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BAEFormer: Bi-Directional and Early Interaction Transformers for Bird's Eye View Semantic Segmentation

BAEFormer: Bi-Directional and Early Interaction Transformers...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Cong Pan Yonghao He Junran Peng Qian Zhang Wei Sui Zhaoxiang Zhang National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences School of Future Technology University of Chinese Academy of Sciences Horizon Robotics Huawei Inc. Center for Artificial Intelligence and Robotics HKISI_CAS

Bird's Eye View (BEV) semantic segmentation is a critical task in autonomous driving. However, existing Transformer-based methods confront difficulties in transforming Perspective View (PV) to BEV due to their unidirectional and posterior interaction mechanisms. To address this issue, we propose a novel Bi-directional and Early Interaction Transformers framework named BAEFormer, consisting of (i) an early-interaction PV-BEV pipeline and (ii) a bi-directional cross-attention mechanism. Moreover, we find that the image feature maps' resolution in the cross-attention module has a limited effect on the final performance. Under this critical observation, we propose to enlarge the size of input images and downsample the multi-view image features for cross-interaction, further improving the accuracy while keeping the amount of computation controllable. Our proposed method for BEV semantic segmentation achieves state-of-the-art performance in real-time inference speed on the nuScenes dataset, i.e., 38.9 mIoU at 45 FPS on a single A100 GPU.

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Physics-Guided ISO-Dependent Sensor Noise Modeling for Extreme Low-Light Photography

Physics-Guided ISO-Dependent Sensor Noise Modeling for Extre...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yue Cao Ming Liu Shuai Liu Xiaotao Wang Lei Lei Wangmeng Zuo School of Computer Science and Technology Harbin Institute of Technology China Peng Cheng Laboratory China

Although deep neural networks have achieved astonishing performance in many vision tasks, existing learningbased methods are far inferior to the physical model-based solutions in extreme low-light sensor noise modeling. To tap the potential of learning-based sensor noise modeling, we investigate the noise formation in a typical imaging process and propose a novel physics-guided ISO-dependent sensor noise modeling approach. Specifically, we build a normalizing flow-based framework to represent the complex noise characteristics of CMOS camera sensors. Each component of the noise model is dedicated to a particular kind of noise under the guidance of physical models. Moreover, we take into consideration of the ISO dependence in the noise model, which is not completely considered by the existing learning-based methods. For training the proposed noise model, a new dataset is further collected with paired noisy-clean images, as well as flat-field and bias frames covering a wide range of ISO settings. Compared to existing methods, the proposed noise model is equipped with a flexible structure and accurate modeling capabilities, which is beneficial for better denoising performance in extreme low-light scenes. The dataset and code are available at https://***/happycaoyue/LLD.

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Neural Koopman Pooling: Control-Inspired Temporal Dynamics Encoding for Skeleton-Based Action recognition

Neural Koopman Pooling: Control-Inspired Temporal Dynamics E...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Xinghan Wang Xin Xu Yadong Mu Peking University Peng Cheng Laboratory

Skeleton-based human action recognition is becoming increasingly important in a variety of fields. Most existing works train a CNN or GCN based backbone to extract spatial-temporal features, and use temporal average/max pooling to aggregate the information. However, these pooling methods fail to capture high-order dynamics information. To address the problem, we propose a plug-and-play module called Koopman pooling, which is a parameterized high-order pooling technique based on Koopman theory. The Koopman operator linearizes a non-linear dynamics system, thus providing a way to represent the complex system through the dynamics matrix, which can be used for classification. We also propose an eigenvalue normalization method to encourage the learned dynamics to be non-decaying and stable. Besides, we also show that our Koopman pooling framework can be easily extended to one-shot action recognition when combined with Dynamic Mode Decomposition. The proposed method is evaluated on three benchmark datasets, namely NTU RGB+D 60, 120 and NW-UCLA. Our experiments clearly demonstrate that Koopman pooling significantly improves the performance under both full-dataset and one-shot settings.

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Interpretable Social Anchors for Human Trajectory Forecasting in Crowds

Interpretable Social Anchors for Human Trajectory Forecastin...

引用

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

作者： Kothari, Parth Sifringer, Brian Alahi, Alexandre EPFL VITA Lab CH-1015 Lausanne Switzerland

ISBN: (纸本)9781665445092

Human trajectory forecasting in crowds, at its core, is a sequence prediction problem with specific challenges of capturing inter-sequence dependencies (social interactions) and consequently predicting socially-compliant multi-modal distributions. In recent years, neural network-based methods have been shown to outperform hand-crafted methods on distance-based metrics. However, these data-driven methods still suffer from one crucial limitation: lack of interpretability. To overcome this limitation, we leverage the power of discrete choice models to learn interpretable rule-based intents, and subsequently utilise the expressibility of neural networks to model scene-specific residual. Extensive experimentation on the interaction-centric benchmark TrajNet++ demonstrates the effectiveness of our proposed architecture to explain its predictions without compromising the accuracy.

关键词： Measurement computer vision Neural networks Knowledge based systems Predictive models Data models Trajectory

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Limitations of Post-Hoc Feature Alignment for Robustness

Limitations of Post-Hoc Feature Alignment for Robustness

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

作者： Burns, Collin Steinhardt, Jacob Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781665445092

Feature alignment is an approach to improving robustness to distribution shift that matches the distribution of feature activations between the training distribution and test distribution. A particularly simple but effective approach to feature alignment involves aligning the batch normalization statistics between the two distributions in a trained neural network. This technique has received renewed interest lately because of its impressive performance on robustness benchmarks. However, when and why this method works is not well understood. We investigate the approach in more detail and identify several limitations. We show that it only significantly helps with a narrow set of distribution shifts and we identify several settings in which it even degrades performance. We also explain why these limitations arise by pinpointing why this approach can be so effective in the first place. Our findings call into question the utility of this approach and Unsupervised Domain Adaptation more broadly for improving robustness in practice.

关键词： Training Knowledge engineering computer vision Neural networks Buildings Benchmark testing Robustness

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Repopulating Street Scenes

Repopulating Street Scenes

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

作者： Wang, Yifan Liu, Andrew Tucker, Richard Wu, Jiajun Curless, Brian L. Seitz, Steven M. Snavely, Noah Univ Washington Seattle WA 98195 USA Google Res Mountain View CA USA Stanford Univ Stanford CA 94305 USA Google Mountain View CA USA

ISBN: (纸本)9781665445092

We present a framework for automatically reconfiguring images of street scenes by populating, depopulating, or repopulating them with objects such as pedestrians or vehicles. Applications of this method include anonymizing images to enhance privacy, generating data augmentations for perception tasks like autonomous driving, and composing scenes to achieve a certain ambiance, such as empty streets in the early morning. At a technical level, our work has three primary contributions: (1) a method for clearing images of objects, (2) a method for estimating sun direction from a single image, and (3) a way to compose objects in scenes that respects scene geometry and illumination. Each component is learned from data with minimal ground truth annotations, by making creative use of large-numbers of short image bursts of street scenes. We demonstrate convincing results on a range of street scenes and illustrate potential applications.

关键词： Geometry Data privacy computer vision Annotations Lighting pattern recognition Task analysis

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Visual Room Rearrangement

Visual Room Rearrangement

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

作者： Weihs, Luca Deitke, Matt Kembhavi, Aniruddha Mottaghi, Roozbeh Prior Allen Inst AI Seattle WA 98103 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781665445092

There has been a significant recent progress in the field of Embodied AI with researchers developing models and algorithms enabling embodied agents to navigate and interact within completely unseen environments. In this paper, we propose a new dataset and baseline models for the task of Rearrangement. We particularly focus on the task of Room Rearrangement: an agent begins by exploring a room and recording objects' initial configurations. We then remove the agent and change the poses and states (e.g., open/closed) of some objects in the room. The agent must restore the initial configurations of all objects in the room. Our dataset, named RoomR, includes 6,000 distinct rearrangement settings involving 72 different object types in 120 scenes. Our experiments show that solving this challenging interactive task that involves navigation and object interaction is beyond the capabilities of the current state-of-the-art techniques for embodied tasks and we are still very far from achieving perfect performance on these types of tasks.

关键词： Visualization computer vision Navigation pattern recognition Task analysis Artificial intelligence

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Cross Modal Focal Loss for RGBD Face Anti-Spoofing

Cross Modal Focal Loss for RGBD Face Anti-Spoofing

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

作者： George, Anjith Marcel, Sebastien Idiap Res Inst Rue Marconi 19 CH-1920 Martigny Switzerland

ISBN: (纸本)9781665445092

Automatic methods for detecting presentation attacks are essential to ensure the reliable use of facial recognition technology. Most of the methods available in the literature for presentation attack detection (PAD) fails in generalizing to unseen attacks. In recent years, multi-channel methods have been proposed to improve the robustness of PAD systems. Often, only a limited amount of data is available for additional channels, which limits the effectiveness of these methods. In this work, we present a new framework for PAD that uses RGB and depth channels together with a novel loss function. The new architecture uses complementary information from the two modalities while reducing the impact of ovetfitting. Essentially, a cross-modal focal loss function is proposed to modulate the loss contribution of each channel as a function of the confidence of individual channels. Extensive evaluations in two publicly available datasets demonstrate the effectiveness of the proposed approach.

关键词： computer vision Face recognition Computational modeling computer architecture Robustness

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Roof-GAN: Learning to Generate Roof Geometry and Relations for Residential Houses

Roof-GAN: Learning to Generate Roof Geometry and Relations f...

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

作者： Qian, Yiming Zhang, Hao Furukawa, Yasutaka Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

This paper presents Roof-GAN, a novel generative adversarial network that generates structured geometry of residential roof structures as a set of roof primitives and their relationships. Given the number of primitives, the generator produces a structured roof model as a graph, which consists of 1) primitive geometry as raster images at each node, encoding facet segmentation and angles;2) inter-primitive colinear/coplanar relationships at each edge;and 3) primitive geometry in a vector format at each node, generated by a novel differentiable vectorizer while enforcing the relationships. The discriminator is trained to assess the primitive raster geometry, the primitive relationships, and the primitive vector geometry in a fully end-to-end architecture. Qualitative and quantitative evaluations demonstrate the effectiveness of our approach in generating diverse and realistic roof models over the competing methods with a novel metric proposed in this paper for the task of structured geometry generation.

关键词： Geometry Measurement Image segmentation computer vision Image coding Image edge detection Generative adversarial networks

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Few-Shot Transformation of Common Actions into Time and Space

Few-Shot Transformation of Common Actions into Time and Spac...

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

作者： Yang, Pengwan Mettes, Pascal Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781665445092

This paper introduces the task of few-shot common action localization in time and space. Given a few trimmed support videos containing the same but unknown action, we strive for spatio-temporal localization of that action in a long untrimmed query video. We do not require any class labels, interval bounds, or bounding boxes. To address this challenging task, we introduce a novel few-shot transformer architecture with a dedicated encoder-decoder structure optimized for joint commonality learning and localization prediction, without the need for proposals. Experiments on reorganizations of the AVA and UCF101-24 datasets show the effectiveness of our approach for few-shot common action localization, even when the support videos are noisy. Although we are not specifically designed for common localization in time only, we also compare favorably against the fewshot and one-shot state-of-the-art in this setting. Lastly, we demonstrate that the few-shot transformer is easily extended to common action localization per pixel.

关键词： Location awareness computer vision computer architecture Transformers pattern recognition Noise measurement Proposals

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