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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Action Shuffle Alternating Learning for Unsupervised Action Segmentation

Action Shuffle Alternating Learning for Unsupervised Action ...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Jun Todorovic, Sinisa Oregon State Univ Corvallis OR 97331 USA

ISBN: (纸本)9781665445092

This paper addresses unsupervised action segmentation. Prior work captures the frame-level temporal structure of videos by a feature embedding that encodes time locations of frames in the video. We advance prior work with a new self-supervised learning (SSL) of a feature embedding that accounts for both frame- and action-level structure of videos. Our SSL trains an RNN to recognize positive and negative action sequences, and the RNN's hidden layer is taken as our new action-level feature embedding. The positive and negative sequences consist of action segments sampled from videos, where in the former the sampled action segments respect their time ordering in the video, and in the latter they are shuffled. As supervision of actions is not available and our SSL requires access to action segments, we specify an HMM that explicitly models action lengths, and infer a MAP action segmentation with the Viterbi algorithm. The resulting action segmentation is used as pseudo-ground truth for estimating our action-level feature embedding and updating the HMM. We alternate the above steps within the Generalized EM framework, which ensures convergence. Our evaluation on the Breakfast, YouTube Instructions, and 50Salads datasets gives superior results to those of the state of the art.

关键词： Training computer vision Viterbi algorithm Computational modeling Hidden Markov models pattern recognition Videos

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FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation

FixBi: Bridging Domain Spaces for Unsupervised Domain Adapta...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Na, Jaemin Jung, Heechul Chang, Hyung Jin Hwang, Wonjun Ajou Univ Suwon South Korea Kyungpook Natl Univ Seoul South Korea Univ Birmingham Birmingham W Midlands England

ISBN: (纸本)9781665445092

Unsupervised domain adaptation (UDA) methods for learning domain invariant representations have achieved remarkable progress. However, most of the studies were based on direct adaptation from the source domain to the target domain and have suffered from large domain discrepancies. In this paper, we propose a UDA method that effectively handles such large domain discrepancies. We introduce a fixed ratio-based mixup to augment multiple intermediate domains between the source and target domain. From the augmented-domains, we train the source-dominant model and the target-dominant model that have complementary characteristics. Using our confidence-based learning methodologies, e.g., bidirectional matching with high-confidence predictions and self-penalization using low-confidence predictions, the models can learn from each other or from its own results. Through our proposed methods, the models gradually transfer domain knowledge from the source to the target domain. Extensive experiments demonstrate the superiority of our proposed method on three public benchmarks: Office-31, Office-Home, and VisDA-2017.(1)

关键词： computer vision Adaptation models Benchmark testing Predictive models pattern recognition Standards

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General Multi-label Image Classification with Transformers

General Multi-label Image Classification with Transformers

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Lanchantin, Jack Wang, Tianlu Ordonez, Vicente Qi, Yanjun Univ Virginia Charlottesville VA 22903 USA

ISBN: (纸本)9781665445092

Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image. In this work we propose the Classification Transformer (C-Tran), a general framework for multi-label image classification that leverages Transformers to exploit the complex dependencies among visual features and labels. Our approach consists of a Transformer encoder trained to predict a set of target labels given an input set of masked labels, and visual features from a convolutional neural network. A key ingredient of our method is a label mask training objective that uses a ternary encoding scheme to represent the state of the labels as positive, negative, or unknown during training. Our model shows state-of-the-art performance on challenging datasets such as COCO and Visual Genome. Moreover, because our model explicitly represents the label state during training, it is more general by allowing us to produce improved results for images with partial or extra label annotations during inference. We demonstrate this additional capability in the COCO, Visual Genome, News-500, and CUB image datasets.

关键词： Training Deep learning Visualization Genomics Transformers Encoding pattern recognition

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Detecting Stable Keypoints from Events through Image Gradient Prediction

Detecting Stable Keypoints from Events through Image Gradien...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chiberre, Philippe Perot, Etienne Sironi, Amos Lepetit, Vincent PROPHESEE Paris France Univ Gustave Eiffel CNRS Ecole Ponts LIGM Marne La Vallee France

ISBN: (纸本)9781665448994

We present a method that detects stable keypoints from an event stream at high speed with a low memory footprint. Our key observation connects two points: It should be easier to reconstruct the image gradients rather than the image itself from the events, and the Harris corner detector, one of the most reliable keypoint detectors for short baseline regular images, depends on the image gradients, not the image. We therefore introduce a recurrent convolutional neural network to predict image gradients from events. As image gradients and events are correlated, this prediction task is relatively easy and we can keep this network very small. We train our network solely on synthetic data. Extracting Harris corners from these gradients is then very efficient. Moreover, in contrast to learned methods, we can change the hyperparameters of the detector without retraining. Our experiments confirm that predicting image gradients rather than images is much more efficient, and that our approach predicts stable corner points which are easier to track for a longer time compared to state-of-the-art event-based methods.

关键词： Image analysis Detectors computer architecture Streaming media Cameras pattern recognition Reliability

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Hierarchical Motion Understanding via Motion Programs

Hierarchical Motion Understanding via Motion Programs

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kulal, Sumith Mao, Jiayuan Aiken, Alex Wu, Jiajun Stanford Univ Stanford CA 94305 USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665445092

Current approaches to video analysis of human motion focus on raw pixels or keypoints as the basic units of reasoning. We posit that adding higher-level motion primitives, which can capture natural coarser units of motion such as "backswing" or "follow-through", can be used to improve downstream analysis tasks. This higher level of abstraction can also capture key features, such as loops of repeated primitives, that are currently inaccessible at lower levels of representation. We therefore introduce Motion Programs, a neuro-symbolic, program-like representation that expresses motions as a composition of high-level primitives. We also present a system for automatically inducing motion programs from videos of human motion and for leveraging motion programs in video synthesis. Experiments show that motion programs can accurately describe a diverse set of human motions and the inferred programs contain semantically meaningful motion primitives, such as arm swings and jumping jacks. Our representation also benefits downstream tasks such as video interpolation and video prediction and outperforms off-the-shelf models. We further demonstrate how these programs can detect diverse kinds of repetitive motion and facilitate interactive video editing.

关键词： Interpolation computer vision Computational modeling Predictive models Cognition pattern recognition Task analysis

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Stochastic Image-to-Video Synthesis using cINNs

Stochastic Image-to-Video Synthesis using cINNs

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Dorkenwald, Michael Milbich, Timo Blattmann, Andreas Rombach, Robin Derpanis, Konstantinos G. Ommer, Bjorn Heidelberg Univ IWR HCI Heidelberg Germany Ryerson Univ Dept Comp Sci Toronto ON Canada Vector Inst AI Toronto ON Canada Samsung AI Ctr Toronto Toronto ON Canada

ISBN: (纸本)9781665445092

Video understanding calls for a model to learn the characteristic interplay between static scene content and its dynamics: Given an image, the model must be able to predict a future progression of the portrayed scene and, conversely, a video should be explained in terms of its static image content and all the remaining characteristics not present in the initial frame. This naturally suggests a bijective mapping between the video domain and the static content as well as residual information. In contrast to common stochastic image-to-video synthesis, such a model does not merely generate arbitrary videos progressing the initial image. Given this image, it rather provides a one-to-one mapping between the residual vectors and the video with stochastic outcomes when sampling. The approach is naturally implemented using a conditional invertible neural network (cINN) that can explain videos by independently modelling static and other video characteristics, thus laying the basis for controlled video synthesis. Experiments on diverse video datasets demonstrate the effectiveness of our approach in terms of both the quality and diversity of the synthesized results. Our project page is available at https://***/3dg90fV.

关键词： computer vision Neural networks Stochastic processes Process control Predictive models Probabilistic logic pattern recognition

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POSEFusion: Pose-guided Selective Fusion for Single-view Human Volumetric Capture

POSEFusion: Pose-guided Selective Fusion for Single-view Hum...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Zhe Yu, Tao Zheng, Zerong Guo, Kaiwen Liu, Yebin Tsinghua Univ Dept Automat Beijing Peoples R China Google Zurich Switzerland

ISBN: (纸本)9781665445092

We propose POse-guided SElective Fusion (POSEFusion), a single-view human volumetric capture method that leverages tracking-based methods and tracking-free inference to achieve high-fidelity and dynamic 3D reconstruction. By contributing a novel reconstruction framework which contains pose-guided keyframe selection and robust implicit surface fusion, our method fully utilizes the advantages of both tracking-based methods and tracking-free inference methods, and finally enables the high-fidelity reconstruction of dynamic surface details even in the invisible regions. We formulate the keyframe selection as a dynamic programming problem to guarantee the temporal continuity of the reconstructed sequence. Moreover, the novel robust implicit surface fusion involves an adaptive blending weight to preserve high-fidelity surface details and an automatic collision handling method to deal with the potential self-collisions. Overall, our method enables high-fidelity and dynamic capture in both visible and invisible regions from a single RGBD camera, and the results and experiments show that our method outperforms state-of-the-art methods.

关键词： Surface reconstruction computer vision Three-dimensional displays Cameras pattern recognition Dynamic programming

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DualAST: Dual Style-Learning Networks for Artistic Style Transfer

DualAST: Dual Style-Learning Networks for Artistic Style Tra...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Haibo Zhao, Lei Wang, Zhizhong Zhang, Huiming Zuo, Zhiwen Li, Ailin Xing, Wei Lu, Dongming Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9781665445092

Artistic style transfer is an image editing task that aims at repainting everyday photographs with learned artistic styles. Existing methods learn styles from either a single style example or a collection of artworks. Accordingly, the stylization results are either inferior in visual quality or limited in style controllability. To tackle this problem, we propose a novel Dual Style-Learning Artistic Style Transfer (DualAST) framework to learn simultaneously both the holistic artist-style (from a collection of artworks) and the specific artwork-style (from a single style image): the artist-style sets the tone (i.e., the overall feeling) for the stylized image, while the artwork-style determines the details of the stylized image, such as color and texture. Moreover, we introduce a Style-Control Block (SCB) to adjust the styles of generated images with a set of learnable style-control factors. We conduct extensive experiments to evaluate the performance of the proposed framework, the results of which confirm the superiority of our method.

关键词： Visualization computer vision Image color analysis Controllability pattern recognition Task analysis

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Probabilistic Embeddings for Cross-Modal Retrieval

Probabilistic Embeddings for Cross-Modal Retrieval

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chun, Sanghyuk Oh, Seong Joon de Rezende, Rafael Sampaio Kalantidis, Yannis Larlus, Diane NAVER AI Lab Seongnam South Korea NAVER Labs Europe Meylan France

ISBN: (纸本)9781665445092

Cross-modal retrieval methods build a common representation space for samples from multiple modalities, typically from the vision and the language domains. For images and their captions, the multiplicity of the correspondences makes the task particularly challenging. Given an image (respectively a caption), there are multiple captions (respectively images) that equally make sense. In this paper, we argue that deterministic functions are not sufficiently powerful to capture such one-to-many correspondences. Instead, we propose to use Probabilistic Cross-Modal Embedding (PCME), where samples from the different modalities are represented as probabilistic distributions in the common embedding space. Since common benchmarks such as COCO suffer from non-exhaustive annotations for cross-modal matches, we propose to additionally evaluate retrieval on the CUB dataset, a smaller yet clean database where all possible image-caption pairs are annotated. We extensively ablate PCME and demonstrate that it not only improves the retrieval performance over its deterministic counterpart but also provides uncertainty estimates that render the embeddings more interpretable.

关键词： computer vision Uncertainty Codes Databases Annotations Tools Benchmark testing

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Style-Aware Normalized Loss for Improving Arbitrary Style Transfer

Style-Aware Normalized Loss for Improving Arbitrary Style Tr...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Cheng, Jiaxin Jaiswal, Ayush Wu, Yue Natarajan, Pradeep Natarajan, Prem USC Informat Sci Inst Arlington VA 22203 USA Amazon Alexa Nat Understanding Arlington VA USA Amazon Seattle WA USA

ISBN: (纸本)9781665445092

Neural Style Transfer (NST) has quickly evolved from single-style to infinite-style models, also known as Arbitrary Style Transfer (AST). Although appealing results have been widely reported in literature, our empirical studies on four well-known AST approaches (GoogleMagenta [14], AdaIN [19], LinearTransfer [29], and SANet [37]) show that more than 50% of the time, AST stylized images are not acceptable to human users, typically due to under- or over-stylization. We systematically study the cause of this imbalanced style transferability (IST) and propose a simple yet effective solution to mitigate this issue. Our studies show that the IST issue is related to the conventional AST style loss, and reveal that the root cause is the equal weightage of training samples irrespective of the properties of their corresponding style images, which biases the model towards certain styles. Through investigation of the theoretical bounds of the AST style loss, we propose a new loss that largely overcomes IST. Theoretical analysis and experimental results validate the effectiveness of our loss, with over 80% relative improvement in style deception rate and 98% relatively higher preference in human evaluation.

关键词： Training computer vision pattern recognition

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