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ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matching

ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Networ...

引用

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

作者： Shi, Yan Cai, Jun-Xiong Shavit, Yoli Mu, Tai-Jiang Feng, Wensen Zhang, Kai Tsinghua Univ Beijing Peoples R China Bar Ilan Univ Fac Engn Ramat Gan Israel Huawei Technol Shenzhen Guangdong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Graph Neural Networks (GNNs) with attention have been successfully applied for learning visual feature matching. However, current methods learn with complete graphs, resulting in a quadratic complexity in the number of features. Motivated by a prior observation that self- and cross-attention matrices converge to a sparse representation, we propose ClusterGNN, an attentional GNN architecture which operates on clusters for learning the feature matching task. Using a progressive clustering module we adaptively divide keypoints into different subgraphs to reduce redundant connectivity, and employ a coarse-to-fine paradigm for mitigating miss-classification within images. Our approach yields a 59.7% reduction in runtime and 58.4% reduction in memory consumption for dense detection, compared to current state-of-the-art GNN-based matching, while achieving a competitive performance on various computer vision tasks.

关键词： computer vision Visualization Runtime Memory management Graph neural networks Complexity theory pattern recognition

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Tencent-MVSE: A Large-Scale Benchmark Dataset for Multi-Modal Video Similarity Evaluation

Tencent-MVSE: A Large-Scale Benchmark Dataset for Multi-Moda...

引用

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

作者： Zeng, Zhaoyang Luo, Yongsheng Liu, Zhenhua Rao, Fengyun Li, Dian Guo, Weidong Wen, Zhen Tencent QQ Browser Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Multi-modal video similarity evaluation is important for video recommendation systems such as video deduplication, relevance matching, ranking, and diversity control. However, there still lacks a benchmark dataset that can support supervised training and accurate evaluation. In this paper, we propose the Tencent-MVSE dataset, which is the first benchmark dataset for the multi-modal video similarity evaluation task. The Tencent-MVSE dataset contains video pairs similarity annotations, and diverse metadata including Chinese title, automatic speech recognition (ASR) text, as well as human-annotated categories/tags. We provide a simple baseline with a multi-modal Transformer architecture to perform supervised multi-modal video similarity evaluation. We also explore pre-training strategies to make use of the unpaired data. The whole dataset as well as our baseline will be released to promote the development of the multi-modal video similarity evaluation. The dataset has been released in https://***/.

关键词： Training computer vision Annotations computer architecture Benchmark testing Transformers pattern recognition

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Learning with Neighbor Consistency for Noisy Labels

Learning with Neighbor Consistency for Noisy Labels

引用

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

作者： Iscen, Ahmet Valmadre, Jack Arnab, Anurag Schmid, Cordelia Google Res Meylan France Univ Adelaide AIML Adelaide SA Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent advances in deep learning have relied on large, labelled datasets to train high-capacity models. However, collecting large datasets in a time- and cost-efficient manner often results in label noise. We present a method for learning from noisy labels that leverages similarities between training examples in feature space, encouraging the prediction of each example to be similar to its nearest neighbours. Compared to training algorithms that use multiple models or distinct stages, our approach takes the form of a simple, additional regularization term. It can be interpreted as an inductive version of the classical, transductive label propagation algorithm. We thoroughly evaluate our method on datasets evaluating both synthetic (CIFAR-10, CIFAR-100) and realistic (mini-Webvision, Web vision, Clothing1M, mini-ImageNet-Red) noise, and achieve competitive or state-of-the-art accuracies across all of them.

关键词： Training Deep learning computer vision Stochastic processes computer architecture Semisupervised learning Prediction algorithms

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Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness

Give Me Your Attention: Dot-Product Attention Considered Har...

引用

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

作者： Lovisotto, Giulio Finnie, Nicole Munoz, Mauricio Mummadi, Chaithanya Kumar Metzen, Jan Hendrik Univ Oxford Oxford England Bosch Ctr Artificial Intelligence Stuttgart Germany Univ Freiburg Freiburg Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Neural architectures based on attention such as vision transformers are revolutionizing image recognition. Their main benefit is that attention allows reasoning about all parts of a scene jointly. In this paper, we show how the global reasoning of (scaled) dot-product attention can be the source of a major vulnerability when confronted with adversarial patch attacks. We provide a theoretical understanding of this vulnerability and relate it to an adversary's ability to misdirect the attention of all queries to a single key token under the control of the adversarial patch. We propose novel adversarial objectives for crafting adversarial patches which target this vulnerability explicitly. We show the effectiveness of the proposed patch attacks on popular image classification (ViTs and DeiTs) and object detection models (DETR). We find that adversarial patches occupying 0.5% of the input can lead to robust accuracies as low as 0% for VIT on ImageNet, and reduce the mAP of DOR on MS COCO to less than 3%.

关键词： computer vision Image recognition Object detection Transformer cores Transformers Robustness Cognition

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Per-Clip Video Object Segmentation

Per-Clip Video Object Segmentation

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

作者： Park, Kwanyong Woo, Sanghyun Oh, Seoung Wug Kweon, In So Lee, Joon-Young Korea Adv Inst Sci & Technol Daejeon South Korea Adobe Res San Jose CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, memory-based approaches show promising results on semi-supervised video object segmentation. These methods predict object masks frame-by-frame with the help of frequently updated memory of the previous mask. Different from this per-frame inference, we investigate an alternative perspective by treating video object segmentation as clip-wise mask propagation. In this per-clip inference scheme, we update the memory with an interval and simultaneously process a set of consecutive frames (i.e. clip) between the memory updates. The scheme provides two potential benefits: accuracy gain by clip-level optimization and efficiency gain by parallel computation of multiple frames. To this end, we propose a new method tailored for the perclip inference. Specifically, we first introduce a clip-wise operation to refine the features based on intra-clip correlation. In addition, we employ a progressive matching mechanism for efficient information-passing within a clip. With the synergy of two modules and a newly proposed perclip based training, our network achieves state-of-the-art performance on Youtube-VOS 2018/2019 val (84.6% and 84.6%) and DAVIS 2016/2017 val (91.9% and 86.1%). Furthermore, our model shows a great speed-accuracy tradeoff with varying memory update intervals, which leads to huge flexibility.

关键词： Training computer vision Correlation Tracking Shape Memory management Object segmentation

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Proceedings - 2019 ieee/CVF conference on computer vision and pattern recognition, cvpr 2019

Proceedings - 2019 IEEE/CVF Conference on Computer Vision an...

引用

32nd ieee/CVF conference on computer vision and pattern recognition, cvpr 2019

ISBN: (纸本)9781728132938

The proceedings contain 1294 papers. The topics discussed include: finding task-relevant features for few-shot learning by category traversal;edge-labeling graph neural network for few-shot learning;generating classification weights with GNN denoising autoencoders for few-shot learning;kervolutional neural networks;why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem;on the structural sensitivity of deep convolutional networks to the directions of fourier basis functions;hardness-aware deep metric learning;auto-deeplab: hierarchical neural architecture search for semantic image segmentation;striking the right balance with uncertainty;and SDRSAC: semidefinite-based randomized approach for robust point cloud registration without correspondences.

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Fine-tuning Image Transformers using Learnable Memory

Fine-tuning Image Transformers using Learnable Memory

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

作者： Sandler, Mark Zhmoginov, Andrey Vladymyrov, Max Jackson, Andrew Google Inc Mountain View CA 94043 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper we propose augmenting vision Transformer models with learnable memory tokens. Our approach allows the model to adapt to new tasks, using few parameters, while optionally preserving its capabilities on previously learned tasks. At each layer we introduce a set of learnable embedding vectors that provide contextual information useful for specific datasets. We call these "memory tokens". We show that augmenting a model with just a handful of such tokens per layer significantly improves accuracy when compared to conventional head-only fine-tuning, and performs only slightly below the significantly more expensive full fine-tuning. We then propose an attention-masking approach that enables extension to new downstream tasks, with a computation reuse. In this setup in addition to being parameters efficient, models can execute both old and new tasks as a part of single inference at a small incremental cost.

关键词： Deep learning Adaptation models computer vision Costs Computational modeling Memory management Transformers

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Differentiable Stereopsis: Meshes from multiple views using differentiable rendering

Differentiable Stereopsis: Meshes from multiple views using ...

引用

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

作者： Goel, Shubham Gkioxari, Georgia Malik, Jitendra Univ Calif Berkeley Berkeley CA 94720 USA Meta AI Menlo Pk CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose Differentiable Stereopsis, a multi-view stereo approach that reconstructs shape and texture from few input views and noisy cameras. We pair traditional stereopsis and modern differentiable rendering to build an end-to-end model which predicts textured 3D meshes of objects with varying topologies and shape. We frame stereopsis as an optimization problem and simultaneously update shape and cameras via simple gradient descent. We run an extensive quantitative analysis and compare to traditional multi-view stereo techniques and state-of-the-art learning based methods. We show compelling reconstructions on challenging real-world scenes and for an abundance of object types with complex shape, topology and texture.(1)

关键词： Solid modeling Three-dimensional displays Shape Statistical analysis Predictive models Rendering (computer graphics) Cameras

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NeurMiPs: Neural Mixture of Planar Experts for View Synthesis

NeurMiPs: Neural Mixture of Planar Experts for View Synthesi...

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

作者： Lin, Zhi-Hao Ma, Wei-Chiu Hsu, Hao-Yu Wang, Yu-Chiang Frank Wang, Shenlong Univ Illinois Urbana IL 61801 USA Natl Taiwan Univ New Taipei Taiwan MIT Cambridge MA 02139 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present Neural Mixtures of Planar Experts (NeurMiPs), a novel planar-based scene representation for modeling geometry and appearance. NeurMiPs leverages a collection of local planar experts in 3D space as the scene representation. Each planar expert consists of the parameters of the local rectangular shape representing geometry and a neural radiance field modeling the color and opacity. We render novel views by calculating ray-plane intersections and composite output colors and densities at intersected points to the image. NeurMiPs blends the efficiency of explicit mesh rendering and flexibility of the neural radiance field. Experiments demonstrate superior performance and speed of our proposed method, compared to other 3D representations in novel view synthesis.

关键词： Geometry computer vision Three-dimensional displays Shape Image color analysis Pipelines Rendering (computer graphics)

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Masked Autoencoders Are Scalable vision Learners

Masked Autoencoders Are Scalable Vision Learners

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

作者： He, Kaiming Chen, Xinlei Xie, Saining Li, Yanghao Dollar, Piotr Girshick, Ross Facebook AI Res FAIR New York NY 10003 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. It is based on two core designs. First, we develop an asymmetric encoder-decoder architecture, with an encoder that operates only on the visible subset of patches (without mask tokens), along with a lightweight decoder that reconstructs the original image from the latent representation and mask tokens. Second, we find that masking a high proportion of the input image, e.g., 75%, yields a nontrivial and meaningful self-supervisory task. Coupling these two designs enables us to train large models efficiently and effectively: we accelerate training (by 3x or more) and improve accuracy. Our scalable approach allows for learning high-capacity models that generalize well: e.g., a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-IK data. Transfer performance in downstream tasks outperforms supervised pretraining and shows promising scaling behavior.

关键词： Training Couplings computer vision Computational modeling computer architecture Data models pattern recognition

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