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Dynamic Cues-Assisted Transformer for Robust Point Cloud Registration

Dynamic Cues-Assisted Transformer for Robust Point Cloud Reg...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Hong Chen Pei Yan Sihe Xiang Yihua Tan Hubei Engineering Research Center of Machine Vision and Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and Technology China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Point Cloud Registration is a critical and challenging task in computer vision. Recent advancements have pre-dominantly embraced a coarse-to-fine matching mechanism, with the key to matching the superpoints located in patches with interframe consistent structures. How-ever, previous methods still face challenges with ambiguous matching, because the interference information aggregated from irrelevant regions may disturb the capture of interframe consistency relations, leading to wrong matches. To address this issue, we propose Dynamic Cues-Assisted Transformer (DCATr). Firstly, the interference from irrelevant regions is greatly reduced by constraining attention to certain cues, i.e., regions with highly correlated structures of potential corresponding superpoints. Secondly, cues-assisted attention is designed to mine the interframe consistency relations, while more attention is assigned to pairs with high consistent confidence in feature aggregation. Finally, a dynamic updating fashion is proposed to facilitate mining richer consistency information, further improving aggregated features' distinctiveness and relieving matching ambiguity. Extensive evaluations on indoor and outdoor standard benchmarks demonstrate that DCATr outperforms all state-of-the-art methods.

关键词： Point cloud compression computer vision Interference Benchmark testing Transformers Feature extraction pattern recognition

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Anycost GANs for Interactive Image Synthesis and Editing

Anycost GANs for Interactive Image Synthesis and Editing

引用

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

作者： Lin, Ji Zhang, Richard Ganz, Frieder Han, Song Zhu, Jun-Yan MIT Cambridge MA 02139 USA Adobe Res San Jose CA 95110 USA CMU Pittsburgh PA USA

ISBN: (纸本)9781665445092

Generative adversarial networks (GANs) have enabled photorealistic image synthesis and editing. However, due to the high computational cost of large-scale generators (e.g., StyleGAN2), it usually takes seconds to see the results of a single edit on edge devices, prohibiting interactive user experience. In this paper, inspired by quick preview features in modern rendering software, we propose Anycost GAN for interactive natural image editing. We train the Anycost GAN to support elastic resolutions and channels for faster image generation at versatile speeds. Running subsets of the full generator produce outputs that are perceptually similar to the full generator, making them a good proxy for quick preview. By using sampling-based multi-resolution training, adaptive-channel training, and a generator-conditioned discriminator, the anycost generator can be evaluated at various configurations while achieving better image quality compared to separately trained models. Furthermore, we develop new encoder training and latent code optimization techniques to encourage consistency between the different sub-generators during image projection. Anycost GAN can be executed at various cost budgets (up to 10x computation reduction) and adapt to a wide range of hardware and latency requirements. When deployed on desktop CPUs and edge devices, our model can provide perceptually similar previews at 6-12x speedup, enabling interactive image editing. The code and demo are publicly available.

关键词： Training Adaptation models Image synthesis Image edge detection Computational modeling Generative adversarial networks Rendering (computer graphics)

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Towards Efficient Tensor Decomposition-Based DNN Model Compression with Optimization Framework

Towards Efficient Tensor Decomposition-Based DNN Model Compr...

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

作者： Yin, Miao Sui, Yang Liao, Siyu Yuan, Bo Rutgers State Univ Dept ECE New Brunswick NJ 08901 USA Amazon Seattle WA USA

ISBN: (纸本)9781665445092

Advanced tensor decomposition, such as tensor train (TT) and tensor ring (TR), has been widely studied for deep neural network (DNN) model compression, especially for recurrent neural networks (RNNs). However, compressing convolutional neural networks (CNNs) using TT/TR always suffers significant accuracy loss. In this paper, we propose a systematic framework for tensor decomposition-based model compression using Alternating Direction Method of Multipliers (ADMM). By formulating TT decompositionbased model compression to an optimization problem with constraints on tensor ranks, we leverage ADMM technique to systemically solve this optimization problem in an iterative way. During this procedure, the entire DNN model is trained in the original structure instead of TT format, but gradually enjoys the desired low tensor rank characteristics. We then decompose this uncompressed model to TT format, and fine-tune it to finally obtain a high-accuracy TTformat DNN model. Our framework is very general, and it works for both CNNs and RNNs, and can be easily modified to fit other tensor decomposition approaches. We evaluate our proposed framework on different DNN models for image classification and video recognition tasks. Experimental results show that our ADMM-based TT-format models demonstrate very high compression performance with high accuracy. Notably, on CIFAR-100, with 2.3x and 2.4xcompression ratios, our models have 1.96% and 2.21% higher top-1 accuracy than the original ResNet-20 and ResNet-32, respectively. For compressing ResNet-18 on ImageNet, our model achieves 2.47x FLOPs reduction without accuracy loss.

关键词： Tensors Image coding Systematics Recurrent neural networks Image recognition Computational modeling Convex functions

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Unveiling the Potential of Structure Preserving for Weakly Supervised Object Localization

Unveiling the Potential of Structure Preserving for Weakly S...

引用

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

作者： Pan, Xingjia Gao, Yingguo Lin, Zhiwen Tang, Fan Dong, Weiming Yuan, Haolei Huang, Feiyue Xu, Changsheng Tencent Youtu Lab Shenzhen Peoples R China Jilin Univ Changchun Jilin Peoples R China Chinese Acad Sci Inst Automat NLPR Beijing Peoples R China UCAS Sch Artificial Intelligence Beijing Peoples R China CASIA LLVis Joint Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

Weakly supervised object localization (WSOL) remains an open problem given the deficiency of finding object extent information using a classification network. Although prior works struggled to localize objects through various spatial regularization strategies, we argue that how to extract object structural information from the trained classification network is neglected. In this paper, we propose a two-stage approach, termed structure-preserving activation (SPA), toward fully leveraging the structure information incorporated in convolutional features for WSOL. First, a restricted activation module (RAM) is designed to alleviate the structuremissing issue caused by the classification network on the basis of the observation that the unbounded classification map and global average pooling layer drive the network to focus only on object parts. Second, we designed a post-process approach, termed self-correlation map generating (SCG) module to obtain structure-preserving localization maps on the basis of the activation maps acquired from the first stage. Specifically, we utilize the high-order self-correlation (HSC) to extract the inherent structural information retained in the learned model and then aggregate HSC of multiple points for precise object localization. Extensive experiments on two publicly available benchmarks including CUB-2002011 and ILSVRC show that the proposed SPA achieves substantial and consistent performance gains compared with baseline approaches. Code and models are available at ***/Panxjia/SPA cvpr2021.

关键词： Location awareness Convolutional codes computer vision Correlation Aggregates Random access memory Benchmark testing

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Dot Distance for Tiny Object Detection in Aerial Images

Dot Distance for Tiny Object Detection in Aerial Images

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

作者： Xu, Chang Wang, Jinwang Yang, Wen Yu, Lei Wuhan Univ Sch Elect Informat Wuhan Peoples R China

ISBN: (纸本)9781665448994

Object detection has achieved great progress with the development of anchor-based and anchor-free detectors. However, the detection of tiny objects is still challenging due to the lack of appearance information. In this paper, we observe that Intersection over Union (IoU), the most widely used metric in object detection, is sensitive to slight offsets between predicted bounding boxes and ground truths when detecting tiny objects. Although some new metrics such as GIoU, DIoU and CIoU are proposed, their performance on tiny object detection is still below the expected level by a large margin. In this paper, we propose a simple but effective new metric called Dot Distance (DotD) for tiny object detection where DotD is defined as normalized Euclidean distance between the center points of two bounding boxes. Extensive experiments on tiny object detection dataset show that anchor-based detectors' performance is highly improved over their baselines with the application of DotD.

关键词： Measurement computer vision conferences Object detection Detectors Euclidean distance pattern recognition

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Variational Transformer Networks for Layout Generation

Variational Transformer Networks for Layout Generation

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

作者： Arroyo, Diego Martin Postels, Janis Tombari, Federico Google Inc Mountain View CA 94043 USA Swiss Fed Inst Technol Zurich Switzerland Tech Univ Munich Munich Germany

ISBN: (纸本)9781665445092

Generative models able to synthesize layouts of different kinds (e.g. documents, user interfaces or furniture arrangements) are a useful tool to aid design processes and as a first step in the generation of synthetic data, among other tasks. We exploit the properties of self-attention layers to capture high level relationships between elements in a layout, and use these as the building blocks of the well-known Variational Autoencoder (VAE) formulation. Our proposed Variational Transformer Network (VTN) is capable of learning margins, alignments and other global design rules without explicit supervision. Layouts sampled from our model have a high degree of resemblance to the training data, while demonstrating appealing diversity. In an extensive evaluation on publicly available benchmarks for different layout types VTNs achieve state-of-the-art diversity and perceptual quality. Additionally, we show the capabilities of this method as part of a document layout detection pipeline.

关键词： computer vision Layout Pipelines Training data User interfaces Tools Transformers

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GenesisTex: Adapting Image Denoising Diffusion to Texture Space

GenesisTex: Adapting Image Denoising Diffusion to Texture Sp...

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conference on computer vision and pattern recognition (cvpr)

作者： Chenjian Gao Boyan Jiang Xinghui Li Yingpeng Zhang Qian Yu School of Software Beihang University R&D Efficiency and Capability Department Tencent IEG

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present GenesisTex, a novel method for synthesizing textures for 3D geometries from text descriptions. GenesisTex adapts the pretrained image diffusion model to texture space by texture space sampling. Specifically, we maintain a latent texture map for each viewpoint, which is updated with predicted noise on the rendering of the corresponding viewpoint. The sampled latent texture maps are then decoded into a final texture map. During the sampling process, we focus on both global and local consistency across multiple viewpoints: global consistency is achieved through the integration of style consistency mechanisms within the noise prediction network, and low-level consistency is achieved by dynamically aligning latent textures. Finally, we apply reference-based inpainting and img2img on denser views for texture refinement. Our approach overcomes the limitations of slow optimization in distillation-based methods and instability in inpainting-based methods. Experiments on meshes from various sources demonstrate that our method surpasses the baseline methods quantitatively and qualitatively.

关键词： Geometry computer vision Three-dimensional displays Noise Rendering (computer graphics) Diffusion models pattern recognition

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NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization

NetAdaptV2: Efficient Neural Architecture Search with Fast S...

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

作者： Yang, Tien-Ju Liao, Yi-Lun Sze, Vivienne MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781665445092

Neural architecture search (NAS) typically consists of three main steps: training a super-network, training and evaluating sampled deep neural networks (DNNs), and training the discovered DNN. Most of the existing efforts speed up some steps at the cost of a significant slowdown of other steps or sacrificing the support of non-differentiable search metrics. The unbalanced reduction in the time spent per step limits the total search time reduction, and the inability to support non-differentiable search metrics limits the performance of discovered DNNs. In this paper, we present NetAdaptV2 with three innovations to better balance the time spent for each step while supporting non-differentiable search metrics. First, we propose channel-level bypass connections that merge network depth and layer width into a single search dimension to reduce the time for training and evaluating sampled DNNs. Second, ordered dropout is proposed to train multiple DNNs in a single forward-backward pass to decrease the time for training a super-network. Third, we propose the multi-layer coordinate descent optimizer that considers the interplay of multiple layers in each iteration of optimization to improve the performance of discovered DNNs while supporting non-differentiable search metrics. With these innovations, NetAdaptV2 reduces the total search time by up to 5.8x on ImageNet and 2.4x on NYU Depth V2, respectively, and discovers DNNs with better accuracy-latency/accuracy-MAC trade-offs than state-of-the-art NAS works. Moreover, the discovered DNN outperforms NAS-discovered MobileNetV3 by 1.8% higher top-1 accuracy with the same latency.(1)

关键词： Training Measurement Deep learning Technological innovation Costs Estimation computer architecture

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Few-Shot Incremental Learning with Continually Evolved Classifiers

Few-Shot Incremental Learning with Continually Evolved Class...

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

作者： Zhang, Chi Song, Nan Lin, Guosheng Zheng, Yun Pan, Pan Xu, Yinghui Nanyang Technol Univ Singapore Singapore Alibaba DAMO Acad Beijing Peoples R China

ISBN: (纸本)9781665445092

Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms that can continually learn new concepts from a few data points, without forgetting knowledge of old classes. The difficulty lies in that limited data from new classes not only lead to significant overfitting issues but also exacerbate the notorious catastrophic forgetting problems. Moreover, as training data come in sequence in FSCIL, the learned classifier can only provide discriminative information in individual sessions, while FSCIL requires all classes to be involved for evaluation. In this paper, we address the FSCIL problem from two aspects. First, we adopt a simple but effective decoupled learning strategy of representations and classifiers that only the classifiers are updated in each incremental session, which avoids knowledge forgetting in the representations. By doing so, we demonstrate that a pre-trained backbone plus a nonparametric class mean classifier can beat state-of-the-art methods. Second, to make the classifiers learned on individual sessions applicable to all classes, we propose a Continually Evolved Classifier (CEC) that employs a graph model to propagate context information between classifiers for adaptation. To enable the learning of CEC, we design a pseudo incremental learning paradigm that episodically constructs a pseudo incremental learning task to optimize the graph parameters by sampling data from the base dataset. Experiments on three popular benchmark datasets, including CIFAR100, miniImageNet, and Caltech-USCD Birds-200-2011 (CUB200), show that our method significantly outperforms the baselines and sets new state-of-the-art results with remarkable advantages.

关键词： computer vision Adaptation models Machine learning algorithms Training data Benchmark testing Power capacitors pattern recognition

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ALGM: Adaptive Local-then-Global Token Merging for Efficient Semantic Segmentation with Plain vision Transformers

ALGM: Adaptive Local-then-Global Token Merging for Efficient...

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conference on computer vision and pattern recognition (cvpr)

作者： Narges Norouzi Svetlana Orlova Daan De Geus Gijs Dubbelman Eindhoven University of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This work presents Adaptive Local-then-Global Merging (ALGM), a token reduction method for semantic segmentation networks that use plain vision Transformers. ALGM merges tokens in two stages: (1) In the first network layer, it merges similar tokens within a small local window and (2) halfway through the network, it merges similar tokens across the entire image. This is motivated by an analysis in which we found that, in those situations, tokens with a high cosine similarity can likely be merged without a drop in segmentation quality. With extensive experiments across multiple datasets and network configurations, we show that ALGM not only significantly improves the throughput by up to 100%, but can also enhance the mean IoU by up to + 1.1, thereby achieving a better trade-off between segmentation quality and efficiency than existing methods. Moreover, our approach is adaptive during inference, meaning that the same model can be used for optimal efficiency or accuracy, depending on the application. Code is available at https://***/ALGM.

关键词： computer vision Adaptation models Codes Adaptive systems Semantic segmentation Computational modeling Merging

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