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A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift

A Differentiable Two-stage Alignment Scheme for Burst Image ...

引用

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

作者： Guo, Shi Yang, Xi Ma, Jianqi Ren, Gaofeng Zhang, Lei Hong Kong Polytech Univ Hong Kong Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Denoising and demosaicking are two essential steps to reconstruct a clean full-color image from the raw data. Recently, joint denoising and demosaicking (JDD) for burst images, namely JDD-B, has attracted much attention by using multiple raw images captured in a short time to reconstruct a single high-quality image. One key challenge of JDD-B lies in the robust alignment of image frames. State-of-the-art alignment methods in feature domain cannot effectively utilize the temporal information of burst images, where large shifts commonly exist due to camera and object motion. In addition, the higher resolution (e.g., 4K) of modern imaging devices results in larger displacement between frames. To address these challenges, we design a differentiable two-stage alignment scheme sequentially in patch and pixel level for effective JDD-B. The input burst images are firstly aligned in the patch level by using a differentiable progressive block matching method, which can estimate the offset between distant frames with small computational cost. Then we perform implicit pixel-wise alignment in full-resolution feature domain to refine the alignment results. The two stages are jointly trained in an end-to-end manner. Extensive experiments demonstrate the significant improvement of our method over existing JDD-B methods. Codes are available https://***/GuoShi28/2StageAlign.

关键词： Visualization computer vision Image resolution Codes Noise reduction Computational efficiency pattern recognition

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BiFormer: vision Transformer with Bi-Level Routing Attention

BiFormer: Vision Transformer with Bi-Level Routing Attention

引用

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

作者： Zhu, Lei Wang, Xinjiang Ke, Zhanghan Zhang, Wayne Lau, Rynson City Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9798350301298

As the core building block of vision transformers, attention is a powerful tool to capture long-range dependency. However, such power comes at a cost: it incurs a huge computation burden and heavy memory footprint as pair-wise token interaction across all spatial locations is computed. A series of works attempt to alleviate this problem by introducing handcrafted and content-agnostic sparsity into attention, such as restricting the attention operation to be inside local windows, axial stripes, or dilated windows. In contrast to these approaches, we propose a novel dynamic sparse attention via bi-level routing to enable a more flexible allocation of computations with content awareness. Specifically, for a query, irrelevant key-value pairs are first filtered out at a coarse region level, and then fine-grained token-to-token attention is applied in the union of remaining candidate regions (i.e., routed regions). We provide a simple yet effective implementation of the proposed bi-level routing attention, which utilizes the sparsity to save both computation and memory while involving only GPU-friendly dense matrix multiplications. Built with the proposed bi-level routing attention, a new general vision transformer, named BiFormer, is then presented. As BiFormer attends to a small subset of relevant tokens in a query adaptive manner without distraction from other irrelevant ones, it enjoys both good performance and high computational efficiency, especially in dense prediction tasks. Empirical results across several computer vision tasks such as image classification, object detection, and semantic segmentation verify the effectiveness of our design. Code is available at https://***/rayleizhu/BiFormer.

关键词： Deep learning architectures and techniques

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Robust Canonical Time Warping for the Alignment of Grossly Corrupted Sequences

Robust Canonical Time Warping for the Alignment of Grossly C...

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Panagakis, Yannis Nicolaou, Mihalis A. Zafeiriou, Stefanos Pantic, Maja Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England

ISBN: (纸本)9780769549897

Temporal alignment of human behaviour from visual data is a very challenging problem due to a numerous reasons, including possible large temporal scale differences, inter/intra subject variability and, more importantly, due to the presence of gross errors and outliers. Gross errors are often in abundance due to incorrect localization and tracking, presence of partial occlusion etc. Furthermore, such errors rarely follow a Gaussian distribution, which is the de-facto assumption in machine learning methods. In this paper, building on recent advances on rank minimization and compressive sensing, a novel, robust to gross errors temporal alignment method is proposed. While previous approaches combine the dynamic time warping (DTW) with low-dimensional projections that maximally correlate two sequences, we aim to learn two underlying projection matrices ( one for each sequence), which not only maximally correlate the sequences but, at the same time, efficiently remove the possible corruptions in any datum in the sequences. The projections are obtained by minimizing the weighted sum of nuclear and l(1) norms, by solving a sequence of convex optimization problems, while the temporal alignment is found by applying the DTW in an alternating fashion. The superiority of the proposed method against the state-of-the-art time alignment methods, namely the canonical time warping and the generalized time warping, is indicated by the experimental results on both synthetic and real datasets.

关键词： Gaussian distribution computer vision convex programming data visualisation image sequences learning (artificial intelligence) matrix algebra minimisation

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Pin the Memory: Learning to Generalize Semantic Segmentation

Pin the Memory: Learning to Generalize Semantic Segmentation

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

作者： Kim, Jin Lee, Jiyoung Park, Jungin Min, Dongbo Sohn, Kwanghoon Yonsei Univ Seoul South Korea NAVER AI Lab Seongnam South Korea Ewha Womans Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The rise of deep neural networks has led to several breakthroughs for semantic segmentation. In spite of this, a model trained on source domain often fails to work properly in new challenging domains, that is directly concerned with the generalization capability of the model. In this paper, we present a novel memory-guided domain generalization method for semantic segmentation based on meta-learning framework. Especially, our method abstracts the conceptual knowledge of semantic classes into categorical memory which is constant beyond the domains. Upon the meta-learning concept, we repeatedly train memory-guided networks and simulate virtual test to 1) learn how to memorize a domain-agnostic and distinct information of classes and 2) offer an externally settled memory as a class-guidance to reduce the ambiguity of representation in the test data of arbitrary unseen domain. To this end, we also propose memory divergence and feature cohesion losses, which encourage to learn memory reading and update processes for category-aware domain generalization. Extensive experiments for semantic segmentation demonstrate the superior generalization capability of our method over state-of-the-art works on various benchmarks.(1)

关键词： Deep learning computer vision Image analysis Shape Semantics Neural networks Benchmark testing

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Deep Blind Video Decaptioning by Temporal Aggregation and Recurrence 32

Deep Blind Video Decaptioning by Temporal Aggregation and Re...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kim, Dahun Woo, Sanghyun Lee, Joon-Young Kweon, In So Korea Adv Inst Sci & Technol Daejeon South Korea Adobe Res San Jose CA USA

ISBN: (纸本)9781728132938

Blind video decaptioning is a problem of automatically removing text overlays and inpainting the occluded parts in videos without any input masks. While recent deep learning based inpainting methods deal with a single image and mostly assume that the positions of the corrupted pixels are known, we aim at automatic text removal in video sequences without mask information. In this paper, we propose a simple yet effective framework for fast blind video decaptioning. We construct an encoder-decoder model, where the encoder takes multiple source frames that can provide visible pixels revealed from the scene dynamics. These hints are aggregated and fed into the decoder. We apply a residual connection from the input frame to the decoder output to enforce our network to focus on the corrupted regions only. Our proposed model was ranked in the first place in the ECCV Chalearn 2018 LAP Inpainting Competition Track2: Video decaptioning. In addition, we further improve this strong model by applying a recurrent feedback. The recurrent feedback not only enforces temporal coherence but also provides strong clues on where the corrupted pixels are. Both qualitative and quantitative experiments demonstrate that our full model produces accurate and temporally consistent video results in real time (50+fps).

关键词： Low-level vision Video Analytics vision Applications and Systems

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OBJECTFOLDER 2.0: A Multisensory Object Dataset for Sim2Real Transfer

OBJECTFOLDER 2.0: A Multisensory Object Dataset for Sim2Real...

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

作者： Gao, Ruohan Si, Zilin Chang, Yen-Yu Clarke, Samuel Bohg, Jeannette Li Fei-Fei Yuan, Wenzhen Wu, Jiajun Stanford Univ Stanford CA 94305 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Objects play a crucial role in our everyday activities. Though multisensory object-centric learning has shown great potential lately, the modeling of objects in prior work is rather unrealistic. OBJECTFOLDER 1.0 is a recent dataset that introduces 100 virtualized objects with visual, acoustic, and tactile sensory data. However;the dataset is small in scale and the multisensory data is of limited quality, hampering generalization to real-world scenarios. We present OBJECTFOLDER 2.0, a large-scale, multisensory dataset of common household objects in the form of implicit neural representations that significantly enhances OBJECTFOLDER 1.0 in three aspects. First, our dataset is 10 times larger in the amount of objects and orders of magnitude faster in rendering time. Second, we significantly improve the multisensory rendering quality for all three modalities. Third, we show that models learned from virtual objects in our dataset successfully transfer to their real-world counterparts in three challenging tasks: object scale estimation, contact localization, and shape reconstruction. OBJECTFOLDER 2.0 offers a new path and testbed for multisensory learning in computer vision and robotics. The dataset is available at https: //***/rhgao/ObjectFolder.

关键词： Location awareness computer vision Visualization Shape Computational modeling Estimation Rendering (computer graphics)

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Robust Multi-Resolution Pedestrian Detection in Traffic Scenes

Robust Multi-Resolution Pedestrian Detection in Traffic Scen...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Yan, Junjie Zhang, Xucong Lei, Zhen Liao, Shengcai Li, Stan Z. Chinese Acad Sci Ctr Biometr & Secur Res Beijing 100864 Peoples R China Ctr Biometr & Secur Res Natl Lab Pattern Recognition Beijing Peoples R China

ISBN: (纸本)9780769549897

The serious performance decline with decreasing resolution is the major bottleneck for current pedestrian detection techniques [14, 23]. In this paper, we take pedestrian detection in different resolutions as different but related problems, and propose a Multi-Task model to jointly consider their commonness and differences. The model contains resolution aware transformations to map pedestrians in different resolutions to a common space, where a shared detector is constructed to distinguish pedestrians from background. For model learning, we present a coordinate descent procedure to learn the resolution aware transformations and deformable part model (DPM) based detector iteratively. In traffic scenes, there are many false positives located around vehicles, therefore, we further build a context model to suppress them according to the pedestrian-vehicle relationship. The context model can be learned automatically even when the vehicle annotations are not available. Our method reduces the mean miss rate to 60% for pedestrians taller than 30 pixels on the Caltech Pedestrian Benchmark, which noticeably outperforms previous state-of-the-art (71%).

关键词： image resolution object detection pedestrians traffic engineering computing

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Learning Representations by Predicting Bags of Visual Words

Learning Representations by Predicting Bags of Visual Words

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

作者： Gidaris, Spyros Bursuc, Andrei Komodakis, Nikos Perez, Patrick Cord, Matthieu Valeo ai Paris France Univ Crete Iraklion Greece

ISBN: (纸本)9781728171685

Self-supervised representation learning targets to learn convnet-based image representations from unlabeled data. Inspired by the success of NLP methods in this area, in this work we propose a self-supervised approach based on spatially dense image descriptions that encode discrete visual concepts, here called visual words. To build such discrete representations, we quantize the feature maps of a first pretrained self-supervised convnet, over a k-means based vocabulary. Then, as a self-supervised task, we train another convnet to predict the histogram of visual words of an image (i.e., its Bag-of-Words representation) given as input a perturbed version of that image. The proposed task forces the convnet to learn perturbation-invariant and context-aware image features, useful for downstream image understanding tasks. We extensively evaluate our method and demonstrate very strong empirical results, e.g., our pre-trained self-supervised representations transfer better on detection task and similarly on classification over classes "unseen" during pre-training, when compared to the supervised case. This also shows that the process of image discretization into visual words can provide the basis for very powerful self-supervised approaches in the image domain, thus allowing further connections to be made to related methods from the NLP domain that have been extremely successful so far.(1)

关键词： computer vision

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Towards robust pedestrian detection in crowded image sequences

Towards robust pedestrian detection in crowded image sequenc...

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ieee conference on computer vision and pattern recognition

作者： Seemann, Edgar Fritz, Mario Schiele, Bernt Tech Univ Darmstadt Darmstadt Germany

ISBN: (纸本)9781424411795

Object class detection in scenes of realistic complexity remains a challenging task in computer vision. Most recent approaches focus on a single and general model for object class detection. However, in particular in the context of image sequences, it may be advantageous to adapt the general model to a more object-instance specific model in order to detect this particular object reliably within the image sequence. In this work we present a generative object model that is capable to scale from a general object class model to a more specific object-instance model. This allows to detect class instances as well as to distinguish between individual object instances reliably. We experimentally evaluate the performance of the proposed system on both still images and image sequences.

关键词： Image recognition

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Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective

Towards Robust and Adaptive Motion Forecasting: A Causal Rep...

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

作者： Liu, Yuejiang Cadei, Riccardo Schweizer, Jonas Bahmani, Sherwin Alahi, Alexandre Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Learning behavioral patterns from observational data has been a de facto approach to motion forecasting. Yet, the current paradigm suffers from two shortcomings: brittle under distribution shifts and inefficient for knowledge transfer. In this work, we propose to address these challenges from a causal representation perspective. We first introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with three groups of latent variables, namely invariant variables, style confounders, and spurious features. We then introduce a learning framework that treats each group separately: (i) unlike the common practice mixing datasets collected from different locations, we exploit their subtle distinctions by means of an invariance loss encouraging the model to suppress spurious correlations;(ii) we devise a modular architecture that factorizes the representations of invariant mechanisms and style confounders to approximate a sparse causal graph;(iii) we introduce a style contrastive loss that not only enforces the structure of style representations but also serves as a self-supervisory signal for test-time refinement on the fly. Experiments on synthetic and real datasets show that our proposed method improves the robustness and reusability of learned motion representations, significantly outperforming prior state-of-the-art motion forecasting models for out-of-distribution generalization and low-shot transfer.

关键词： Representation learning Learning systems Correlation Dynamics computer architecture Predictive models Robustness

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