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Spatiotemporal Self-Supervised Learning for Point Clouds in the Wild

Spatiotemporal Self-Supervised Learning for Point Clouds in ...

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conference on computer vision and pattern recognition (CVPR)

作者： Yanhao Wu Tong Zhang Wei Ke Sabine Susstrunk Mathieu Salzmann School of Software Engineering Xi'an Jiaotong University China School of Computer and Communication Sciences EPFL Switzerland

Self-supervised learning (SSL) has the potential to benefit many applications, particularly those where manually annotating data is cumbersome. One such situation is the semantic segmentation of point clouds. In this context, existing methods employ contrastive learning strategies and define positive pairs by performing various augmentation of point clusters in a single frame. As such, these methods do not exploit the temporal nature of LiDAR data. In this paper, we introduce an SSL strategy that leverages positive pairs in both the spatial and temporal domain. To this end, we design (i) a point-to-cluster learning strategy that aggregates spatial information to distinguish objects; and (ii) a cluster-to-cluster learning strategy based on unsupervised object tracking that exploits temporal correspondences. We demonstrate the benefits of our approach via extensive experiments performed by self-supervised training on two large-scale LiDAR datasets and transferring the resulting models to other point cloud segmentation benchmarks. Our results evidence that our method outperforms the state-of-the-art point cloud SSL methods. 1 1 Our code and pretrained models will be found at https://***/YanhaoWu/STSSL. Correspondence to Ke Wei.

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Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization

Gradient Norm Aware Minimization Seeks First-Order Flatness ...

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conference on computer vision and pattern recognition (CVPR)

作者： Xingxuan Zhang Renzhe Xu Han Yu Hao Zou Peng Cui Department of Computer Science Tsinghua University

Recently, flat minima are proven to be effective for improving generalization and sharpness-aware minimization (SAM) achieves state-of-the-art performance. Yet the current definition of flatness discussed in SAM and its follow-ups are limited to the zeroth-order flatness (i.e., the worst-case loss within a perturbation radius). We show that the zeroth-order flatness can be insufficient to discriminate minima with low generalization error from those with high generalization error both when there is a single minimum or multiple minima within the given perturbation radius. Thus we present first-order flatness, a stronger measure of flatness focusing on the maximal gradient norm within a perturbation radius which bounds both the maximal eigenvalue of Hessian at local minima and the regularization function of SAM. We also present a novel training procedure named Gradient norm Aware Minimization (GAM) to seek minima with uniformly small curvature across all directions. Experimental results show that GAM improves the generalization of models trained with current optimizers such as SGD and Adam W on various datasets and networks. Furthermore, we show that GAM can help SAM find flatter minima and achieve better generalization. The code is available at https://***/xxgege/GAM.

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Finding Geometric Models by Clustering in the Consensus Space

Finding Geometric Models by Clustering in the Consensus Spac...

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conference on computer vision and pattern recognition (CVPR)

作者： Daniel Barath Denys Rozumnyi Ivan Eichhardt Levente Hajder Jiri Matas Computer Vision and Geometry Group ETH Zurich Switzerland VRG Faculty of Electrical Engineering CTU in Prague Czech Republic TMEIC Corporation Americas Roanoke VA USA Eotvös Loránd University Budapest Hungary

We propose a new algorithm for finding an unknown number of geometric models, e.g., homographies. The problem is formalized as finding dominant model instances progressively without forming crisp point-to-model assignments. Dominant instances are found via a RANSAC-like sampling and a consolidation process driven by a model quality function considering previously proposed instances. New ones are found by clustering in the consensus space. This new formulation leads to a simple iterative algorithm with state-of-the-art accuracy while running in real-time on a number of vision problems - at least two orders of magnitude faster than the competitors on two-view motion estimation. Also, we propose a deterministic sampler reflecting the fact that real-world data tend to form spatially coherent structures. The sampler returns connected components in a progressively densified neighborhood-graph. We present a number of applications where the use of multiple geometric models improves accuracy. These include pose estimation from multiple generalized homographies; trajectory estimation of fast-moving objects; and we also propose a way of using multiple homographies in global SfM algorithms. Source code: https://***/danini/clustering-in-consensus-space.

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Continuous Intermediate Token Learning with Implicit Motion Manifold for Keyframe Based Motion Interpolation

Continuous Intermediate Token Learning with Implicit Motion ...

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conference on computer vision and pattern recognition (CVPR)

作者： Clinton A. Mo Kun Hu Chengjiang Long Zhiyong Wang School of Computer Science The University of Sydney NSW Australia Meta Reality Labs Burlingame CA USA

Deriving sophisticated 3D motions from sparse keyframes is a particularly challenging problem, due to continuity and exceptionally skeletal precision. The action features are often derivable accurately from the full series of keyframes, and thus, leveraging the global context with transformers has been a promising data-driven embedding approach. However, existing methods are often with inputs of interpolated intermediate frame for continuity using basic interpolation methods with keyframes, which result in a trivial local minimum during training. In this paper, we propose a novel framework to formulate latent motion manifolds with keyframe-based constraints, from which the continuous nature of intermediate token representations is considered. Particularly, our proposed framework consists of two stages for identifying a latent motion subspace, i.e., a keyframe encoding stage and an intermediate token generation stage, and a subsequent motion synthesis stage to extrapolate and compose motion data from manifolds. Through our extensive experiments conducted on both the LaFAN1 and CMU Mocap datasets, our proposed method demonstrates both superior interpolation accuracy and high visual similarity to ground truth motions.

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HDR Imaging with Spatially Varying Signal-to-Noise Ratios

HDR Imaging with Spatially Varying Signal-to-Noise Ratios

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conference on computer vision and pattern recognition (CVPR)

作者： Yiheng Chi Xingguang Zhang Stanley H. Chan School of Electrical and Computer Engineering Purdue University

While today's high dynamic range (HDR) image fusion algorithms are capable of blending multiple exposures, the acquisition is often controlled so that the dynamic range within one exposure is narrow. For HDR imaging in photon-limited situations, the dynamic range can be enormous and the noise within one exposure is spatially varying. Existing image denoising algorithms and HDR fusion algorithms both fail to handle this situation, leading to severe limitations in low-light HDR imaging. This paper presents two contributions. Firstly, we identify the source of the problem. We find that the issue is associated with the co-existence of (1) spatially varying signal-to-noise ratio, especially the excessive noise due to very dark regions, and (2) a wide luminance range within each exposure. We show that while the issue can be handled by a bank of denoisers, the complexity is high. Secondly, we propose a new method called the spatially varying high dynamic range (SV-HDR) fusion network to simultaneously denoise and fuse images. We introduce a new exposure-shared block within our custom-designed multi-scale transformer framework. In a variety of testing conditions, the performance of the proposed SV-HDR is better than the existing methods.

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AGAIN: Adversarial Training with Attribution Span Enlargement and Hybrid Feature Fusion

AGAIN: Adversarial Training with Attribution Span Enlargemen...

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conference on computer vision and pattern recognition (CVPR)

作者： Shenglin Yin Kelu Yao Sheng Shi Yangzhou Du Zhen Xiao School of Computer Science Peking University China Zhejiang Laboratory Hangzhou China Institute of Computing Technology Chinese Academy of Sciences China Northwest University Xi'an P. R. China AI Lab Lenovo Research Beijing P. R. China

The deep neural networks (DNNs) trained by adversarial training (AT) usually suffered from significant robust generalization gap, i.e., DNNs achieve high training robustness but low test robustness. In this paper, we propose a generic method to boost the robust generalization of AT methods from the novel perspective of attribution span. To this end, compared with standard DNNs, we discover that the generalization gap of adversarially trained DNNs is caused by the smaller attribution span on the input image. In other words, adversarially trained DNNs tend to focus on specific visual concepts on training images, causing its limitation on test robustness. In this way, to enhance the robustness, we propose an effective method to enlarge the learned attribution span. Besides, we use hybrid feature statistics for feature fusion to enrich the diversity of features. Extensive experiments show that our method can effectively improves robustness of adversarially trained DNNs, outperforming previous SOTA methods. Furthermore, we provide a theoretical analysis of our method to prove its effectiveness.

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Angelic Patches for Improving Third-Party Object Detector Performance

Angelic Patches for Improving Third-Party Object Detector Pe...

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conference on computer vision and pattern recognition (CVPR)

作者： Wenwen Si Shuo Li Sangdon Park Insup Lee Osbert Bastani Dept. of Computer & Info. Science University of Pennsylvania School of Cybersecurity & Privacy Georgia Institute of Technology

Deep learning models have shown extreme vulnerability to distribution shifts such as synthetic perturbations and spatial transformations. In this work, we explore whether we can adopt the characteristics of adversarial attack methods to help improve robustness of object detection to distribution shifts such as synthetic perturbations and spatial transformations. We study a class of realistic object detection settings wherein the target objects have control over their appearance. To this end, we propose a reversed Fast Gradient Sign Method (FGSM) to obtain these angelic patches that significantly increase the detection probability, even without pre-knowledge of the perturbations. In detail, we apply the patch to each object instance simultaneously, strengthening not only classification, but also bounding box accuracy. Experiments demonstrate the efficacy of the partial-covering patch in solving the complex bounding box problem. More importantly, the performance is also transferable to different detection models even under severe affine transformations and deformable shapes. To the best of our knowledge, we are the first object detection patch that achieves both cross-model efficacy and multiple patches. We observed average accuracy improvements of 30% in the real-world experiments. Our code is available at: https://***/averysi224/angelic_patches.

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SMPConv: Self-Moving Point Representations for Continuous Convolution

SMPConv: Self-Moving Point Representations for Continuous Co...

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conference on computer vision and pattern recognition (CVPR)

作者： Sanghyeon Kim Eunbyung Park Department of Electrical and Computer Engineering Sungkyunkwan University Department of Artificial Intelligence Sungkyunkwan University

Continuous convolution has recently gained prominence due to its ability to handle irregularly sampled data and model long-term dependency. Also, the promising experimental results of using large convolutional kernels have catalyzed the development of continuous convolution since they can construct large kernels very efficiently. Lever-aging neural networks, more specifically multilayer perceptrons (MLPs), is by far the most prevalent approach to implementing continuous convolution. However, there are a few drawbacks, such as high computational costs, complex hyperparameter tuning, and limited descriptive power of filters. This paper suggests an alternative approach to building a continuous convolution without neural networks, resulting in more computationally efficient and improved performance. We present self-moving point representations where weight parameters freely move, and interpolation schemes are used to implement continuous functions. When applied to construct convolutional kernels, the experimental results have shown improved performance with drop-in replacement in the existing frame-works. Due to its lightweight structure, we are first to demonstrate the effectiveness of continuous convolution in a large-scale setting, e.g., ImageNet, presenting the improvements over the prior arts. Our code is available on https://***/sangnekim/SMPConv

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A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation

A New Comprehensive Benchmark for Semi-supervised Video Anom...

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conference on computer vision and pattern recognition (CVPR)

作者： Congqi Cao Yue Lu Peng Wang Yanning Zhang ASGO School of Computer Science Northwestern Polytechnical University China

Semi-supervised video anomaly detection (VAD) is a critical task in the intelligent surveillance system. However, an essential type of anomaly in VAD named scene-dependent anomaly has not received the attention of researchers. Moreover, there is no research investigating anomaly anticipation, a more significant task for preventing the occurrence of anomalous events. To this end, we propose a new comprehensive dataset, NWPU Campus, containing 43 scenes, 28 classes of abnormal events, and 16 hours of videos. At present, it is the largest semi-supervised VAD dataset with the largest number of scenes and classes of anomalies, the longest duration, and the only one considering the scene-dependent anomaly. Meanwhile, it is also the first dataset proposed for video anomaly anticipation. We further propose a novel model capable of detecting and anticipating anomalous events simultaneously. Compared with 7 outstanding VAD algorithms in recent years, our method can cope with scene-dependent anomaly detection and anomaly anticipation both well, achieving state-of-the-art performance on ShanghaiTech, CUHK Avenue, IITB Corridor and the newly proposed NWPU Campus datasets consistently. Our dataset and code is available at: https://***.

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Text2Scene: Text-driven Indoor Scene Stylization with Part-Aware Details

Text2Scene: Text-driven Indoor Scene Stylization with Part-A...

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conference on computer vision and pattern recognition (CVPR)

作者： Inwoo Hwang Hyeonwoo Kim Young Min Kim Department of Electrical and Computer Engineering Seoul National University Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University

We propose Text2Scene, a method to automatically create realistic textures for virtual scenes composed of multiple objects. Guided by a reference image and text descriptions, our pipeline adds detailed texture on labeled 3D geometries in the room such that the generated colors respect the hierarchical structure or semantic parts that are often composed of similar materials. Instead of applying flat stylization on the entire scene at a single step, we obtain weak semantic cues from geometric segmentation, which are further clarified by assigning initial colors to segmented parts. Then we add texture details for individual objects such that their projections on image space exhibit feature embedding aligned with the embedding of the input. The decomposition makes the entire pipeline tractable to a moderate amount of computation resources and memory. As our framework utilizes the existing resources of image and text embedding, it does not require dedicated datasets with high-quality textures designed by skillful artists. To the best of our knowledge, it is the first practical and scalable approach that can create detailed and realistic textures of the desired style that maintain structural context for scenes with multiple objects.

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