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SceneGen: Learning to Generate Realistic Traffic Scenes

SceneGen: Learning to Generate Realistic Traffic Scenes

引用

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

作者： Tan, Shuhan Wong, Kelvin Wang, Shenlong Manivasagam, Sivabalan Ren, Mengye Urtasun, Raquel Uber Adv Technol Grp Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Univ Toronto Toronto ON Canada

ISBN: (纸本)9781665445092

We consider the problem of generating realistic traffic scenes automatically. Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics and are limited in their ability to model the true complexity and diversity of real traffic scenes, thus inducing a content gap between synthesized traffic scenes versus real ones. As a result, existing simulators lack the fidelity necessary to train and test self-driving vehicles. To address this limitation, we present SceneGen-a neural autoregressive model of traffic scenes that eschews the need for rules and heuristics. In particular, given the ego-vehicle state and a high definition map of surrounding area, SceneGen inserts actors of various classes into the scene and synthesizes their sizes, orientations, and velocities. We demonstrate on two large-scale datasets SceneGen's ability to faithfully model distributions of real traffic scenes. Moreover, we show that SceneGen coupled with sensor simulation can be used to train perception models that generalize to the real world.

关键词： computer vision Computational modeling Complexity theory pattern recognition

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FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation

FlowStep3D: Model Unrolling for Self-Supervised Scene Flow E...

引用

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

作者： Kittenplon, Yair Eldar, Yonina C. Raviv, Dan Tel Aviv Univ Tel Aviv Israel Weizmann Inst Sci Rehovot Israel

ISBN: (纸本)9781665445092

Estimating the 3D motion of points in a scene, known as scene flow, is a core problem in computer vision. Traditional learning-based methods designed to learn end-to-end 3D flow often suffer from poor generalization. Here we present a recurrent architecture that learns a single step of an unrolled iterative alignment procedure for refining scene flow predictions. Inspired by classical algorithms, we demonstrate iterative convergence toward the solution using strong regularization. The proposed method can handle sizeable temporal deformations and suggests a slimmer architecture than competitive all-to-all correlation approaches. Trained on FlyingThings3D synthetic data only, our network successfully generalizes to real scans, outperforming all existing methods by a large margin on the KITTI self-supervised benchmark.(1)

关键词： Training Learning systems computer vision Three-dimensional displays Refining Estimation computer architecture

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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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Deep Lucas-Kanade Homography for Multimodal Image Alignment

Deep Lucas-Kanade Homography for Multimodal Image Alignment

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

作者： Zhao, Yiming Huang, Xinming Zhang, Ziming Worcester Polytech Inst 100 Inst Rd Worcester MA 01609 USA

ISBN: (纸本)9781665445092

Estimating homography to align image pairs captured by different sensors or image pairs with large appearance changes is an important and general challenge for many computer vision applications. In contrast to others, we propose a generic solution to pixel-wise align multimodal image pairs by extending the traditional Lucas-Kanade algorithm with networks. The key contribution in our method is how we construct feature maps, named as deep Lucas-Kanade feature map (DLKFM). The learned DLKFM can spontaneously recognize invariant features under various appearance-changing conditions. It also has two nice properties for the Lucas-Kanade algorithm: (1) The template feature map keeps brightness consistency with the input feature map, thus the color difference is very small while they are well-aligned. (2) The Lucas-Kanade objective function built on DLKFM has a smooth landscape around ground truth homography parameters, so the iterative solution of the Lucas-Kanade can easily converge to the ground truth. With those properties, directly updating the Lucas-Kanade algorithm on our feature maps will precisely align image pairs with large appearance changes. We share the datasets, code, and demo video online

关键词： computer vision Shape Machine learning Linear programming Feature extraction Iterative algorithms Sensors

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Task Residual for Tuning vision-Language Models

Task Residual for Tuning Vision-Language Models

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conference on computer vision and pattern recognition (cvpr)

作者： Tao Yu Zhihe Lu Xin Jin Zhibo Chen Xinchao Wang National University of Singapore University of Science and Technology of China

Large-scale vision-language models (VLMs) pre-trained on billion-level data have learned general visual representations and broad visual concepts. In principle, the welllearned knowledge structure of the VLMs should be inherited appropriately when being transferred to downstream tasks with limited data. However, most existing efficient transfer learning (ETL) approaches for VLMs either damage or are excessively biased towards the prior knowledge, e.g., prompt tuning (PT) discards the pre-trained text-based classifier and builds a new one while adapter-style tuning (AT) fully relies on the pre-trained features. To address this, we propose a new efficient tuning approach for VLMs named Task Residual Tuning (TaskRes), which performs directly on the text-based classifier and explicitly decouples the prior knowledge of the pre-trained models and new knowledge regarding a target task. Specifically, TaskRes keeps the original classifier weights from the VLMs frozen and obtains a new classifier for the target task by tuning a set of prior-independent parameters as a residual to the original one, which enables reliable prior knowledge preservation and flexible task-specific knowledge exploration. The proposed TaskRes is simple yet effective, which significantly outperforms previous ETL methods (e.g., PT and AT) on 11 benchmark datasets while requiring minimal effort for the implementation. Our code is available at https://***/geekyutao/TaskRes.

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Isometric Multi-Shape Matching

Isometric Multi-Shape Matching

引用

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

作者： Gao, Maolin Laehner, Zorah Thunberg, Johan Cremers, Daniel Bernard, Florian Tech Univ Munich Munich Germany Halmstad Univ Halmstad Sweden Univ Siegen Siegen Germany

ISBN: (纸本)9781665445092

Finding correspondences between shapes is a fundamental problem in computer vision and graphics, which is relevant for many applications, including 3D reconstruction, object tracking, and style transfer. The vast majority of correspondence methods aim to find a solution between pairs of shapes, even if multiple instances of the same class are available. While isometries are often studied in shape correspondence problems, they have not been considered explicitly in the multi-matching setting. This paper closes this gap by proposing a novel optimisation formulation for isometric multi-shape matching. We present a suitable optimisation algorithm for solving our formulation and provide a convergence and complexity analysis. Our algorithm obtains multi-matchings that are by construction provably cycle-consistent. We demonstrate the superior performance of our method on various datasets and set the new state-of-the-art in isometric multi-shape matching.

关键词： computer vision Three-dimensional displays Shape Linear programming Complexity theory Object tracking Optimization

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Scene Essence

Scene Essence

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

作者： Qiu, Jiayan Yang, Yiding Wang, Xinchao Tao, Dacheng Univ Sydney Sydney NSW Australia Stevens Inst Technol Hoboken NJ 07030 USA Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781665445092

What scene elements, if any, are indispensable for recognizing a scene? We strive to answer this question through the lens of an exotic learning scheme. Our goal is to identify a collection of such pivotal elements, which we term as Scene Essence, to be those that would alter scene recognition if taken out from the scene. To this end, we devise a novel approach that learns to partition the scene objects into two groups, essential ones and minor ones, under the supervision that if only the essential ones are kept while the minor ones are erased in the input image, a scene recognizer would preserve its original prediction. Specifically, we introduce a learnable graph neural network (GNN) for labelling scene objects, based on which the minor ones are wiped off by an off-the-shelf image inpainter. The features of the inpainted image derived in this way, together with those learned from the GNN with the minor-object nodes pruned, are expected to fool the scene discriminator. Both subjective and objective evaluations on Places365, SUN397, and MIT67 datasets demonstrate that, the learned Scene Essence yields a visually plausible image that convincingly retains the original scene category.

关键词： computer vision Image recognition Graph neural networks pattern recognition Labeling Lenses

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N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution

N-Gram in Swin Transformers for Efficient Lightweight Image ...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Haram Choi Jeongmin Lee Jihoon Yang Department of Computer Science & Engineering Sogang University LG Innotek

While some studies have proven that Swin Transformer (Swin) with window self-attention (WSA) is suitable for single image super-resolution (SR), the plain WSA ignores the broad regions when reconstructing high-resolution images due to a limited receptive field. In addition, many deep learning SR methods suffer from intensive computations. To address these problems, we introduce the N-Gram context to the low-level vision with Transformers for the first time. We define N-Gram as neighboring local windows in Swin, which differs from text analysis that views N-Gram as consecutive characters or words. N-Grams interact with each other by sliding-WSA, expanding the regions seen to restore degraded pixels. Using the N-Gram context, we propose NGswin, an efficient SR network with SCDP bottleneck taking multi-scale outputs of the hierarchical encoder. Experimental results show that NGswin achieves competitive performance while maintaining an efficient structure when compared with previous leading methods. Moreover, we also improve other Swin-based SR methods with the N-Gram context, thereby building an enhanced model: SwinIR-NG. Our improved SwinIR-NG out-performs the current best lightweight SR approaches and establishes state-of-the-art results. Codes are available at https://***/rami0205/NGramSwin.

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Improved Handling of Motion Blur in Online Object Detection

Improved Handling of Motion Blur in Online Object Detection

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

作者： Sayed, Mohamed Brostow, Gabriel UCL London England

ISBN: (纸本)9781665445092

We wish to detect specific categories of objects, for online vision systems that will run in the real world. Object detection is already very challenging. It is even harder when the images are blurred, from the camera being in a car or a hand-held phone. Most existing efforts either focused on sharp images, with easy to label ground truth, or they have treated motion blur as one of many generic corruptions. Instead, we focus especially on the details of egomotion induced blur. We explore five classes of remedies, where each targets different potential causes for the performance gap between sharp and blurred images. For example, first deblurring an image changes its human interpretability, but at present, only partly improves object detection. The other four classes of remedies address multi-scale texture, out-of-distribution testing, label generation, and conditioning by blur-type. Surprisingly, we discover that custom label generation aimed at resolving spatial ambiguity, ahead of all others, markedly improves object detection. Also, in contrast to findings from classification, we see a noteworthy boost by conditioning our model on bespoke categories of motion blur. We validate and cross-breed the different remedies experimentally on blurred COCO images and real-world blur datasets, producing an easy and practical favorite model with superior detection rates.

关键词： computer vision Computational modeling Machine vision Object detection Cameras pattern recognition Automobiles

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BiFormer: vision Transformer with Bi-Level Routing Attention

BiFormer: Vision Transformer with Bi-Level Routing Attention

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conference on computer vision and pattern recognition (cvpr)

作者： Lei Zhu Xinjiang Wang Zhanghan Ke Wayne Zhang Rynson Lau City University of Hong Kong SenseTime Research

As the core building block of vision transformers, attention is a powerful tool to capture long-range dependency. However, such power comes at a cost: it incurs a huge computation burden and heavy memory footprint as pairwise token interaction across all spatial locations is computed. A series of works attempt to alleviate this problem by introducing handcrafted and content-agnostic sparsity into attention, such as restricting the attention operation to be inside local windows, axial stripes, or dilated windows. In contrast to these approaches, we propose a novel dynamic sparse attention via bi-level routing to enable a more flexible allocation of computations with content awareness. Specifically, for a query, irrelevant key-value pairs are first filtered out at a coarse region level, and then fine-grained token-to-token attention is applied in the union of remaining candidate regions (i.e., routed regions). We provide a simple yet effective implementation of the proposed bilevel routing attention, which utilizes the sparsity to save both computation and memory while involving only GPU-friendly dense matrix multiplications. Built with the proposed bi-level routing attention, a new general vision transformer, named BiFormer, is then presented. As BiFormer attends to a small subset of relevant tokens in a query adaptive manner without distraction from other irrelevant ones, it enjoys both good performance and high computational efficiency, especially in dense prediction tasks. Empirical results across several computer vision tasks such as image classification, object detection, and semantic segmentation verify the effectiveness of our design. Code is available at https://***/rayleizhu/BiFormer.

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