AP of 1.8 and 1.9 points for ResNet-50 and Swin-Tiny backbone respectively. To the best of our knowledge, this is the first work to propose knowledge distillation schemes for instance semantic segmentation with transformer-based models.

关键词： Knowledge engineering computer vision Semantic segmentation Computational modeling Impedance matching Neural networks computer architecture

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CSG0: Continual Urban Scene Generation with Zero Forgetting

CSG0: Continual Urban Scene Generation with Zero Forgetting

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Himalaya Jain Tuan-Hung Vu Patrick Pé rez Matthieu Cord Valeo.ai Paris France Sorbonne University Paris France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in this work a continual scene generation setup in which GANs are trained on a stream of distinct domains; ideally, the learned models should eventually be able to generate new scenes in all seen domains. This setup reflects the real-life scenario where data are continuously acquired in different places at different times. In such a continual setup, we aim for learning with zero forgetting, i.e., with no degradation in synthesis quality over earlier domains due to catastrophic forgetting. To this end, we introduce a novel framework that not only (i) enables seamless knowledge transfer in continual training but also (ii) guarantees zero forgetting with a small overhead cost. While being more memory efficient, thanks to continual learning, our model obtains better synthesis quality as compared against the brute-force solution that trains one full model for each domain. Especially, under extreme low-data regimes, our approach outperforms the brute-force one by a large margin.

关键词： Training Visualization Costs Semantics Memory management Generative adversarial networks pattern recognition

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Optimizing Binary MRFs via Extended Roof Duality

Optimizing Binary MRFs via Extended Roof Duality

引用

conference on computer vision and pattern recognition (CVPR)

作者： Carsten Rother Vladimir Kolmogorov Victor Lempitsky Martin Szummer Microsoft Research Limited Cambridge UK University College London UK Moscow State University Russia

Many computer vision applications rely on the efficient optimization of challenging, so-called non-submodular, binary pairwise MRFs. A promising graph cut based approach for optimizing such MRFs known as "roof duality" was recently introduced into computer vision. We study two methods which extend this approach. First, we discuss an efficient implementation of the "probing" technique introduced recently by Bows et al. (2006). It simplifies the MRF while preserving the global optimum. Our code is 400-700 faster on some graphs than the implementation of the work of Bows et al. (2006). Second, we present a new technique which takes an arbitrary input labeling and tries to improve its energy. We give theoretical characterizations of local minima of this procedure. We applied both techniques to many applications, including image segmentation, new view synthesis, super-resolution, diagram recognition, parameter learning, texture restoration, and image deconvolution. For several applications we see that we are able to find the global minimum very efficiently, and considerably outperform the original roof duality approach. In comparison to existing techniques, such as graph cut, TRW, BP, ICM, and simulated annealing, we nearly always find a lower energy.

关键词： computer vision Application software Labeling Image segmentation Energy resolution Image resolution Image recognition Image restoration Deconvolution Simulated annealing

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Dformer: Learning Efficient Image Restoration with Perceptual Guidance

Dformer: Learning Efficient Image Restoration with Perceptua...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nodirkhuja Khudjaev Roman Tsoy S M A Sharif Azamat Myrzabekov Seongwan Kim Jaeho Lee Opt-AI Inc. LG Sciencepark Seoul South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image restoration tasks incorporate widespread real-world application. Apart from its significant practicability, generic deep image restoration methods still fail to handle complex tasks, like shadow removal, low-light enhancement, etc. This paper addresses the limitations of existing image restoration methods by introducing a novel deep architecture. The proposed model incorporates illumination mapping inspired by the Retinex theory within a double encoder-decoder network. Additionally, it utilizes a multi-head cross-attention mechanism to correlate input and reconstructed images to generate plausible and refined images. The proposed model employs a perceptual optimization strategy to tackle intricate restoration tasks effectively. It surpasses state-of-the-art methods in demanding tasks such as shadow removal, low-light image enhancement, and blind compress image enhancement, all while utilizing fewer trainable parameters. Our method is selected among the top solutions in the New Trends in Image Restoration and Enhancement’24 (NTIRE) challenge for shadow removal, securing a top position without resorting to score-boosting techniques such as ensembling.

关键词： Image coding Image edge detection Lighting Deep architecture Market research Image restoration pattern recognition

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Effective and Generic Structure from Motion using Angular Error

Effective and Generic Structure from Motion using Angular Er...

引用

International conference on pattern recognition

作者： M. Lhuillier LASMEA UMR CNRS 6602 Université Blaise Pascal Aubiere France

ISBN: (纸本)0769525210

Generic camera modeling using raxels and associated methods was recently introduced in computer vision. The main advantage is the applicability for any camera model, which contrasts to the many specific methods designed for a single camera model. This paper introduces a bundle adjustment based on angular error for the generic structure from motion problem. Experiments include automatic, robust and optimal estimation of scene structure and camera motion from a long image sequence acquired by a handheld non-central (calibrated) catadioptric camera

关键词： Cameras Image sequences Layout Stereo vision computer errors computer vision Design methodology Robustness Motion estimation Image segmentation

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