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ER3: A Unified Framework for Event Retrieval, recognition and Recounting 30

ER3: A Unified Framework for Event Retrieval, Recognition an...

引用

30th ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Gao, Zhanning Hua, Gang Zhang, Dongqing Jojic, Nebojsa Wang, Le Xue, Jianru Zheng, Nanning Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Shaanxi Peoples R China Microsoft Res Redmond WA USA

ISBN: (纸本)9781538604571

We develop a unified framework for complex event retrieval, recognition and recounting. The framework is based on a compact video representation that exploits the temporal correlations in image features. Our feature alignment procedure identifies and removes the feature redundancies across frames and outputs an intermediate tensor representation we call video imprint. The video imprint is then fed into a reasoning network, whose attention mechanism parallels that of memory networks used in language modeling. The reasoning network simultaneously recognizes the event category and locates the key pieces of evidence for event recounting. In event retrieval tasks, we show that the compact video representation aggregated from the video imprint achieves significantly better retrieval accuracy compared with existing methods. We also set new state of the art results in event recognition tasks with an additional benefit: The latent structure in our reasoning network highlights the areas of the video imprint and can be directly used for event recounting. As video imprint maps back to locations in the video frames, the network allows not only the identification of key frames but also specific areas inside each frame which are most influential to the decision process.

关键词： Feature extraction Cognition pattern recognition Neural networks Layout computer vision

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End-to-End Deep Kronecker-Product Matching for Person Re-identification 31

End-to-End Deep Kronecker-Product Matching for Person Re-ide...

引用

31st ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Shen, Yantao Xiao, Tong Li, Hongsheng Yi, Shuai Wang, Xiaogang Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9781538664209

Person re-identification aims to robustly measure similarities between person images. The significant variation of person poses and viewing angles challenges for accurate person re-identification. The spatial layout and correspondences between query person images are vital information for tackling this problem but are ignored by most state-of-the-art methods. In this paper, we propose a novel Kronecker Product Matching module to match feature maps of different persons in an end-to-end trainable deep neural network. A novel feature soft warping scheme is designed for aligning the feature maps based on matching results, which is shown to be crucial for achieving superior accuracy. The multi-scale features based on hourglass-like networks and self residual attention are also exploited to further boost the re-identification performance. The proposed approach outperforms state-of-the-art methods on the Market-1501, CUHK03, and DukeMTMC datasets, which demonstrates the effectiveness and generalization ability of our proposed approach.

关键词： computer vision Estimation Neural networks Measurement Layout Semantics

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Deep Deterministic Uncertainty: A New Simple Baseline

Deep Deterministic Uncertainty: A New Simple Baseline

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

作者： Mukhoti, Jishnu Kirsch, Andreas van Amersfoort, Joost Torr, Philip H. S. Gal, Yarin Univ Oxford Dept Comp Sci OATML Oxford England Univ Oxford Dept Engn Sci Torr Vis Grp Oxford England

ISBN: (纸本)9798350301298

Reliable uncertainty from deterministic single-forward pass models is sought after because conventional methods of uncertainty quantification are computationally expensive. We take two complex single-forward-pass uncertainty approaches, DUQ and SNGP, and examine whether they mainly rely on a well-regularized feature space. Crucially, without using their more complex methods for estimating uncertainty, we find that a single softmax neural net with such a regularized feature-space, achieved via residual connections and spectral normalization, outperforms DUQ and SNGP's epistemic uncertainty predictions using simple Gaussian Discriminant Analysis post-training as a separate feature-space density estimator-without fine-tuning on OoD data, feature ensembling, or input pre-procressing. Our conceptually simple Deep Deterministic Uncertainty (DDU) baseline can also be used to disentangle aleatoric and epistemic uncertainty and performs as well as Deep Ensembles, the stateof-the art for uncertainty prediction, on several OoD benchmarks (CIFAR-10/100 vs SVHN/Tiny-ImageNet, ImageNet vs ImageNet-O), active learning settings across different model architectures, as well as in large scale vision tasks like semantic segmentation, while being computationally cheaper.

关键词： accountability ethics in vision fairness privacy Transparency

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Volumetric Environment Representation for vision-Language Navigation

Volumetric Environment Representation for Vision-Language Na...

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

作者： Liu, Rui Wang, Wenguan Yan, Yi Zhejiang Univ CCAI ReLER Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798350353006

vision-language navigation (VLN) requires an agent to navigate through an 3D environment based on visual observations and natural language instructions. It is clear that the pivotal factor for successful navigation lies in the comprehensive scene understanding. Previous VLN agents employ monocular frameworks to extract 2D features of perspective views directly. Though straightforward, they struggle for capturing 3D geometry and semantics, leading to a partial and incomplete environment representation. To achieve a comprehensive 3D representation with fine-grained details, we introduce a Volumetric Environment Representation (VER), which voxelizes the physical world into structured 3D cells. For each cell, VER aggregates multi-view 2D features into such a unified 3D space via 2D-3D sampling. Through coarse-to-fine feature extraction and multi-task learning for VER, our agent predicts 3D occupancy, 3D room layout, and 3D bounding boxes jointly. Based on online collected VERs, our agent performs volume state estimation and builds episodic memory for predicting the next step. Experimental results show our environment representations from multi-task learning lead to evident performance gains on VLN. Our model achieves state-of-the-art performance across VLN benchmarks (R2R, REVERIE, and R4R).

关键词： Semantics

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Live Demonstration: PINK: Polarity-based Anti-flicker for Event Cameras

Live Demonstration: PINK: Polarity-based Anti-flicker for Ev...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Im, Gyubeom Park, Keunjoo Kim, Junseok Son, Bongki Shin, Seungchul Lee, Haechang Samsung Electronics 1-1 Samsungjeonja-ro Hwaseong-Si Korea Republic of

ISBN: (纸本)9798350302493

Dynamic vision Sensor (DVS) is an event sensor that asynchronously captures an event whenever there is a brightness change in the scene. However, due to the event sensor's high temporal resolution property, it is particularly vulnerable to flicker. Numerous unwanted events caused by flickering can cause serious loads on computing power and prevent the event sensor from extracting useful information, making it difficult to use the event sensor for machine vision applications in practice. In this demo, we propose the PINK (PolarIty-based aNti-flicKer), which filters flicker based on the event polarity (on/off). The proposed method is lightweight and efficient and can be used in real time. To the best of our knowledge, this is the first anti-flicker algorithm that works in real time and provides a usable opticalflow result for machine vision applications in an extreme flicker environment. A video showing the example of live demo is available online at https://***/5UdU0PLZaf8. © 2023 ieee.

关键词： computer vision

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BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation

BBAM: Bounding Box Attribution Map for Weakly Supervised Sem...

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

作者： Lee, Jungbeom Yi, Jihun Shin, Chaehun Yoon, Sungroh Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea Seoul Natl Univ ISRC INMC ASRI Seoul South Korea Seoul Natl Univ Inst Engn Res Seoul South Korea

ISBN: (纸本)9781665445092

Weakly supervised segmentation methods using bounding box annotations focus on obtaining a pixel-level mask from each box containing an object. Existing methods typically depend on a class-agnostic mask generator, which operates on the low-level information intrinsic to an image. In this work, we utilize higher-level information from the behavior of a trained object detector, by seeking the smallest areas of the image from which the object detector produces almost the same result as it does from the whole image. These areas constitute a bounding-box attribution map (BBAM), which identifies the target object in its bounding box and thus serves as pseudo ground-truth for weakly supervised semantic and instance segmentation. This approach significantly outperforms recent comparable techniques on both the PASCAL VOC and MS COCO benchmarks in weakly supervised semantic and instance segmentation. In addition, we provide a detailed analysis of our method, offering deeper insight into the behavior of the BBAM.

关键词： Image segmentation computer vision Annotations Semantics Detectors Benchmark testing Generators

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Exclusivity-Consistency Regularized Multi-view Subspace Clustering 30

Exclusivity-Consistency Regularized Multi-view Subspace Clus...

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30th ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wang, Xiaobo Guo, Xiaojie Lei, Zhen Zhang, Changqing Li, Stan Z. Chinese Acad Sci Ctr Biometr & Secur Res Beijing Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci IIE State Key Lab Informat Secur Beijing Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China

ISBN: (纸本)9781538604571

Multi-view subspace clustering aims to partition a set of multi-source data into their underlying groups. To boost the performance of multi-view clustering, numerous subspace learning algorithms have been developed in recent years, but with rare exploitation of the representation complementarity between different views as well as the indicator consistency among the representations, let alone considering them simultaneously. In this paper, we propose a novel multi-view subspace clustering model that attempts to harness the complementary information between different representations by introducing a novel position-aware exclusivity term. Meanwhile, a consistency term is employed to make these complementary representations to further have a common indicator. We formulate the above concerns into a unified optimization framework. Experimental results on several benchmark datasets are conducted to reveal the effectiveness of our algorithm over other state-of-the-arts.

关键词： Clustering algorithms Optimization pattern recognition computer vision Benchmark testing Standards conferences

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MSCap: Multi-Style Image Captioning with Unpaired Stylized Text 32

MSCap: Multi-Style Image Captioning with Unpaired Stylized T...

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Guo, Longteng Liu, Jing Yao, Peng Li, Jiangwei Lu, Hanqing Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Sci & Technol Beijing Beijing Peoples R China Huawei Devices Multimedia Dept Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781728132938

In this paper, we propose an adversarial learning network for the task of multi-style image captioning (MSCap) with a standard factual image caption dataset and a multi-stylized language corpus without paired images. How to learn a single model for multi-stylized image captioning with unpaired data is a challenging and necessary task, whereas rarely studied in previous works. The proposed framework mainly includes four contributive modules following a typical image encoder. First, a style dependent caption generator to output a sentence conditioned on an encoded image and a specified style. Second, a caption discriminator is presented to distinguish the input sentence to be real or not. The discriminator and the generator are trained in an adversarial manner to enable more natural and human-like captions. Third, a style classifier is employed to discriminate the specific style of the input sentence. Besides, a back-translation module is designed to enforce the generated stylized captions are visually grounded with the intuition of the cycle consistency for factual caption and stylized caption. We enable an end-to-end optimization of the whole model with differentiable sofiinax *** last, we conduct comprehensive experiments using a combined dataset containing four caption styles to demonstrate the outstanding performance of our proposed method.

关键词： vision + Language

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Anomaly Detection in Video via Self-Supervised and Multi-Task Learning

Anomaly Detection in Video via Self-Supervised and Multi-Tas...

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

作者： Georgescu, Mariana-Iuliana Barbalau, Antonio Ionescu, Radu Tudor Khan, Fahad Shahbaz Popescu, Marius Shah, Mubarak Univ Bucharest Bucharest Romania MBZ Univ Artificial Intelligence Abu Dhabi U Arab Emirates SecurifAI Bucharest Romania Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)9781665445092

Anomaly detection in video is a challenging computer vision problem. Due to the lack of anomalous events at training time, anomaly detection requires the design of learning methods without full supervision. In this paper, we approach anomalous event detection in video through self-supervised and multi-task learning at the object level. We first utilize a pre-trained detector to detect objects. Then, we train a 3D convolutional neural network to produce discriminative anomaly-specific information by jointly learning multiple proxy tasks: three self-supervised and one based on knowledge distillation. The self-supervised tasks are: (i) discrimination of forward/backward moving objects (arrow of time), (ii) discrimination of objects in consecutive/intermittent frames (motion irregularity) and (iii) reconstruction of object-specific appearance information. The knowledge distillation task takes into account both classification and detection information, generating large prediction discrepancies between teacher and student models when anomalies occur. To the best of our knowledge, we are the first to approach anomalous event detection in video as a multi-task learning problem, integrating multiple self-supervised and knowledge distillation proxy tasks in a single architecture. Our lightweight architecture outperforms the state-of-the-art methods on three benchmarks: Avenue, ShanghaiTech and UCSD Ped2. Additionally, we perform an ablation study demonstrating the importance of integrating self-supervised learning and normality-specific distillation in a multi-task learning setting.

关键词： Training computer vision Three-dimensional displays Event detection computer architecture Benchmark testing Predictive models

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Augmentation Strategies for Learning with Noisy Labels

Augmentation Strategies for Learning with Noisy Labels

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

作者： Nishi, Kento Ding, Yi Rich, Alex Hollerer, Tobias Lynbrook High Sch San Jose CA 95129 USA Univ Calif Santa Barbara Santa Barbara CA 93106 USA

ISBN: (纸本)9781665445092

Imperfect labels are ubiquitous in real-world datasets. Several recent successful methods for training deep neural networks (DNNs) robust to label noise have used two primary techniques: filtering samples based on loss during a warm-up phase to curate an initial set of cleanly labeled samples, and using the output of a network as a pseudo-label for subsequent loss calculations. In this paper, we evaluate different augmentation strategies for algorithms tackling the "learning with noisy labels" problem. We propose and examine multiple augmentation strategies and evaluate them using synthetic datasets based on CIFAR-10 and CIFAR-100, as well as on the real-world dataset ClothinglM. Due to several commonalities in these algorithms, we find that using one set of augmentations for loss modeling tasks and another set for learning is the most effective, improving results on the state-of-the-art and other previous methods. Furthermore, we find that applying augmentation during the warm-up period can negatively impact the loss convergence behavior of correctly versus incorrectly labeled samples. We introduce this augmentation strategy to the state-of-the-art technique and demonstrate that we can improve performance across all evaluated noise levels. In particular, we improve accuracy on the CIFAR-10 benchmark at 90% symmetric noise by more than 15% in absolute accuracy, and we also improve performance on the Clothing1M dataset.

关键词： Training Deep learning Machine learning algorithms Filtering Neural networks pattern recognition Noise measurement

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