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Towards Open World Object Detection

Towards Open World Object Detection

引用

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

作者： Joseph, K. J. Khan, Salman Khan, Fahad Shahbaz Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9781665445092

Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosity about these unknown instances aids in learning about them, when the corresponding knowledge is eventually available. This motivates us to propose a novel computer vision problem called: 'Open World Object Detection', where a model is tasked to: 1) identify objects that have not been introduced to it as 'unknown', without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, when the corresponding labels are progressively received. We formulate the problem, introduce a strong evaluation protocol and provide a novel solution, which we call ORE: Open World Object Detector, based on contrastive clustering and energy based unknown identification. Our experimental evaluation and ablation studies analyse the efficacy of ORE in achieving Open World objectives. As an interesting by-product, we find that identifying and characterising unknown instances helps to reduce confusion in an incremental object detection setting, where we achieve state-ofthe-art performance, with no extra methodological effort. We hope that our work will attract further research into this newly identified, yet crucial research direction.'

关键词： computer vision Protocols Computational modeling Object detection Detectors Benchmark testing pattern recognition

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You See What I Want You to See: Exploring Targeted Black-Box Transferability Attack for Hash-based Image Retrieval Systems

You See What I Want You to See: Exploring Targeted Black-Box...

引用

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

作者： Xiao, Yanru Wang, Cong Old Dominion Univ Norfolk VA 23529 USA

ISBN: (纸本)9781665445092

With the large multimedia content online, deep hashing has become a popular method for efficient image retrieval and storage. However, by inheriting the algorithmic back-end from softmax classification, these techniques are vulnerable to the well-known adversarial examples as well. The massive collection of online images into the database also opens up new attack vectors. Attackers can embed adversarial images into the database and target specific categories to be retrieved by user queries. In this paper, we start from an adversarial standpoint to explore and enhance the capacity of targeted black-box transferability attack for deep hashing. We motivate this work by a series of empirical studies to see the unique challenges in image retrieval. We study the relations between adversarial subspace and black-box transferability via utilizing random noise as a proxy. Then we develop a new attack that is simultaneously adversarial and robust to noise to enhance transferability. Our experimental results demonstrate about 1.2-3x improvements of black-box transferability compared with the state-of-the-art mechanisms.

关键词： computer vision Codes Image retrieval Multimedia databases pattern recognition Classification algorithms Image storage

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Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation

Smoothing the Disentangled Latent Style Space for Unsupervis...

引用

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

作者： Liu, Yahui Sangineto, Enver Chen, Yajing Bao, Linchao Zhang, Haoxian Sebe, Nicu Lepri, Bruno Wang, Wei De Nadai, Marco Univ Trento Trento Italy Tencent AI Lab Shenzhen Peoples R China Fdn Bruno Kessler Povo Italy

ISBN: (纸本)9781665445092

Image-to-Image (I2I) multi-domain translation models are usually evaluated also using the quality of their semantic interpolation results. However, state-of-the-art models frequently show abrupt changes in the image appearance during interpolation, and usually perform poorly in interpolations across domains. In this paper, we propose a new training protocol based on three specific losses which help a translation network to learn a smooth and disentangled latent style space in which: 1) Both intra- and inter-domain interpolations correspond to gradual changes in the generated images and 2) The content of the source image is better preserved during the translation. Moreover, we propose a novel evaluation metric to properly measure the smoothness of latent style space of I2I translation models. The proposed method can be plugged in existing translation approaches, and our extensive experiments on different datasets show that it can significantly boost the quality of the generated images and the graduality of the interpolations.

关键词： Training Interpolation computer vision Smoothing methods Protocols Computational modeling Semantics

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Depth Completion with Twin Surface Extrapolation at Occlusion Boundaries

Depth Completion with Twin Surface Extrapolation at Occlusio...

引用

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

作者： Imran, Saif Liu, Xiaoming Morris, Daniel Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9781665445092

Depth completion starts from a sparse set of known depth values and estimates the unknown depths for the remaining image pixels. Most methods model this as depth interpolation and erroneously interpolate depth pixels into the empty space between spatially distinct objects, resulting in depth-smearing across occlusion boundaries. Here we propose a multi-hypothesis depth representation that explicitly models both foreground and background depths in the difficult occlusion-boundary regions. Our method can be thought of as performing twin-surface extrapolation, rather than interpolation, in these regions. Next our method fuses these extrapolated surfaces into a single depth image leveraging the image data. Key to our method is the use of an asymmetric loss function that operates on a novel twin-surface representation. This enables us to train a network to simultaneously do surface extrapolation and surface fusion. We characterize our loss function and compare with other common losses. Finally, we validate our method on three different datasets;KITTI, an outdoor real-world dataset, NYU2, indoor real-world depth dataset and Virtual KITTI, a photo-realistic synthetic dataset with dense groundtruth, and demonstrate improvement over the state of the art.

关键词： Extrapolation Interpolation computer vision Fuses Estimation Channel estimation Robustness

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FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions

FCPose: Fully Convolutional Multi-Person Pose Estimation wit...

引用

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

作者： Mao, Weian Tian, Zhi Wang, Xinlong Shen, Chunhua Univ Adelaide Adelaide SA Australia Monash Univ Melbourne Vic Australia

ISBN: (纸本)9781665445092

We propose a fully convolutional multi-person pose estimation framework using dynamic instance-aware convolutions, termed FCPose. Different from existing methods, which often require ROI (Region of Interest) operations and/or grouping post-processing, FCPose eliminates the ROIs and grouping post-processing with dynamic instance-aware keypoint estimation heads. The dynamic keypoint heads are conditioned on each instance (person), and can encode the instance concept in the dynamically-generated weights of their filters. Moreover, with the strong representation capacity of dynamic convolutions, the keypoint heads in FCPose are designed to be very compact, resulting in fast inference and making FCPose have almost constant inference time regardless of the number of persons in the image. For example, on the COCO dataset, a real-time version of FCPose using the DLA-34 backbone infers about 4.5xfaster than Mask R-CNN (ResNet-101) (41.67 FPS vs. 9.26 FPS) while achieving improved performance (64.8% AP kp vs. 64.3% AP kp ). FCPose also offers better speed/accuracy trade-off than other state-of-the-art methods. Our experiment results show that FCPose is a simple yet effective multi-person pose estimation framework.

关键词： Convolutional codes computer vision Head Pose estimation Graphics processing units Real-time systems pattern recognition

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MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation

MetaAlign: Coordinating Domain Alignment and Classification ...

引用

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

作者： Wei, Guoqiang Lan, Cuiling Zeng, Wenjun Chen, Zhibo Univ Sci & Technol China Hefei Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665445092

For unsupervised domain adaptation (UDA), to alleviate the effect of domain shift, many approaches align the source and target domains in the feature space by adversarial learning or by explicitly aligning their statistics. However, the optimization objective of such domain alignment is generally not coordinated with that of the object classification task itself such that their descent directions for optimization may be inconsistent. This will reduce the effectiveness of domain alignment in improving the performance of UDA. In this paper, we aim to study and alleviate the optimization inconsistency problem between the domain alignment and classification tasks. We address this by proposing an effective meta-optimization based strategy dubbed MetaAlign, where we treat the domain alignment objective and the classification objective as the meta-train and meta-test tasks in a meta-learning scheme. MetaAlign encourages both tasks to be optimized in a coordinated way, which maximizes the inner product of the gradients of the two tasks during training. Experimental results demonstrate the effectiveness of our proposed method on top of various alignment-based baseline approaches, for tasks of object classification and object detection. MetaAlign helps achieve the state-of-the-art performance.

关键词： Training computer vision Object detection Adversarial machine learning pattern recognition Task analysis Optimization

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Plan2Scene: Converting Floorplans to 3D Scenes

Plan2Scene: Converting Floorplans to 3D Scenes

引用

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

作者： Vidanapathirana, Madhawa Wu, Qirui Furukawa, Yasutaka Chang, Angel X. Savva, Manolis Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

We address the task of converting a floorplan and a set of associated photos of a residence into a textured 3D mesh model, a task which we call Plan2Scene. Our system 1) lifts a floorplan image to a 3D mesh model;2) synthesizes surface textures based on the input photos;and 3) infers textures for unobserved surfaces using a graph neural network architecture. To train and evaluate our system we create indoor surface texture datasets, and augment a dataset of floorplans and photos from prior work with rectified surface crops and additional annotations. Our approach handles the challenge of producing tileable textures for dominant surfaces such as floors, walls, and ceilings from a sparse set of unaligned photos that only partially cover the residence. Qualitative and quantitative evaluations show that our system produces realistic 3D interior models, outperforming baseline approaches on a suite of texture quality metrics and as measured by a holistic user study.

关键词： Measurement Solid modeling computer vision Three-dimensional displays Crops Graph neural networks Surface texture

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Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors

Function4D: Real-time Human Volumetric Capture from Very Spa...

引用

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

作者： Yu, Tao Zheng, Zerong Guo, Kaiwen Liu, Pengpeng Dai, Qionghai Liu, Yebin Tsinghua Univ Dept Automat Beijing Peoples R China Google Zurich Switzerland Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9781665445092

Human volumetric capture is a long-standing topic in computer vision and computer graphics. Although high-quality results can be achieved using sophisticated off-line systems, real-time human volumetric capture of complex scenarios, especially using light-weight setups, remains challenging. In this paper, we propose a human volumetric capture method that combines temporal volumetric fusion and deep implicit functions. To achieve high-quality and temporal-continuous reconstruction, we propose dynamic sliding fusion to fuse neighboring depth observations together with topology consistency. Moreover, for detailed and complete surface generation, we propose detail-preserving deep implicit functions for RGBD input which can not only preserve the geometric details on the depth inputs but also generate more plausible texturing results. Results and experiments show that our method outperforms existing methods in terms of view sparsity, generalization capacity, reconstruction quality, and run-time efficiency.

关键词： Hair Geometry Surface reconstruction computer vision Real-time systems Sensor systems Sensors

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Adaptive Rank Estimate in Robust Principal Component Analysis

Adaptive Rank Estimate in Robust Principal Component Analysi...

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

作者： Xu, Zhengqin He, Rui Xie, Shoulie Wu, Shiqian Wuhan Univ Sci & Technol Sch Machinery & Automat Wuhan Hubei Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Hubei Peoples R China Wuhan Univ Sci & Technol Inst Robot & Intelligent Syst Wuhan Hubei Peoples R China Inst Infocomm Res A STAR Signal Proc RF & Opt Dept Singapore Singapore

ISBN: (纸本)9781665445092

Robust principal component analysis (RPCA) and its variants have gained wide applications in computer vision. However, these methods either involve manual adjustment of some parameters, or require the rank of a low-rank mafrix to be known a prior. In this paper, an adaptive rank estimate based RPCA (ARE-RPCA) is proposed, which adaptively assigns weights on different singular values via rank estimation. More specifically, we study the characteristics of the low-rank mafrix, and develop an improved Gerschgorin disk theorem to estimate the rank of the low-rank matrix accurately. Furthermore in view of the issue occurred in the Gerschgorin disk theorem that adjustment factor need to be manually pre-defined, an adaptive setting method, which greatly facilitates the practical implementation of the rank estimation, is presented. Then, the weights of singular values in the nuclear norm are updated adaptively based on iteratively estimated rank, and the resultant low-rank matrix is close to the target. Experimental results show that the proposed ARE-RPCA outperforms the state-of-the-art methods in various complex scenarios.

关键词： computer vision Estimation Manuals pattern recognition Sparse matrices Principal component analysis

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DriveGAN: Towards a Controllable High-Quality Neural Simulation

DriveGAN: Towards a Controllable High-Quality Neural Simulat...

引用

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

作者： Kim, Seung Wook Philion, Jonah Torralba, Antonio Fidler, Sanja NVIDIA Santa Clara CA 95051 USA Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665445092

Realistic simulators are critical for training and verifying robotics systems. While most of the contemporary simulators are hand-crafted, a scaleable way to build simulators is to use machine learning to learn how the environment behaves in response to an action, directly from data. In this work, we aim to learn to simulate a dynamic environment directly in pixel-space, by watching unannotated sequences offrames and their associated actions. We introduce a novel high-quality neural simulator referred to as DriveGAN that achieves controllability by disentangling different components without supervision. In addition to steering controls, it also includes controls for sampling features of a scene, such as the weather as well as the location of non player objects. Since DriveGAN is a filly differentiable simulator, it further allows for re-simulation of a given video sequence, offering an agent to drive through a recorded scene again, possibly taking different actions. We train DriveGAN on multiple datasets, including 160 hours of real-world driving data. We showcase that our approach greatly surpasses the performance of previous data-driven simulators, and allows for new key features not explored before.

关键词： Training computer vision Computational modeling Video sequences Machine learning Generative adversarial networks Controllability

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