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ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal Action Localization

ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised...

引用

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

作者： He, Bo Yang, Xitong Kang, Le Cheng, Zhiyu Zhou, Xin Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA Baidu Res Sunnyvale CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Weakly-supervised temporal action localization aims to recognize and localize action segments in untrimmed videos given only video-level action labels for training. Without the boundary information of action segments, existing methods mostly rely on multiple instance learning (MIL), where the predictions of unlabeled instances (i.e., video snippets) are supervised by classifying labeled bags (i.e., untrimmed videos). However, this formulation typically treats snippets in a video as independent instances, ignoring the underlying temporal structures within and across action segments. To address this problem, we propose ASM-Loc, a novel WTAL framework that enables explicit, action-aware segment modeling beyond standard MIL-based methods. Our framework entails three segment-centric components: (i) dynamic segment sampling for compensating the contribution of short actions;(ii) infra- and inter-segment attention for modeling action dynamics and capturing temporal dependencies;(iii) pseudo instance-level supervision for improving action boundary prediction. Furthermore, a multistep refinement strategy is proposed to progressively improve action proposals along the model training process. Extensive experiments on THUMOS-14 and ActivityNet-v1.3 demonstrate the effectiveness of our approach, establishing new state of the art on both datasets.

关键词： Training Location awareness computer vision Computational modeling Pipelines Predictive models pattern recognition

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InOut : Diverse Image Outpainting via GAN Inversion

InOut : Diverse Image Outpainting via GAN Inversion

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

作者： Cheng, Yen-Chi Lin, Chieh Hubert Lee, Hsin-Ying Ren, Jian Tulyakov, Sergey Yang, Ming-Hsuan Carnegie Mellon Univ Pittsburgh PA 15213 USA UC Merced Merced CA USA Snap Inc Santa Monica CA USA Yonsei Univ Seoul South Korea Google Res Mountain View CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image outpainting seeks for a semantically consistent extension of the input image beyond its available content. Compared to inpainting - filling in missing pixels in a way coherent with the neighboring pixels - outpainting can be achieved in more diverse ways since the problem is less constrained by the surrounding pixels. Existing image outpainting methods pose the problem as a conditional image-to-image translation task, often generating repetitive structures and textures by replicating the content available in the input image. In this work, we formulate the problem from the perspective of inverting generative adversarial networks. Our generator renders micro-patches conditioned on their joint latent code as well as their individual positions in the image. To outpaint an image, we seek for multiple latent codes not only recovering available patches but also synthesizing diverse outpainting by patch-based generation. This leads to richer structure and content in the outpainted regions. Furthermore, our formulation allows for outpainting conditioned on the categorical input, thereby enabling flexible user controls. Extensive experimental results demonstrate the proposed method performs favorably against existing in- and outpainting methods, featuring higher visual quality and diversity.

关键词： Representation learning Visualization computer vision Codes Generative adversarial networks Generators Filling

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Mask3D: Pre-training 2D vision Transformers by Learning Masked 3D Priors

Mask3D: Pre-training 2D Vision Transformers by Learning Mask...

引用

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

作者： Hou, Ji Dai, Xiaoliang He, Zijian Dai, Angela Niessner, Matthias Meta Real Labs Menlo Pk CA 94025 USA Tech Univ Munich Munich Germany

ISBN: (纸本)9798350301298

Current popular backbones in computer vision, such as vision Transformers (ViT) and ResNets are trained to perceive the world from 2D images. However, to more effectively understand 3D structural priors in 2D backbones, we propose Mask3D to leverage existing large-scale RGB-D data in a self-supervised pre-training to embed these 3D priors into 2D learned feature representations. In contrast to traditional 3D contrastive learning paradigms requiring 3D reconstructions or multi-view correspondences, our approach is simple: we formulate a pre-text reconstruction task by masking RGB and depth patches in individual RGB-D frames. We demonstrate the Mask3D is particularly effective in embedding 3D priors into the powerful 2D ViT backbone, enabling improved representation learning for various scene understanding tasks, such as semantic segmentation, instance segmentation and object detection. Experiments show that Mask3D notably outperforms existing self-supervised 3D pre-training approaches on ScanNet, NYUv2, and Cityscapes image understanding tasks, with an improvement of +6.5% mIoU against the state-of-the-art Pri3D on ScanNet image semantic segmentation.

关键词： 3D from single images

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MLP-3D: A MLP-like 3D Architecture with Grouped Time Mixing

MLP-3D: A MLP-like 3D Architecture with Grouped Time Mixing

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

作者： Qiu, Zhaofan Yao, Ting Ngo, Chong-Wah Mei, Tao JD Explore Acad Beijing Peoples R China Singapore Management Univ Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Convolutional Neural Networks (CNNs) have been regarded as the go-to models for visual recognition. More recently, convolution-free networks, based on multi-head self-attention (MSA) or multi-layer perceptrons (MLPs), become more and more popular. Nevertheless, it is not trivial when utilizing these newly-minted networks for video recognition due to the large variations and complexities in video data. In this paper, we present MLP-3D networks, a novel MLP-like 3D architecture for video recognition. Specifically, the architecture consists of MLP-3D blocks, where each block contains one MLP applied across tokens (i.e., token-mixing MLP) and one MLP applied independently to each token (i.e., channel MLP). By deriving the novel grouped time mixing (GTM) operations, we equip the basic token-mixing MLP with the ability of temporal modeling. GTM divides the input tokens into several temporal groups and linearly maps the tokens in each group with the shared projection matrix. Furthermore, we devise several variants of GTM with different grouping strategies, and compose each variant in different blocks of MLP-3D network by greedy architecture search. Without the dependence on convolutions or attention mechanisms, our MLP-3D networks achieves 68.5%/81.4% top-1 accuracy on Something-Something V2 and Kinetics-400 datasets, respectively. Despite with fewer computations, the results are comparable to state-of-the-art widely-used 3D CNNs and video transformers.

关键词： Visualization computer vision Three-dimensional displays computer architecture Transformers pattern recognition Complexity theory

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Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective

Deep Representation Learning on Long-tailed Data: A Learnabl...

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

作者： Liu, Jialun Sun, Yifan Han, Chuchu Dou, Zhaopeng Li, Wenhui Jilin Univ Jilin Jilin Peoples R China Megvii Inc Beijing Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781728171685

This paper considers learning deep features from long-tailed data. We observe that in the deep feature space, the head classes and the tail classes present different distribution patterns. The head classes have a relatively large spatial span, while the tail classes have a significantly small spatial span, due to the lack of intra-class diversity. This uneven distribution between head and tail classes distorts the overall feature space, which compromises the discriminative ability of the learned features. In response, we seek to expand the distribution of the tail classes during training, so as to alleviate the distortion of the feature space. To this end, we propose to augment each instance of the tail classes with certain disturbances in the deep feature space. With the augmentation, a specified feature vector becomes a set of probable features scattered around itself which is analogical to an atomic nucleus surrounded by the electron cloud. Intuitively, we name it as "feature cloud". The intra-class distribution of the feature cloud is learned from the head classes, and thus provides higher intra-class variation to the tail classes. Consequentially, it alleviates the distortion of the learned feature space, and improves deep representation learning on long tailed data. Extensive experimental evaluations on person re-identification and face recognition tasks confirm the effectiveness of our method.

关键词： Face recognition

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Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video 32

Object-centric Auto-encoders and Dummy Anomalies for Abnorma...

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

作者： Ionescu, Radu Tudor Khan, Fahad Shahbaz Georgescu, Mariana-Iuliana Shao, Ling Incept Inst Artificial Intelligence IIAI Abu Dhabi U Arab Emirates Univ Bucharest 14 Acad Bucharest Romania SecurifAI 21 Mircea Voda Bucharest Romania

ISBN: (纸本)9781728132938

Abnormal event detection in video is a challenging vision problem. Most existing approachesformulate abnormal event detection as an outlier detection task, due to the scarcity of anomalous data during training. Because of the lack of prior information regardingabnormal events, these methods are not fully-equipped to differentiate between normal and abnormal events. In this work, we formalize abnormal event detection as a one-versus-rest binary classification problem. Our contribution is two-fold. First,we introducean unsupervisedfeature learningframework based on object-centric convolutional auto-encoders to encode both motion and appearanceinformation. Second, we propose a supervisedclassificationapproach based on clusteringthe trainingsamples into normality clusters. A one-versus-rest abnormal event classifier is then employed to separate each normality cluster from the rest. For the purpose of training the classifier, the other clusters act as dummy anomalies. During inference, an object is labeled as abnormal if the highest classification score assigned by the one-versus-rest classifiers is negative. Comprehensive experiments are performed on four benchmarks: Avenue, ShanghaiTech, UCSD and UMN. Our approach provides superior results on allfour data sets. On the large-scale ShanghaiTech data set, our method provides an absolute gain of 8.4% in terms offrame-level AUC compared to the state-of-the-artmethod [].

关键词： Action recognition Deep Learning Motion and Tracking Video Analytics vision Applications and Systems

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Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation

Multi-Source Domain Adaptation with Collaborative Learning f...

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

作者： He, Jianzhong Jia, Xu Chen, Shuaijun Liu, Jianzhuang Huawei Cloud Data Storage & Intelligent Vis Tech Res Dept Shenzhen Peoples R China Huawei Technol Noahs Ark Lab Shenzhen Peoples R China Dalian Univ Technol Dalian Peoples R China

ISBN: (纸本)9781665445092

Multi-source unsupervised domain adaptation (MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain. In this paper, we propose a novel multi-source domain adaptation framework based on collaborative learning for semantic segmentation. Firstly, a simple image translation method is introduced to align the pixel value distribution to reduce the gap between source domains and target domain to some extent. Then, to fully exploit the essential semantic information across source domains, we propose a collaborative learning method for domain adaptation without seeing any data from target domain. In addition, similar to the setting of unsupervised domain adaptation, unlabeled target domain data is leveraged to further improve the performance of domain adaptation. This is achieved by additionally constraining the outputs of multiple adaptation models with pseudo labels online generated by an ensembled model. Extensive experiments and ablation studies are conducted on the widely-used domain adaptation benchmark datasets in semantic segmentation. Our proposed method achieves 59.0% mIoU on the validation set of Cityscapes by training on the labeled Synscapes and GTA5 datasets and unlabeled training set of Cityscapes. It significantly outperforms all previous state-of-the-arts single-source and multi-source unsupervised domain adaptation methods.

关键词： Training Image segmentation Adaptation models computer vision Semantics Benchmark testing Collaborative work

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Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach 30

Query-Focused Video Summarization: Dataset, Evaluation, and ...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Sharghi, Aidean Laurel, Jacob S. Gong, Boqing Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA Univ Alabama Birmingham Dept Comp Sci Birmingham AL 35294 USA

ISBN: (纸本)9781538604571

Recent years have witnessed a resurgence of interest in video summarization. However, one of the main obstacles to the research on video summarization is the user subjectivity - users have various preferences over the summaries. The subjectiveness causes at least two problems. First, no single video summarizer fits all users unless it interacts with and adapts to the individual users. Second, it is very challenging to evaluate the performance of a video summarizer. To tackle the first problem, we explore the recently proposed query-focused video summarization which introduces user preferences in the form of text queries about the video into the summarization process. We propose a memory network parameterized sequential determinantal point process in order to attend the user query onto different video frames and shots. To address the second challenge, we contend that a good evaluation metric for video summarization should focus on the semantic information that humans can perceive rather than the visual features or temporal overlaps. To this end, we collect dense per-video-shot concept annotations, compile a new dataset, and suggest an efficient evaluation method defined upon the concept annotations. We conduct extensive experiments contrasting our video summarizer to existing ones and present detailed analyses about the dataset and the new evaluation method.

关键词： Semantics Measurement Visualization Automobiles Dictionaries computer vision Knowledge discovery Videotapes Dictionaries as Topic data mining Automobiles Semantics images computer vision Visualization evaluation method Dataset Cars semiotics

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Captioning Images with Diverse Objects 30

Captioning Images with Diverse Objects

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Venugopalan, Subhashini Mooney, Raymond Hendricks, Lisa Anne Darrell, Trevor Rohrbach, Marcus Saenko, Kate UT Austin Austin TX 78712 USA Univ Calif Berkeley Berkeley CA USA Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781538604571

Recent captioning models are limited in their ability to scale and describe concepts unseen in paired image-text corpora. We propose the Novel Object Captioner (NOC), a deep visual semantic captioning model that can describe a large number of object categories not present in existing image-caption datasets. Our model takes advantage of external sources -labeled images from object recognition datasets, and semantic knowledge extracted from unannotated text. We propose minimizing a joint objective which can learn from these diverse data sources and leverage distributional semantic embeddings, enabling the model to generalize and describe novel objects outside of image-caption datasets. We demonstrate that our model exploits semantic information to generate captions for hundreds of object categories in the Image Net object recognition data set that are not observed in MSCOCO image-caption training data, as well as many categories that are observed very rarely. Both automatic evaluations and human judgements show that our model considerably outperforms prior work in being able to describe many more categories of objects.

关键词： captions Data sources Dataset Semantics object recognition

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Multi-output regularized projection

Multi-output regularized projection

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conference on computer vision and pattern recognition

作者： Yu, K Yu, SP Tresp, V Corporate Technology Siemens AG Germany Institute for Computer Science University of Munich Germany

ISBN: (纸本)0769523722

Dimensionality reduction via feature projection has been widely used in pattern recognition and machine learning. It is often beneficial to derive the projections not only based on the inputs but also on the target values in the training data set. This is of particular importance in predicting multivariate or structured outputs. which is an area of growing interest. In this paper we introduce a novel projection framework which is sensitive to both input features and outputs. Based on the derived features prediction accuracy can be greatly improved. We validate our approach in two applications. The first is to model users ' preferences on a set of paintings. The second application is concerned with image categorization where each image may belong to multiple categories. The proposed algorithm produces very encouraging results in both settings.

关键词： Feature extraction

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