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Unsupervised Disentanglement of Linear-Encoded Facial Semantics

Unsupervised Disentanglement of Linear-Encoded Facial Semant...

引用

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

作者： Zheng, Yutong Huang, Yu-Kai Tao, Ran Shen, Zhiqiang Savvides, Marios Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665445092

We propose a method to disentangle linear-encoded facial semantics from StyleGAN without external supervision. The method derives from linear regression and sparse representation learning concepts to make the disentangled latent representations easily interpreted as well. We start by coupling StyleGAN with a stabilized 3D deformable facial reconstruction method to decompose single-view GAN generations into multiple semantics. Latent representations are then extracted to capture interpretable facial semantics. In this work, we make it possible to get rid of labels for disentangling meaningful facial semantics. Also, we demonstrate that the guided extrapolation along the disentangled representations can help with data augmentation, which sheds light on handling unbalanced data. Finally, we provide an analysis of our learned localized facial representations and illustrate that the semantic information is encoded, which surprisingly complies with human intuition. The overall unsupervised design brings more flexibility to representation learning in the wild.

关键词： Couplings Extrapolation computer vision Three-dimensional displays Semantics Linear regression Reconstruction algorithms

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Multimodal Motion Prediction with Stacked Transformers

Multimodal Motion Prediction with Stacked Transformers

引用

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

作者： Liu, Yicheng Zhang, Jinghuai Fang, Liangji Jiang, Qinhong Zhou, Bolei Chinese Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety of autonomous driving. Recent motion prediction approaches attempt to achieve such multimodal motion prediction by implicitly regularizing the feature or explicitly generating multiple candidate proposals. However, it remains challenging since the latent features may concentrate on the most frequent mode of the data while the proposal-based methods depend largely on the prior knowledge to generate and select the proposals. In this work, we propose a novel transformer framework for multimodal motion prediction, termed as mmTransformer. A novel network architecture based on stacked transformers is designed to model the multimodality at feature level with a set of fixed independent proposals. A region-based training strategy is then developed to induce the multimodality of the generated proposals. Experiments on Argoverse dataset show that the proposed model achieves the state-of-the-art performance on motion prediction, substantially improving the diversity and the accuracy of the predicted trajectories. Demo video and code are available at https://***/mmTransformer.

关键词： Training computer vision Computational modeling Predictive models Network architecture Transformers Trajectory

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Saliency-Guided Image Translation

Saliency-Guided Image Translation

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

作者： Jiang, Lai Xu, Mai Wang, Xiaofei Sigal, Leonid Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China Univ British Columbia Dept Comp Sci Vancouver BC Canada

ISBN: (纸本)9781665445092

In this paper, we propose a novel task for saliency-guided image translation, with the goal of image-to-image translation conditioned on the user specified saliency map. To address this problem, we develop a novel Generative Adversarial Network (GAN)-based model, called SalG-GAN. Given the original image and target saliency map, SalG-GAN can generate a translated image that satisfies the target saliency map. In SalG-GAN, a disentangled representation framework is proposed to encourage the model to learn diverse translations for the same target saliency condition. A saliency-based attention module is introduced as a special attention mechanism for facilitating the developed structures of saliency-guided generator, saliency cue encoder and saliency-guided global and local discriminators. Furthermore, we build a synthetic dataset and a real-world dataset with labeled visual attention for training and evaluating our SalG-GAN. The experimental results over both datasets verify the effectiveness of our model for saliency-guided image translation.

关键词： Training Visualization computer vision Generative adversarial networks Generators pattern recognition Task analysis

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Diffusion-based Generation, Optimization, and Planning in 3D Scenes

Diffusion-based Generation, Optimization, and Planning in 3D...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Siyuan Huang Zan Wang Puhao Li Baoxiong Jia Tengyu Liu Yixin Zhu Wei Liang Song-Chun Zhu BIGAI National Key Laboratory of General Artificial Intelligence School of Computer Science & Technology Beijing Institute of Technology Dept. of Automation Tsinghua University Institute for AI Peking University Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing

We introduce the SceneDiffuser, a conditional generative model for 3D scene understanding. SceneDiffuser provides a unified model for solving scene-conditioned generation, optimization, and planning. In contrast to prior work, SceneDiffuser is intrinsically scene-aware, physics-based, and goal-oriented. With an iterative sampling strategy, SceneDiffuser jointly formulates the scene-aware generation, physics-based optimization, and goal-oriented planning via a diffusion-based denoising process in a fully differentiable fashion. Such a design alleviates the discrepancies among different modules and the posterior collapse of previous scene-conditioned generative models. We evaluate the SceneDiffuser on various 3D scene understanding tasks, including human pose and motion generation, dexterous grasp generation, path planning for 3D navigation, and motion planning for robot arms. The results show significant improvements compared with previous models, demonstrating the tremendous potential of the SceneDiffuser for the broad community of 3D scene understanding.

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Confluent Vessel Trees with Accurate Bifurcations

Confluent Vessel Trees with Accurate Bifurcations

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

作者： Zhang, Zhongwen Marin, Dmitrii Drangova, Maria Boykov, Yuri Univ Waterloo Waterloo ON Canada Vector Res Inst Toronto ON Canada Robarts Res London ON Canada

ISBN: (纸本)9781665445092

We are interested in unsupervised reconstruction of complex near-capillary vasculature with thousands of bifurcations where supervision and learning are infeasible. Unsupervised methods can use many structural constraints, e.g. topology, geometry, physics. Common techniques use variants of MST on geodesic tubular graphs minimizing symmetric pairwise costs, i.e. distances. We show limitations of such standard undirected tubular graphs producing typical errors at bifurcations where flow\directedness" is critical. We introduce a new general concept of confluence for continuous oriented curves forming vessel trees and show how to enforce it on discrete tubular graphs. While confluence is a high-order property, we present an efficient practical algorithm for reconstructing confluent vessel trees using minimum arborescence on a directed graph enforcing confluence via simple flow-extrapolating arc construction. Empirical tests on large near-capillary sub-voxel vasculature volumes demonstrate significantly improved reconstruction accuracy at bifurcations. Our code has also been made publicly available(1).

关键词： Geometry computer vision Costs Codes Directed graphs Bifurcation Topology

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Towards Robust Classification Model by Counterfactual and Invariant Data Generation

Towards Robust Classification Model by Counterfactual and In...

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

作者： Chang, Chun-Hao Adam, George Alexandru Goldenberg, Anna Univ Toronto Hosp Sick Children Vector Inst Toronto ON Canada

ISBN: (纸本)9781665445092

Despite the success of machine learning applications in science, industry, and society in general, many approaches are known to be non-robust, often relying on spurious correlations to make predictions. Spuriousness occurs when some features correlate with labels but are not causal;relying on such features prevents models from generalizing to unseen environments where such correlations break. In this work, we focus on image classification and propose two data generation processes to reduce spuriousness. Given human annotations of the subset of the features responsible (causal) for the labels (e.g. bounding boxes), we modify this causal set to generate a surrogate image that no longer has the same label (i.e. a counterfactual image). We also alter non-causal features to generate images still recognized as the original labels, which helps to learn a model invariant to these features. In several challenging datasets, our data generations outperform state-of-the-art methods in accuracy when spurious correlations break, and increase the saliency focus on causal features providing better explanations.

关键词： Industries computer vision Correlation Image recognition Annotations Computational modeling Machine learning

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BEVHeight: A Robust Framework for vision-based Roadside 3D Object Detection

BEVHeight: A Robust Framework for Vision-based Roadside 3D O...

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conference on computer vision and pattern recognition (cvpr)

作者： Lei Yang Kaicheng Yu Tao Tang Jun Li Kun Yuan Li Wang Xinyu Zhang Peng Chen State Key Laboratory of Automotive Safety and Energy Tsinghua University Autonomous Driving Lab Alibaba Group Shenzhen Campus Sun Yat-sen University Center for Machine Learning Research Peking University

While most recent autonomous driving system focuses on developing perception methods on ego-vehicle sensors, people tend to overlook an alternative approach to leverage intelligent roadside cameras to extend the perception ability beyond the visual range. We discover that the state-of-the-art vision-centric bird's eye view detection methods have inferior performances on roadside cameras. This is because these methods mainly focus on recovering the depth regarding the camera center, where the depth difference between the car and the ground quickly shrinks while the distance increases. In this paper, we propose a simple yet effective approach, dubbed BEVHeight, to address this issue. In essence, instead of predicting the pixel-wise depth, we regress the height to the ground to achieve a distance-agnostic formulation to ease the optimization process of camera-only perception methods. On popular 3D detection benchmarks of roadside cameras, our method surpasses all previous vision-centric methods by a significant margin. The code is available at https://***/ADLab-AutoDrive/BEVHeight.

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ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformer

ProxyFormer: Proxy Alignment Assisted Point Cloud Completion...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Shanshan Li Pan Gao Xiaoyang Tan Mingqiang Wei College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics

Problems such as equipment defects or limited view-points will lead the captured point clouds to be incomplete. Therefore, recovering the complete point clouds from the partial ones plays an vital role in many practical tasks, and one of the keys lies in the prediction of the missing part. In this paper, we propose a novel point cloud completion approach namely ProxyFormer that divides point clouds into existing (input) and missing (to be predicted) parts and each part communicates information through its proxies. Specifically, we fuse information into point proxy via feature and position extractor, and generate features for missing point proxies from the features of existing point proxies. Then, in order to better perceive the position of missing points, we design a missing part sensitive transformer, which converts random normal distribution into reasonable position information, and uses proxy alignment to refine the missing proxies. It makes the predicted point proxies more sensitive to the features and positions of the missing part, and thus makes these proxies more suitable for subsequent coarse-to-fine processes. Experimental results show that our method outperforms state-of-the-art completion networks on several benchmark datasets and has the fastest inference speed.

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Exemplar-Based Open-Set Panoptic Segmentation Network

Exemplar-Based Open-Set Panoptic Segmentation Network

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

作者： Hwang, Jaedong Oh, Seoung Wug Lee, Joon-Young Han, Bohyung Seoul Natl Univ ECE Seoul South Korea Seoul Natl Univ ASRI Seoul South Korea Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We extend panoptic segmentation to the open-world and introduce an open-set panoptic segmentation (OPS) task. This task requires performing panoptic segmentation for not only known classes but also unknown ones that have not been acknowledged during training. We investigate the practical challenges of the task and construct a benchmark on top of an existing dataset, COCO. In addition, we propose a novel exemplar-based open-set panoptic segmentation network (EOPSN) inspired by exemplar theory. Our approach identifies a new class based on exemplars, which are identified by clustering and employed as pseudoground-truths. The size of each class increases by mining new exemplars based on the similarities to the existing ones associated with the class. We evaluate EOPSN on the proposed benchmark and demonstrate the effectiveness of our proposals. The primary goal of our work is to draw the attention of the community to the recognition in the open-world scenarios. The implementation of our algorithm is available on the project webpage(1).

关键词： Training Image segmentation computer vision Solid modeling Benchmark testing Solids pattern recognition

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RIFormer: Keep Your vision Backbone Effective But Removing Token Mixer

RIFormer: Keep Your Vision Backbone Effective But Removing T...

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conference on computer vision and pattern recognition (cvpr)

作者： Jiahao Wang Songyang Zhang Yong Liu Taiqiang Wu Yujiu Yang Xihui Liu Kai Chen Ping Luo Dahua Lin Shanghai AI Laboratory The University of HongKong Tsinghua Shenzhen International Graduate School

This paper studies how to keep a vision backbone effective while removing token mixers in its basic building blocks. Token mixers, as self-attention for vision transformers (ViTs), are intended to perform information communication between different spatial tokens but suffer from considerable computational cost and latency. However, directly removing them will lead to an incomplete model structure prior, and thus brings a significant accuracy drop. To this end, we first develop an RepIdentityFormer base on the re-parameterizing idea, to study the token mixer free model architecture. And we then explore the improved learning paradigm to break the limitation of simple token mixer free backbone, and summarize the empirical practice into 5 guidelines. Equipped with the proposed optimization strategy, we are able to build an extremely simple vision backbone with encouraging performance, while enjoying the high efficiency during inference. Extensive experiments and ablative analysis also demonstrate that the inductive bias of network architecture, can be incorporated into simple network structure with appropriate optimization strategy. We hope this work can serve as a starting point for the exploration of optimization-driven efficient network design.

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