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SGTR: End-to-end Scene Graph Generation with Transformer

SGTR: End-to-end Scene Graph Generation with Transformer

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

作者： Li, Rongjie Zhang, Songyang He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Scene Graph Generation (SGG) remains a challenging visual understanding task due to its compositional property. Most previous works adopt a bottom-up two-stage or a point-based one-stage approach, which often suffers from high time complexity or sub-optimal designs. In this work, wc propose a novel SGG method to address the aforementioned issues, formulating the task as a bipartite graph construction problem. To solve the problem, we develop a transformer-based end-to-end framework that first generates the entity and predicate proposal set, followed by inferring directed edges to form the relation triplets. In particular, we develop a new entity-aware predicate representation based on a structural predicate generator that leverages the compositional property of relationships. Moreover, we design a graph assembling module to infer the connectivity of the bipartite scene graph based on our entity-aware structure, enabling us to generate the scene graph in an end-to-end manner. Extensive experimental results show that our design is able to achieve the state-of-the-art or comparable performance on two challenging benchmarks, surpassing most of the existing approaches and enjoying higher efficiency in inference. We hope our model can serve as a strong baseline for the Transformer-based scene graph generation.(1)

关键词： Visualization Image analysis Transformers pattern recognition Decoding Bipartite graph Proposals

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Unsupervised part learning for visual recognition 30

Unsupervised part learning for visual recognition

引用

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

作者： Sicre, Ronan Avrithis, Yannis Kijak, Ewa Jurie, Frederic INRIA IRISA Rennes France Normandie Univ UNICAEN ENSICAEN CNRS UMR GREYC Caen France

ISBN: (纸本)9781538604571

Part-based image classification aims at representing categories by small sets of learned discriminative parts, upon which an image representation is built. Considered as a promising avenue a decade ago, this direction has been neglected since the advent of deep neural networks. In this context, this paper brings two contributions: first, this work proceeds one step further compared to recent part-based models (PBM), focusing on how to learn parts without using any labeled data. Instead of learning a set of parts per class, as generally performed in the PBM literature, the proposed approach both constructs a partition of a given set of images into visually similar groups, and subsequently learns a set of discriminative parts per group in a fully unsupervised fashion. This strategy opens the door to the use of PBM in new applications where labeled data are typically not available, such as instance-based image retrieval. Second, this paper shows that despite the recent success of end-to-end models, explicit part learning can still boost classification performance. We experimentally show that our learned parts can help building efficient image representations, which outperform state-of-the art Deep Convolutional Neural Networks (DCNN) on both classification and retrieval tasks.

关键词： image representation phytobacteriomycin Image classification image retrieval end-to-end DOOR PART Neural network

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Language-driven Temporal Activity Localization: A Semantic Matching Reinforcement Learning Model 32

Language-driven Temporal Activity Localization: A Semantic M...

引用

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

作者： Wang, Weining Huang, Yan Wang, Liang Natl Lab Pattern Recognit NLPR Ctr Res Intelligent Percept & Comp CRIPAC Beijing Peoples R China Chinese Acad Sci CASIA Inst Automat Ctr Excellence Brain Sci & Intelligence Technol C Beijing Peoples R China Univ Chinese Acad Sci UCAS Beijing Peoples R China Chinese Acad Sci CAS AIR Artificial Intelligence Res Beijing Peoples R China

ISBN: (纸本)9781728132938

Current studies on action detection in untrimmed videos are mostly designed for action classes, where an action is described at word level such as jumping, tumbling, swing, etc. This paper focuses on a rarely investigated problem of localizing an activity via a sentence query which would be more challenging and practical. Considering that current methods are generally time-consuming due to the dense frame-processing manner, we propose a recurrent neural network based reinforcement learning model which selectively observes a sequence of frames and associates the given sentence with video content in a matching-based manner. However, directly matching sentences with video content performs poorly due to the large visual-semantic discrepancy. Thus, we extend the method to a semantic matching reinforcement learning (SM-RL) model by extracting semantic concepts of videos and then fusing them with global context features. Extensive experiments on three benchmark datasets, TACoS, Charades-STA and DiDeMo, show that our method achieves the state-of-the-art performance with a high detection speed, demonstrating both effectiveness and efficiency of our method.

关键词： Video Analytics vision + Language

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Active vision Dataset Benchmark 31

Active Vision Dataset Benchmark

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

作者： Ammirato, Phil Berg, Alexander C. Kosecka, Jana Univ N Carolina Chapel Hill NC 27514 USA George Mason Univ Fairfax VA 22030 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Several recent efforts in computer vision indicate a trend toward studying and understanding problems in larger scale environments, beyond single images, and focus on connections to tasks in navigation, mobile manipulation, and visual question answering. A common goal of these tasks is the capability of moving in the environment, acquiring novel views during perception and while performing a task. This capability comes easily in synthetic environments, however achieving the same effect with real images is much more laborious. We propose using the existing Active vision Dataset to form a benchmark for such problems in a real-world settings with real images. The dataset is well suited for evaluating tasks of multiview active recognition, target driven navigation, and target search, and also can be effective for studying the transfer of strategies learned in simulation to real settings.

关键词： Task analysis Training Visualization Robots Benchmark testing Navigation

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DC-SSL: Addressing Mismatched Class Distribution in Semi-supervised Learning

DC-SSL: Addressing Mismatched Class Distribution in Semi-sup...

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

作者： Zhao, Zhen Zhou, Luping Duan, Yue Wang, Lei Qi, Lei Shi, Yinghuan Univ Sydney Sydney NSW Australia Nanjing Univ Nanjing Peoples R China Univ Wollongong Wollongong NSW Australia Southeast Univ Dhaka Bangladesh

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Consistency-based Semi-supervised learning (SSL) has achieved promising performance recently. However, the success largely depends on the assumption that the labeled and unlabeled data share an identical class distribution, which is hard to meet in real practice. The distribution mismatch between the labeled and unlabeled sets can cause severe bias in the pseudo-labels of SSL, resulting in significant performance degradation. To bridge this gap, we put forward a new SSL learning framework, named Distribution Consistency SSL (DC-SSL), which rectifies the pseudolabels from a distribution perspective. The basic idea is to directly estimate a reference class distribution (RCD), which is regarded as a surrogate of the ground truth class distribution about the unlabeled data, and then improve the pseudo-labels by encouraging the predicted class distribution (PCD) of the unlabeled data to approach RCD gradually. To this end, this paper revisits the Exponentially Moving Average (EMA) model and utilizes it to estimate RCD in an iteratively improved manner, which is achieved with a momentum-update scheme throughout the training procedure. On top of this, two strategies are proposed for RCD to rectify the pseudo-label prediction, respectively. They correspond to an efficient training-free scheme and a training-based alternative that generates more accurate and reliable predictions. DC-SSL is evaluated on multiple SSL benchmarks and demonstrates remarkable performance improvement over competitive methods under matched- and mismatched-distribution scenarios.

关键词： Training Degradation Bridges computer vision Machine learning Semisupervised learning Benchmark testing

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Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets

Equivariance Allows Handling Multiple Nuisance Variables Whe...

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

作者： Lokhande, Vishnu Suresh Chakraborty, Rudrasis Ravi, Sathya N. Singh, Vikas

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Pooling multiple neuroimaging datasets across institutions often enables improvements in statistical power when evaluating associations (e.g., between risk factors and disease outcomes) that may otherwise be too weak to detect. When there is only a single source of variability (e.g., different scanners), domain adaptation and matching the distributions of representations may suffice in many scenarios. But in the presence of more than one nuisance variable which concurrently influence the measurements, pooling datasets poses unique challenges, e.g., variations in the data can come from both the acquisition method as well as the demographics of participants (gender, age). Invariant representation learning, by itself, is ill-suited to fully model the data generation process. In this paper, we show how bringing recent results on equivariant representation learning (for studying symmetries in neural networks) instantiated on structured spaces together with simple use of classical results on causal inference provides an effective practical solution. In particular, we demonstrate how our model allows dealing with more than one nuisance variable under some assumptions and can enable analysis of pooled scientific datasets in scenarios that would otherwise entail removing a large portion of the samples. Our code is available on https://***/vsingh-group/DatasetPooling

关键词： Representation learning Neuroimaging computer vision Codes Atmospheric measurements Neural networks Particle measurements

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SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network

SelfSAGCN: Self-Supervised Semantic Alignment for Graph Conv...

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

作者： Yang, Xu Deng, Cheng Dang, Zhiyuan Wei, Kun Yan, Junchi Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Shanghai Jiao Tong Univ Dept CSE Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665445092

Graph convolution networks (GCNs) are a powerful deep learning approach and have been successfully applied to representation learning on graphs in a variety of real-world applications. Despite their success, two fundamental weaknesses of GCNs limit their ability to represent graph-structured data: poor performance when labeled data are severely scarce and indistinguishable features when more layers are stacked. In this paper, we propose a simple yet effective Self-Supervised Semantic Alignment Graph Convolution Network (SelfSAGCN), which consists of two crux techniques: Identity Aggregation and Semantic Alignment, to overcome these weaknesses. The behind basic idea is the node features in the same class but learned from semantic and graph structural aspects respectively, are expected to be mapped nearby. Specifically, the Identity Aggregation is applied to extract semantic features from labeled nodes, the Semantic Alignment is utilized to align node features obtained from different aspects using the class central similarity. In this way, the over-smoothing phenomenon is alleviated, while the similarities between the unlabeled features and labeled ones from the same class are enhanced. Experimental results on five popular datasets show that the proposed SelfSAGCN outperforms state-of-the-art methods on various classification tasks.

关键词： Deep learning computer vision Convolution Computational modeling Semantics Benchmark testing Feature extraction

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Learning To Recognize Procedural Activities with Distant Supervision

Learning To Recognize Procedural Activities with Distant Sup...

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

作者： Lin, Xudong Petroni, Fabio Bertasius, Gedas Rohrbach, Marcus Chang, Shih-Fu Torresani, Lorenzo Columbia Univ New York NY 10027 USA Facebook AI Res Menlo Pk CA USA UNC Chapel Hill Chapel Hill NC USA Dartmouth Hanover NH USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper we consider the problem of classifying fine-grained, multi-step activities (e.g., cooking different recipes, making disparate home improvements, creating various forms of arts and crafts) from long videos spanning up to several minutes. Accurately categorizing these activities requires not only recognizing the individual steps that compose the task but also capturing their temporal dependencies. This problem is dramatically different from traditional action classification, where models are typically optimized on videos that span only a few seconds and that are manually trimmed to contain simple atomic actions. While step annotations could enable the training of models to recognize the individual steps of procedural activities, existing large-scale datasets in this area do not include such segment labels due to the prohibitive cost of manually annotating temporal boundaries in long videos. To address this issue, we propose to automatically identify steps in instructional videos by leveraging the distant supervision of a textual knowledge base (wikiHow) that includes detailed descriptions of the steps needed for the execution of a wide variety of complex activities. Our method uses a language model to match noisy, automatically-transcribed speech from the video to step descriptions in the knowledge base. We demonstrate that video models trained to recognize these automatically-labeled steps (without manual supervision) yield a representation that achieves superior generalization performance on four downstream tasks: recognition of procedural activities, step classification, step forecasting and egocentric video classification.

关键词： Training Knowledge based systems Manuals Predictive models pattern recognition Noise measurement Task analysis

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Towards Better Generalization: Joint Depth-Pose Learning without PoseNet

Towards Better Generalization: Joint Depth-Pose Learning wit...

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

作者： Zhao, Wang Liu, Shaohui Shu, Yezhi Liu, Yong-Jin Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781728171685

In this work, we tackle the essential problem of scale inconsistency for self-supervised joint depth-pose learning. Most existing methods assume that a consistent scale of depth and pose can be learned across all input samples, which makes the learning problem harder, resulting in degraded performance and limited generalization in indoor environments and long-sequence visual odometry application. To address this issue, we propose a novel system that explicitly disentangles scale from the network estimation. Instead of relying on PoseNet architecture, our method recovers relative pose by directly solving fundamental matrix from dense optical flow correspondence and makes use of a two-view triangulation module to recover an up-to-scale 3D structure. Then, we align the scale of the depth prediction with the triangulated point cloud and use the transformed depth map for depth error computation and dense reprojection check. Our whole system can be jointly trained end-to-end. Extensive experiments show that our system not only reaches state-of-the-art performance on KITTI depth and flow estimation, but also significantly improves the generalization ability of existing self-supervised depth-pose learning methods under a variety of challenging scenarios, and achieves state-of-the-art results among self-supervised learning-based methods on KITTI Odometry and NYUv2 dataset. Furthermore, we present some interesting findings on the limitation of PoseNet-based relative pose estimation methods in terms of generalization ability. Code is available at https://***/B1ueber2y/TrianFlow.

关键词： computer vision

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Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild

Learning to Recommend Frame for Interactive Video Object Seg...

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

作者： Yin, Zhaoyuan Zheng, Jia Luo, Weixin Qian, Shenhan Zhang, Hanling Gao, Shenghua Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China Manycore KooLab Rancho Cordova CA USA Meituan Grp Beijing Peoples R China ShanghaiTech Univ Shanghai Peoples R China Hunan Univ Sch Design Changsha Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9781665445092

This paper proposes a framework for the interactive video object segmentation (VOS) in the wild where users can choose some frames for annotations iteratively. Then, based on the user annotations, a segmentation algorithm refines the masks. The previous interactive VOS paradigm selects the frame with some worst evaluation metric, and the ground truth is required for calculating the evaluation metric, which is impractical in the testing phase. In contrast, in this paper, we advocate that the frame with the worst evaluation metric may not be exactly the most valuable frame that leads to the most performance improvement across the video. Thus, we formulate the frame selection problem in the interactive VOS as a Markov Decision Process, where an agent is learned to recommend the frame under a deep reinforcement learning framework. The learned agent can automatically determine the most valuable frame, making the interactive setting more practical in the wild. Experimental results on the public datasets show the effectiveness of our learned agent without any changes to the underlying VOS algorithms.

关键词： Measurement computer vision Codes Annotations Object segmentation Reinforcement learning Markov processes

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