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DSAC - Differentiable RANSAC for Camera Localization 30

DSAC - Differentiable RANSAC for Camera Localization

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30th ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Brachmann, Eric Krull, Alexander Nowozin, Sebastian Shotton, Jamie Michel, Frank Gumhold, Stefan Rother, Carsten Tech Univ Dresden Dresden Germany Microsoft Coimbatore Tamil Nadu India

ISBN: (纸本)9781538604571

RANSAC is an important algorithm in robust optimization and a central building block for many computer vision applications. In recent years, traditionally hand-crafted pipelines have been replaced by deep learning pipelines, which can be trained in an end-to-end fashion. However, RANSAC has so far not been used as part of such deep learning pipelines, because its hypothesis selection procedure is non-differentiable. In this work, we present two different ways to overcome this limitation. The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w.r.t. to all learnable parameters. We call this approach DSAC, the differentiable counterpart of RANSAC. We apply DSAC to the problem of camera localization, where deep learning has so far failed to improve on traditional approaches. We demonstrate that by directly minimizing the expected loss of the output camera poses, robustly estimated by RANSAC, we achieve an increase in accuracy. In the future, any deep learning pipeline can use DSAC as a robust optimization component(1).

关键词： pipeline Differentiable Cameras piping images computer vision Machine Learning

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Bottleneck Transformers for Visual recognition

Bottleneck Transformers for Visual Recognition

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

作者： Srinivas, Aravind Lin, Tsung-Yi Parmar, Niki Shlens, Jonathon Abbeel, Pieter Vaswani, Ashish Univ Calif Berkeley Berkeley CA 94720 USA Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

We present BoTNet, a conceptually simple yet powerful backbone architecture that incorporates self-attention for multiple computer vision tasks including image classification, object detection and instance segmentation. By just replacing the spatial convolutions with global self-attention in the final three bottleneck blocks of a ResNet and no other changes, our approach improves upon the baselines significantly on instance segmentation and object detection while also reducing the parameters, with minimal overhead in latency. Through the design of BoTNet, we also point out how ResNet bottleneck blocks with self-attention can be viewed as Transformer blocks. Without any bells and whistles, BoTNet achieves 44.4% Mask AP and 49.7% Box AP on the COCO Instance Segmentation benchmark using the Mask R-CNN framework;surpassing the previous best published single model and single scale results of ResNeSt [67] evaluated on the COCO validation set. Finally, we present a simple adaptation of the BoTNet design for image classification, resulting in models that achieve a strong performance of 84.7% top-1 accuracy on the ImageNet benchmark while being up to 1.64x faster in "compute"(1) time than the popular EfficientNet models on TPU-v3 hardware. We hope our simple and effective approach will serve as a strong baseline for future research in self-attention models for vision.(2)

关键词： computer vision Image segmentation Adaptation models Computational modeling Botnet computer architecture Object detection

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LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search

LightTrack: Finding Lightweight Neural Networks for Object T...

引用

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

作者： Yan, Bin Peng, Houwen Wu, Kan Wang, Dong Fu, Jianlong Lu, Huchuan Microsoft Res Asia Beijing Peoples R China Dalian Univ Technol Dalian Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Object tracking has achieved significant progress over the past few years. However, state-of-the-art trackers become increasingly heavy and expensive, which limits their deployments in resource-constrained applications. In this work, we present LightTrack, which uses neural architecture search (NAS) to design more lightweight and efficient object trackers. Comprehensive experiments show that our LightTrack is effective. It can find trackers that achieve superior performance compared to handcrafted SOTA trackers, such as SiamRPN++ [30] and Ocean [56], while using much fewer model Flops and parameters. Moreover, when deployed on resource-constrained mobile chipsets, the discovered trackers run much faster. For example, on Snapdragon 845 Adreno GPU, LightTrack runs 12x faster than Ocean, while using 13x fewer parameters and 38x fewer Flops. Such improvements might narrow the gap between academic models and industrial deployments in object tracking task. LightTrack is released at here.

关键词： computer vision Oceans Neural networks Graphics processing units computer architecture Search problems Real-time systems

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COCAS: A Large-Scale Clothes Changing Person Dataset for Re-identification

COCAS: A Large-Scale Clothes Changing Person Dataset for Re-...

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

作者： Yu, Shijie Li, Shihua Chen, Dapeng Zhao, Rui Yan, Junjie Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit SIAT SenseTime Joint Lab Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Microelect Beijing Peoples R China

ISBN: (纸本)9781728171685

Recent years have witnessed great progress in person reidentification (re-id). Several academic benchmarks such as Market1501, CUHK03 and DukeMTMC play important roles to promote the re-id research. To our best knowledge, all the existing benchmarks assume the same person will have the same clothes. While in real-world scenarios, it is very often for a person to change clothes. To address the clothes changing person re-id problem, we construct a novel large-scale re-id benchmark named ClOthes ChAnging Person Set (COCAS), which provides multiple images of the same identity with different clothes. COCAS totally contains 62,382 body images from 5,266 persons. Based on COCAS, we introduce a new person re-id setting for clothes changing problem, where the query includes both a clothes template and a person image taking another clothes. Moreover, we propose a two-branch network named Biometric-Clothes Network (BC-Net) which can effectively integrate biometric and clothes feature for re-id under our setting. Experiments show that it is feasible for clothes changing re-id with clothes templates.

关键词： Large dataset

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Convex Global 3D Registration with Lagrangian Duality 30

Convex Global 3D Registration with Lagrangian Duality

引用

30th ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Briales, Jesus Gonzalez-Jimenez, Javier Univ Malaga MAPIR UMA Grp Malaga Spain

ISBN: (纸本)9781538604571

The registration of 3D models by a Euclidean transformation is a fundamental task at the core of many application in computer vision. This problem is non- convex due to the presence of rotational constraints, making traditional local optimization methods prone to getting stuck in local minima. This paper addresses finding the globally optimal transformation in various 3D registration problems by a unified formulation that integrates common geometric registration modalities (namely point-to-point, point-to-line and point-to-plane). This formulation renders the optimization problem independent of both the number and nature of the correspondences. The main novelty of our proposal is the introduction of a strengthened Lagrangian dual relaxation for this problem, which surpasses previous similar approaches [32] in effectiveness. In fact, even though with no theoretical guarantees, exhaustive empirical evaluation in both synthetic and real experiments always resulted on a tight relaxation that allowed to recover a guaranteed globally optimal solution by exploiting duality theory. Thus, our approach allows for effectively solving the 3D registration with global optimality guarantees while running at a fraction of the time for the state-of-the-art alternative [34], based on a more computationally intensive Branch and Bound method.

关键词： Registration images computer vision duality theory Branch and bound methods Lagrangian Non-Convex Three Dimensions point to point communication

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Tell Me Where to Look: Guided Attention Inference Network 31

Tell Me Where to Look: Guided Attention Inference Network

引用

31st ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Li, Kunpeng Wu, Ziyan Peng, Kuan-Chuan Ernst, Jan Fu, Yun Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Coll Comp & Informat Sci Boston MA 02115 USA Siemens Corp Technol Princeton NJ USA

ISBN: (纸本)9781538664209

Weakly supervised learning with only coarse labels can obtain visual explanations of deep neural network such as attention maps by back-propagating gradients. These attention maps are then available as priors for tasks such as object localization and semantic segmentation. In one common framework we address three shortcomings of previous approaches in modeling such attention maps: We (1) make attention maps an explicit and natural component of the end-to-end training for the first time, (2) provide self-guidance directly on these maps by exploring supervision from the network itself to improve them, and (3) seamlessly bridge the gap between using weak and extra supervision if available. Despite its simplicity, experiments on the semantic segmentation task demonstrate the effectiveness of our methods. We clearly surpass the state-of-the-art on PASCAL VOC 2012 test and val. sets. Besides, the proposed framework provides a way not only explaining the focus of the learner but also feeding back with direct guidance towards specific tasks. Under mild assumptions our method can also be understood as a plug-in to existing weakly supervised learners to improve their generalization performance.

关键词： Task analysis Training Semantics Visualization Training data computer vision Supervised learning

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PaCa-ViT: Learning Patch-to-Cluster Attention in vision Transformers

PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Tran...

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

作者： Grainger, Ryan Paniagua, Thomas Song, Xi Cuntoor, Naresh Lee, Mun Wai Wu, Tianfu NC State Dept ECE Raleigh NC 27695 USA BlueHalo Arlington VA USA

ISBN: (纸本)9798350301298

vision Transformers (ViTs) are built on the assumption of treating image patches as "visual tokens" and learn patch-to-patch attention. The patch embedding based tokenizer has a semantic gap with respect to its counterpart, the textual tokenizer. The patch-to-patch attention suffers from the quadratic complexity issue, and also makes it nontrivial to explain learned ViTs. To address these issues in ViT, this paper proposes to learn Patch-to-Cluster attention (PaCa) in ViT. Queries in our PaCa-ViT starts with patches, while keys and values are directly based on clustering (with a predefined small number of clusters). The clusters are learned end-to-end, leading to better tokenizers and inducing joint clustering-for-attention and attention-for-clustering for better and interpretable models. The quadratic complexity is relaxed to linear complexity. The proposed PaCa module is used in designing efficient and interpretable ViT backbones and semantic segmentation head networks. In experiments, the proposed methods are tested on ImageNet-1k image classification, MS-COCO object detection and instance segmentation and MIT-ADE20k semantic segmentation. Compared with the prior art, it obtains better performance in all the three benchmarks than the SWin [32] and the PVTs [47, 48] by significant margins in ImageNet-1k and MIT-ADE20k. It is also significantly more efficient than PVT models in MS-COCO and MIT-ADE20k due to the linear complexity. The learned clusters are semantically meaningful. Code and model checkpoints are available at https://***/iVMCL/PaCaViT.

关键词： Deep learning architectures and techniques

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LAMP: Learn A Motion pattern for Few-Shot Video Generation

LAMP: Learn A Motion Pattern for Few-Shot Video Generation

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

作者： Wu, Ruiqi Chen, Liangyu Yang, Tong Guo, Chunle Li, Chongyi Zhang, Xiangyu Nankai Univ CS VCIP Tianjin Peoples R China NKIARI Shenzhen Futian Peoples R China MEGVII Technol Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

In this paper, we present a few-shot text-to-video framework, LAMP, which enables a text-to-image diffusion model to Learn A specific Motion pattern with 8 16 videos on a single GPU. Unlike existing methods, which require a large number of training resources or learn motions that are precisely aligned with template videos, it achieves a trade-off between the degree of generation freedom and the resource costs for model training. Specifically, we design a motion-content decoupled pipeline that uses an off-the-shelf text-to-image model for content generation so that our tuned video diffusion model mainly focuses on motion learning. The well-developed text-to-image techniques can provide visually pleasing and diverse content as generation conditions, which highly improves video quality and generation freedom. To capture the features of temporal dimension, we expand the pre-trained 2D convolution layers of the T2I model to our novel temporal-spatial motion learning layers and modify the attention blocks to the temporal level. Additionally, we develop an effective inference trick, shared-noise sampling, which can improve the stability of videos without computational costs. Our method can also be flexibly applied to other tasks, e.g. real-world image animation and video editing. Extensive experiments demonstrate that LAMP can effectively learn the motion pattern on limited data and generate high-quality videos. The code and models are available at https://rqwu. ***/projects/LAMP.

关键词： Image coding

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OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer

OSRT: Omnidirectional Image Super-Resolution with Distortion...

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

作者： Yu, Fanghua Wang, Xintao Cao, Mingdeng Li, Gen Shan, Ying Dong, Chao Chinese Acad Sci ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol Shenzhen Peoples R China Tencent PCG ARC Shenzhen Peoples R China Univ Tokyo Tokyo Japan Tencent Online Video Platform Technol Shenzhen Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Omnidirectional images (ODIs) have obtained lots of research interest for immersive experiences. Although ODIs require extremely high resolution to capture details of the entire scene, the resolutions of most ODIs are insufficient. Previous methods attempt to solve this issue by image super-resolution (SR) on equirectangular projection (ERP) images. However, they omit geometric properties of ERP in the degradation process, and their models can hardly generalize to real ERP images. In this paper, we propose Fisheye downsampling, which mimics the real-world imaging process and synthesizes more realistic low-resolution samples. Then we design a distortion-aware Transformer (OSRT) to modulate ERP distortions continuously and self-adaptively. Without a cumbersome process, OSRT outperforms previous methods by about 0.2dB on PSNR. Moreover, we propose a convenient data augmentation strategy, which synthesizes pseudo ERP images from plain images. This simple strategy can alleviate the over-fitting problem of large networks and significantly boost the performance of ODISR. Extensive experiments have demonstrated the state-of-the-art performance of our OSRT.

关键词： Low-level vision

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3D-aware Image Synthesis via Learning Structural and Textural Representations

3D-aware Image Synthesis via Learning Structural and Textura...

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

作者： Xu, Yinghao Peng, Sida Yang, Ceyuan Shen, Yujun Zhou, Bolei Chinese Univ Hong Kong Hong Kong Peoples R China Zhejiang Univ Hangzhou Peoples R China Bytedance Inc Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Making generative models 3D -aware bridges the 2D image space and the 3D physical world yet remains challenging. Recent attempts equip a Generative Adversarial Network (GAN) with a Neural Radiance Field (NeRF), which maps 3D coordinates to pixel values, as a 3D prior. However, the implicit function in NeRF has a very local receptive field, making the generator hard to become aware of the global structure. Meanwhile, NeRF is built on volume rendering which can be too costly to produce high resolution results, increasing the optimization difficulty. To alleviate these two problems, we propose a novel framework, termed as VolumeGAN, for high-fidelity 3D -aware image synthesis, through explicitly learning a structural representation and a textural representation. We first learn a feature volume to represent the underlying structure, which is then converted to a feature field using a NeRFlike model. The feature field is further accumulated into a 2D feature map as the textural representation, followed by a neural renderer for appearance synthesis. Such a design enables independent control of the shape and the appearance.

关键词： Solid modeling computer vision Three-dimensional displays Image synthesis Shape Generative adversarial networks Rendering (computer graphics)

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