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Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers

Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Cen...

引用

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

作者： Bhattacharyya, Apratim Reino, Daniel Olmeda Fritz, Mario Schiele, Bernt Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany Toyota Motor Europe Brussels Belgium CISPA Helmholtz Ctr Informat Secur Saarbrucken Germany

ISBN: (纸本)9781665445092

Accurate prediction of pedestrian and bicyclist paths is integral to the development of reliable autonomous vehicles in dense urban environments. The interactions between vehicle and pedestrian or bicyclist have a significant impact on the trajectories of traffic participants e.g. stopping or turning to avoid collisions. Although recent datasets and trajectory prediction approaches have fostered the development of autonomous vehicles yet the amount of vehicle-pedestrian (bicyclist) interactions modeled are sparse. In this work, we propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In particular, our dataset caters more diverse and complex interactions in dense urban scenarios compared to the existing datasets. To address the challenges in predicting future trajectories with dense interactions, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. This enables our Joint-beta-cVAE approach to better model the distribution of future trajectories. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.

关键词： computer vision Computational modeling Urban areas Predictive models Turning Trajectory pattern recognition

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Cloud2Curve: Generation and Vectorization of Parametric Sketches

Cloud2Curve: Generation and Vectorization of Parametric Sket...

引用

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

作者： Das, Ayan Yang, Yongxin Hospedales, Timothy Xiang, Tao Song, Yi-Zhe Univ Surrey CVSSP SketchX Guildford Surrey England iFlyTek Surrey Joint Res Ctr Artificial Intellige Guildford Surrey England Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9781665445092

Analysis of human sketches in deep learning has advanced immensely through the use of waypoint-sequences rather than raster-graphic representations. We further aim to model sketches as a sequence of low-dimensional parametric curves. To this end, we propose an inverse graphics framework capable of approximating a raster or waypoint based stroke encoded as a point-cloud with a variable-degree Bezier curve. Building on this module, we present Cloud2Curve, a generative model for scalable high-resolution vector sketches that can be trained end-to-end using point-cloud data alone. As a consequence, our model is also capable of deterministic vectorization which can map novel raster or waypoint based sketches to their corresponding high-resolution scalable Bezier equivalent. We evaluate the generation and vectorization capabilities of our model on Quick, Draw! and K-MNIST datasets.

关键词： Training Graphics Deep learning computer vision Computational modeling Fitting computer architecture

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Troubleshooting Blind Image Quality Models in the Wild

Troubleshooting Blind Image Quality Models in the Wild

引用

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

作者： Wang, Zhihua Wang, Haotao Chen, Tianlong Wang, Zhangyang Ma, Kede City Univ Hong Kong Hong Kong Peoples R China Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665445092

Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics. When applying this type of approach to troubleshoot "best-performing" BIQA models in the wild, we are faced with a practical challenge: it is highly nontrivial to obtain stronger competing models for efficient failure-spotting. Inspired by recent findings that difficult samples of deep models may be exposed through network pruning, we construct a set of "self-competitors," as random ensembles of pruned versions of the target model to be improved. Diverse failures can then be efficiently identified via self-gMAD competition. Next, we fine-tune both the target and its pruned variants on the human-rated gMAD set. This allows all models to learn from their respective failures, preparing themselves for the next round of self-gMAD competition. Experimental results demonstrate that our method efficiently troubleshoots BIQA models in the wild with improved generalizability.

关键词： Image quality Measurement computer vision Computational modeling Mathematical models pattern recognition Computational efficiency

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Temporal-Relational CrossTransformers for Few-Shot Action recognition

Temporal-Relational CrossTransformers for Few-Shot Action Re...

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

作者： Perrett, Toby Masullo, Alessandro Burghardt, Tilo Mirmehdi, Majid Damen, Dima Univ Bristol Dept Comp Sci Bristol Avon England

ISBN: (纸本)9781665445092

We propose a novel approach to few-shot action recognition, finding temporally-corresponding frame tuples between the query and videos in the support set. Distinct from previous few-shot works, we construct class prototypes using the CrossTransformer attention mechanism to observe relevant sub-sequences of all support videos, rather than using class averages or single best matches. Video representations are formed from ordered tuples of varying numbers of frames, which allows sub-sequences of actions at different speeds and temporal offsets to be compared.(1) Our proposed Temporal-Relational CrossTransformers (TRX) achieve state-of-the-art results on few-shot splits of Kinetics, Something-Something V2 (SSv2), HMDB51 and UCF101. Importantly, our method outperforms prior work on SSv2 by a wide margin (12%) due to the its ability to model temporal relations. A detailed ablation showcases the importance of matching to multiple support set videos and learning higher-order relational CrossTransformers.

关键词： computer vision Prototypes Kinetic theory Spatiotemporal phenomena pattern recognition Videos

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D²IM-Net: Learning Detail Disentangled Implicit Fields from Single Images

D<SUP>2</SUP>IM-Net: Learning Detail Disentangled Implicit F...

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

作者： Li, Manyi Zhang, Hao Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features. Our key idea is to train the network to learn a detail disentangled reconstruction consisting of two functions, one implicit field representing the coarse 3D shape and the other capturing the details. Given an input image, our network, coined (DIM)-I-2-Net, encodes it into global and local features which are respectively fed into two decoders. The base decoder uses the global features to reconstruct a coarse implicit field, while the detail decoder reconstructs, from the local features, two displacement maps, defined over the front and back sides of the captured object. The final 3D reconstruction is a fusion between the base shape and the displacement maps, with three losses enforcing the recovery of coarse shape, overall structure, and surface details via a novel Laplacian term.

关键词： Surface reconstruction computer vision Three-dimensional displays Laplace equations Shape Decoding pattern recognition

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Representation Learning via Global Temporal Alignment and Cycle-Consistency

Representation Learning via Global Temporal Alignment and Cy...

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

作者： Hadji, Isma Derpanis, Konstantinos G. Jepson, Allan D. Samsung AI Ctr Toronto Toronto ON Canada

ISBN: (纸本)9781665445092

We introduce a weakly supervised method for representation learning based on aligning temporal sequences (e.g., videos) of the same process (e.g., human action). The main idea is to use the global temporal ordering of latent correspondences across sequence pairs as a supervisory signal. In particular, we propose a loss based on scoring the optimal sequence alignment to train an embedding network. Our loss is based on a novel probabilistic path finding view of dynamic time warping (DTW) that contains the following three key features: (i) the local path routing decisions are contrastive and differentiable, (ii) pairwise distances are cast as probabilities that are contrastive as well, and (iii) our formulation naturally admits a global cycle-consistency loss that verifies correspondences. For evaluation, we consider the tasks of fine-grained action classification, few shot learning, and video synchronization. We report significant performance increases over previous methods. In addition, we report two applications of our temporal alignment framework, namely 3D pose reconstruction and fine-grained audio/visual retrieval.

关键词： computer vision Three-dimensional displays Probabilistic logic Routing pattern recognition Synchronization Task analysis

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Neural Auto-Exposure for High-Dynamic Range Object Detection

Neural Auto-Exposure for High-Dynamic Range Object Detection

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

作者： Onzon, Emmanuel Mannan, Fahim Heide, Felix Algolux Montreal PQ Canada Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781665445092

Real-world scenes have a dynamic range of up to 280 dB that todays imaging sensors cannot directly capture. Existing live vision pipelines tackle this fundamental challenge by relying on high dynamic range (HDR) sensors that try to recover HDR images from multiple captures with different exposures. While HDR sensors substantially increase the dynamic range, they are not without disadvantages, including severe artifacts for dynamic scenes, reduced fill factor, lower resolution, and high sensor cost. At the same time, traditional auto-exposure methods for low-dynamic range sensors have advanced as proprietary methods relying on image statistics separated from downstream vision algorithms. In this work, we revisit auto-exposure control as an alternative to HDR sensors. We propose a neural network for exposure selection that is trained jointly, end-to-end with an object detector and an image signal processing (ISP) pipeline. To this end, we use an HDR dataset for automotive object detection and an HDR training procedure. We validate that the proposed neural auto-exposure control, which is tailored to object detection, outperforms conventional auto-exposure methods by more than 6 points in mean average precision (mAP).

关键词： Image sensors Training computer vision Pipelines Object detection computer architecture Dynamic range

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Mirror3D: Depth Refinement for Mirror Surfaces

Mirror3D: Depth Refinement for Mirror Surfaces

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

作者： Tan, Jiaqi Lin, Weijie Chang, Angel X. Savva, Manolis Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

Despite recent progress in depth sensing and 3D reconstruction, mirror surfaces are a significant source of errors. To address this problem, we create the Mirror3D dataset: a 3D mirror plane dataset based on three RGBD datasets (Matterpot3D, NYUv2 and ScanNet) containing 7,011 mirror instance masks and 3D planes. We then develop Mirror3DNet: a module that refines raw sensor depth or estimated depth to correct errors on mirror surfaces. Our key idea is to estimate the 3D mirror plane based on RGB input and surrounding depth context, and use this estimate to directly regress mirror surface depth. Our experiments show that Mirror3DNet significantly mitigates errors from a variety of input depth data, including raw sensor depth and depth estimation or completion methods.

关键词： Surface reconstruction computer vision Three-dimensional displays Estimation computer architecture Prediction methods Sensors

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PSD: Principled Synthetic-to-Real Dehazing Guided by Physical Priors

PSD: Principled Synthetic-to-Real Dehazing Guided by Physica...

引用

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

作者： Chen, Zeyuan Wang, Yangchao Yang, Yang Liu, Dong Univ Sci & Technol China Hefei Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781665445092

Deep learning-based methods have achieved remarkable performance for image dehazing. However, previous studies are mostly focused on training models with synthetic hazy images, which incurs performance drop when the models are used for real-world hazy images. We propose a Principled Synthetic-to-real Dehazing (PSD) framework to improve the generalization performance of dehazing. Starting from a dehazing model backbone that is pre-trained on synthetic data, PSD exploits real hazy images to fine-tune the model in an unsupervised fashion. For the fine-tuning, we leverage several well-grounded physical priors and combine them into a prior loss committee. PSD allows for most of the existing dehazing models as its backbone, and the combination of multiple physical priors boosts dehazing significantly. Through extensive experiments, we demonstrate that our PSD framework establishes the new state-of-the-art performance for real-world dehazing, in terms of visual quality assessed by no-reference quality metrics as well as subjective evaluation and downstream task performance indicator.

关键词： Training Measurement Learning systems Visualization computer vision Data models pattern recognition

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Omnimatte: Associating Objects and Their Effects in Video

Omnimatte: Associating Objects and Their Effects in Video

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

作者： Lu, Erika Cole, Forrester Dekel, Tali Zisserman, Andrew Freeman, William T. Rubinstein, Michael Google Res Mountain View CA 94043 USA Univ Oxford Oxford England Weizmann Inst Sci Rehovot Israel

ISBN: (纸本)9781665445092

computer vision is increasingly effective at segmenting objects in images and videos;however, scene effects related to the objects-shadows, reflections, generated smoke, etc.-are typically overlooked. Identifying such scene effects and associating them with the objects producing them is important for improving our fundamental understanding of visual scenes, and can also assist a variety of applications such as removing, duplicating, or enhancing objects in video. In this work, we take a step towards solving this novel problem of automatically associating objects with their effects in video. Given an ordinary video and a rough segmentation mask over time of one or more subjects of interest, we estimate an omnimatte for each subject-an alpha matte and color image that includes the subject along with all its related time-varying scene elements. Our model is trained only on the input video in a self-supervised manner, without any manual labels, and is generic-it produces omnimattes automatically for arbitrary objects and a variety of effects. We show results on real-world videos containing interactions between different types of subjects (cars, animals, people) and complex effects, ranging from semitransparent elements such as smoke and reflections, to fully opaque effects such as objects attached to the subject.

关键词： Training Image segmentation computer vision Color Visual effects Reflection pattern recognition

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