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NomMer: Nominate Synergistic Context in vision Transformer for Visual recognition

NomMer: Nominate Synergistic Context in Vision Transformer f...

引用

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

作者： Liu, Hao Jiang, Xinghua Li, Xin Bao, Zhimin Jiang, Deqiang Ren, Bo Tencent YouTu Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, vision Transformers (ViT), with the self-attention (SA) as the de facto ingredients, have demonstrated great potential in the computer vision community. For the sake of trade-off between efficiency and performance, a group of works merely perform SA operation within local patches, whereas the global contextual information is abandoned, which would be indispensable for visual recognition tasks. To solve the issue, the subsequent global-local ViTs take a stab at marrying local SA with global one in parallel or alternative way in the model. Nevertheless, the exhaustively combined local and global context may exist redundancy for various visual data, and the receptive field within each layer is fixed. Alternatively, a more graceful way is that global and local context can adaptively contribute per se to accommodate different visual data. To achieve this goal, we in this paper propose a novel ViT architecture, termed NomMer, which can dynamically Nominate the synergistic global-local context in vision transforMer. By investigating the working pattern of NomMer, we further explore what context information is focused. Beneficial from this "dynamic nomination" mechanism, without bells and whistles, the NomMer can not only achieve 84.5% Top-1 classification accuracy on ImageNet with only 73M parameters, but also show promising performance on dense prediction tasks, i.e., object detection and semantic segmentation. The code and models are publicly available at https://***/TencentYoutuResearch/VisualrecognitionNomMer.

关键词： Visualization computer vision Image segmentation Semantics Redundancy computer architecture Object detection

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Performance-Aware Mutual Knowledge Distillation for Improving Neural Architecture Search

Performance-Aware Mutual Knowledge Distillation for Improvin...

引用

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

作者： Xie, Pengtao Du, Xuefeng Univ Calif San Diego La Jolla CA 92093 USA Univ Wisconsin Madison Madison WI USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Knowledge distillation has shown great effectiveness for improving neural architecture search (NAS). Mutual knowledge distillation (MKD), where a group of models mutually generate knowledge to train each other, has achieved promising results in many applications. In existing MKD methods, mutual knowledge distillation is performed between models without scrutiny: a worse-performing model is allowed to generate knowledge to train a better-performing model, which may lead to collective failures. To address this problem, we propose a performance-aware MKD (PAMKD) approach for NAS, where knowledge generated by model A is allowed to train model B only if the performance of A is better than B. We propose a three-level optimization framework to formulate PAMKD, where three learning stages are performed end-to-end: 1) each model trains an initial model independently;2) the initial models are evaluated on a validation set and better-performing models generate knowledge to train worse-performing models;3) architectures are updated by minimizing a validation loss. Experimental results on a variety of datasets demonstrate that our method is effective.

关键词： computer vision Computational modeling computer architecture pattern recognition Optimization

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The DEVIL is in the Details: A Diagnostic Evaluation Benchmark for Video Inpainting

The DEVIL is in the Details: A Diagnostic Evaluation Benchma...

引用

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

作者： Szeto, Ryan Corso, Jason J. Univ Michigan Ann Arbor MI 48109 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Quantitative evaluation has increased dramatically among recent video inpainting work, but the video and mask content used to gauge performance has received relatively little attention. Although attributes such as camera and background scene motion inherently change the difficulty of the task and affect methods differently, existing evaluation schemes fail to control for them, thereby providing minimal insight into inpainting failure modes. To address this gap, we propose the Diagnostic Evaluation of Video Inpainting on Landscapes (DEVIL) benchmark, which consists of two contributions: (i) a novel dataset of videos and masks labeled according to several key inpainting failure modes, and (ii) an evaluation scheme that samples slices of the dataset characterized by a fixed content attribute, and scores performance on each slice according to reconstruction, realism, and temporal consistency quality. By revealing systematic changes in performance induced by particular characteristics of the input content, our challenging benchmark enables more insightful analysis into video inpainting methods and serves as an invaluable diagnostic tool for the field. Our code and data are available at ***/MichiganCOG/devil.

关键词： computer vision Systematics Codes Benchmark testing Cameras pattern recognition Behavioral sciences

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Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference

Pushing the Limits of Simple Pipelines for Few-Shot Learning...

引用

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

作者： Hu, Shell Xu Li, Da Stuhmer, Jan Kim, Minyoung Hospedales, Timothy M. Samsung AI Ctr Cambridge Cambridge England Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Few-shot learning (FSL) is an important and topical problem in computer vision that has motivated extensive research into numerous methods spanning from sophisticated metalearning methods to simple transfer learning baselines. We seek to push the limits of a simple-but-effective pipeline for real-world few-shot image classification in practice. To this end, we explore few-shot learning from the perspective of neural architecture, as well as a three stage pipeline of pre-training on external data, meta-training with labelled few-shot tasks, and task-specific fine-tuning on unseen tasks. We investigate questions such as: (1) How pre-training on external data benefits FSL? (2) How state of the art transformer architectures can be exploited? and (3) How to best exploit fine-tuning? Ultimately, we show that a simple transformer-based pipeline yields surprisingly good performance on standard benchmarks such as Mini-ImageNet, CIFAR-FS, CDFSL and Meta-Dataset. Our code is available at https://***/pmf.

关键词： Training computer vision Pipelines Transfer learning computer architecture Benchmark testing Transformers

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Contrastive Boundary Learning for Point Cloud Segmentation

Contrastive Boundary Learning for Point Cloud Segmentation

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

作者： Tang, Liyao Zhan, Yibing Chen, Zhe Yu, Baosheng Tao, Dacheng Univ Sydney Sydney NSW Australia JD Explore Acad Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Point cloud segmentation is fundamental in understanding 3D environments. However, current 3D point cloud segmentation methods usually perform poorly on scene boundaries, which degenerates the overall segmentation performance. In this paper, we focus on the segmentation of scene boundaries. Accordingly, we first explore metrics to evaluate the segmentation performance on scene boundaries. To address the unsatisfactory performance on boundaries, we then propose a novel contrastive boundary learning (CBL) framework for point cloud segmentation. Specifically, the proposed CBL enhances feature discrimination between points across boundaries by contrasting their representations with the assistance of scene contexts at multiple scales. By applying CBL on three different baseline methods, we experimentally show that CBL consistently improves different baselines and assists them to achieve compelling performance on boundaries, as well as the overall performance, e.g. in mIoU. The experimental results demonstrate the effectiveness of our method and the importance of boundaries for 3D point cloud segmentation. Code and model will be made publicly available at hups://***/LiyaoTang/contrastBoundary.

关键词： Point cloud compression Measurement computer vision Three-dimensional displays Codes Computational modeling pattern recognition

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Retrieval Augmented Classification for Long-Tail Visual recognition

Retrieval Augmented Classification for Long-Tail Visual Reco...

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

作者： Long, Alexander Yin, Wei Ajanthan, Thalaiyasingam Vu Nguyen Purkait, Pulak Garg, Ravi Blair, Alan Shen, Chunhua van den Hengel, Anton Amazon Seattle WA 98109 USA Univ Adelaide Adelaide SA Australia Univ New South Wales Sydney NSW Australia Zhejiang Univ Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We introduce Retrieval Augmented Classification (RAC), a generic approach to augmenting standard image classification pipelines with an explicit retrieval module. RAC consists of a standard base image encoder fused with a parallel retrieval branch that queries a non-parametric external memory of pre-encoded images and associated text snippets. We apply RAC to the problem of long-tail classification and demonstrate a significant improvement over previous state-of-the-art on Places365-LT and iNaturalist-2018 (14.5% and 6.7% respectively), despite using only the training datasets themselves as the external information source. We demonstrate that RAC's retrieval module, without prompting, learns a high level of accuracy on tail ***, in turn, frees the base encoder to focus on common classes, and improve its performance thereon. RAC represents an alternative approach to utilizing large, pretrained models without requiring fine-tuning, as well as a first step towards more effectively making use of external memory within common computer vision architectures.

关键词： Training computer vision Visualization Pipelines Memory management Tail pattern recognition

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Omni-DETR: Omni-Supervised Object Detection with Transformers

Omni-DETR: Omni-Supervised Object Detection with Transformer...

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

作者： Wang, Pei Cai, Zhaowei Yang, Hao Swaminathan, Gurumurthy Vasconcelos, Nuno Schiele, Bernt Soatto, Stefano AWS AI Labs Sunnyvale CA 94089 USA Univ Calif San Diego La Jolla CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We consider the problem of omni-supervised object detection, which can use unlabeled, fully labeled and weakly labeled annotations, such as image tags, counts, points, etc., for object detection. This is enabled by a unified architecture, Omni-DETR, based on the recent progress on student-teacher framework and end-to-end transformer based object detection. Under this unified architecture, different types of weak labels can be leveraged to generate accurate pseudo labels, by a bipartite matching based filtering mechanism, for the model to learn. In the experiments, Omni-DETR has achieved state-of-the-art results on multiple datasets and settings. And we have found that weak annotations can help to improve detection performance and a mixture of them can achieve a better trade-off between annotation cost and accuracy than the standard complete annotation. These findings could encourage larger object detection datasets with mixture annotations. The code is available at https://***/amazon-research/omni-detr.

关键词： computer vision Costs Codes Annotations Filtering Object detection computer architecture

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DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover's Distance Improves Out-Of-Distribution Face Identification

DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover's Dist...

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

作者： Phan, Hai Nguyen, Anh Auburn Univ Auburn AL 36849 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Face identification (FI) is ubiquitous and drives many high-stake decisions made by the law enforcement. A common FI approach compares two images by taking the cosine similarity between their image embeddings. Yet, such approach suffers from poor out-of-distribution (OOD) generalization to new types of images (e.g., when a query face is masked, cropped or rotated) not included in the training set or the gallery. Here, we propose a re-ranking approach that compares two faces using the Earth Mover's Distance on the deep, spatial features of image patches. Our extra comparison stage explicitly examines image similarity at a fine-grained level (e.g., eyes to eyes) and is more robust to OOD perturbations and occlusions than traditional FI. Interestingly, without finetuning feature extractors, our method consistently improves the accuracy on all tested OOD queries: masked, cropped, rotated, and adversarial while obtaining similar results on in-distribution images.

关键词： Earth Training computer vision Law enforcement Face recognition Perturbation methods Feature extraction

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Weakly But Deeply Supervised Occlusion-Reasoned Parametric Road Layouts

Weakly But Deeply Supervised Occlusion-Reasoned Parametric R...

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

作者： Liu, Buyu Zhuang, Bingbing Chandraker, Manmohan NEC Labs Amer Princeton NJ 08540 USA Univ Calif San Diego San Diego CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose an end-to-end network that takes a single perspective RGB image of a complex road scene as input, to produce occlusion-reasoned layouts in perspective space as well as a parametric bird's-eye-view (BEV) space. In contrast to prior works that require dense supervision such as semantic labels in perspective view, our method only requires human annotations for parametric attributes that are cheaper and less ambiguous to obtain. To solve this challenging task, our design is comprised of modules that incorporate inductive biases to learn occlusion-reasoning, geometric transformation and semantic abstraction, where each module may be supervised by appropriately transforming the parametric annotations. We demonstrate how our design choices and proposed deep supervision help achieve meaningful representations and accurate predictions. We validate our approach on two public datasets, KITTI and NuScenes, to achieve state-of-the-art results with considerably less human supervision.

关键词： computer vision Laser radar Annotations Roads Computational modeling Semantics Layout

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How Well Do Sparse ImageNet Models Transfer?

How Well Do Sparse ImageNet Models Transfer?

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

作者： Iofinova, Eugenia Peste, Alexandra Kurtz, Mark Alistarh, Dan IST Austria Klosterneuburg Austria Neural Mag Somerville MA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Transfer learning is a classic paradigm by which models pretrained on large "upstream" datasets are adapted to yield good results on "downstream" specialized datasets. Generally, more accurate models on the "upstream" dataset tend to provide better transfer accuracy "downstream". In this work, we perform an in-depth investigation of this phenomenon in the context of convolutional neural networks (CNNs) trained on the ImageNet dataset, which have been pruned-that is, compressed by sparsifiying their connections. We consider transfer using unstructured pruned models obtained by applying several state-of-the-art pruning methods, including magnitude-based, second-order, re-growth, lottery-ticket, and regularization approaches, in the context of twelve standard transfer tasks. In a nutshell, our study shows that sparse models can match or even outperform the transfer performance of dense models, even at high sparsities, and, while doing so, can lead to significant inference and even training speedups. At the same time, we observe and analyze significant differences in the behaviour of different pruning methods. The code is available at: https://***/ISTDASLab/sparse- imagenet-transfer.

关键词： Training Adaptation models computer vision Image coding Transfer learning pattern recognition Convolutional neural networks

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