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Systematic Architectural Design of Scale Transformed Attention Condenser DNNs via Multi-Scale Class Representational Response Similarity Analysis

Systematic Architectural Design of Scale Transformed Attenti...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Hryniowski, Andrew Wong, Alexander Vision and Image Processing Research Group University of Waterloo Canada Waterloo Artificial Intelligence Institute WaterlooON Canada DarwinAI Corp. WaterlooON Canada

ISBN: (纸本)9798350302493

Self-attention mechanisms are commonly included in a convolutional neural networks to achieve an improved efficiency performance balance. However, adding self-attention mechanisms adds additional hyperparameters to tune for the application at hand. In this work we propose a novel type of DNN analysis called Multi-Scale Class Representational Response Similarity Analysis (ClassRep-Sim) which can be used to identify specific design interventions that lead to more efficient self-attention convolutional neural network architectures. Using insights grained from ClassRepSim we propose the Spatial Transformed Attention Condenser (STAC) module, a novel attention-condenser based self-attention module. We show that adding STAC modules to ResNet style architectures can result in up to a 1.6% increase in top-1 accuracy compared to vanilla ResNet models and up to a 0.5% increase in top-1 accuracy compared to SENet models on the ImageNet64x64 dataset, at the cost of up to 1.7% increase in FLOPs and 2x the number of parameters. In addition, we demonstrate that results from ClassRepSim analysis can be used to select an effective parameterization of the STAC module resulting in competitive performance compared to an extensive parameter search. © 2023 ieee.

关键词： Convolutional neural networks

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Improving Commonsense in vision-Language Models via Knowledge Graph Riddles

Improving Commonsense in Vision-Language Models via Knowledg...

引用

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

作者： Ye, Shuquan Xie, Yujia Chen, Dongdong Xu, Yichong Yuan, Lu Zhu, Chenguang Liao, Jing Microsoft Redmond WA USA City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

This paper focuses on analyzing and improving the commonsense ability of recent popular vision-language (VL) models. Despite the great success, we observe that existing VL-models still lack commonsense knowledge/reasoning ability (e.g., "Lemons are sour"), which is a vital component towards artificial general intelligence. Through our analysis, we find one important reason is that existing large-scale VL datasets do not contain much commonsense knowledge, which motivates us to improve the commonsense of VL-models from the data perspective. Rather than collecting a new VL training dataset, we propose a more scalable strategy, i.e., "Data Augmentation with kNowledge graph linearization for CommonsensE capability" (DANCE). It can be viewed as one type of data augmentation technique, which can inject commonsense knowledge into existing VL datasets on the fly during training. More specifically, we leverage the commonsense knowledge graph (e.g., ConceptNet) and create variants of text description in VL datasets via bidirectional sub-graph sequentialization. For better commonsense evaluation, we further propose the first retrieval-based commonsense diagnostic benchmark. By conducting extensive experiments on some representative VL-models, we demonstrate that our DANCE technique is able to significantly improve the commonsense ability while maintaining the performance on vanilla retrieval tasks. The code and data are available at https:// ***/ pleaseconnectwifi/ DANCE.

关键词： Multi-modal learning

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Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis

Don't Lie to Me! Robust and Efficient Explainability with Ve...

引用

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

作者： Fel, Thomas Ducoffe, Melanie Vigouroux, David Cadene, Remi Capelle, Mikael Nicodeme, Claire Serre, Thomas Brown Univ Carney Inst Brain Sci Providence RI 02912 USA Artificial & Nat Intelligence Toulouse Inst Toulouse France Sorbonne Univ CNRS Paris France IRT St Exupery Toulouse France SNCF Innovat & Res Div Paris France Thales Alenia Space Cannes France Airbus AI Res Blagnac France

ISBN: (纸本)9798350301298

A plethora of attribution methods have recently been developed to explain deep neural networks. These methods use different classes of perturbations (e.g, occlusion, blurring, masking, etc) to estimate the importance of individual image pixels to drive a model's decision. Nevertheless, the space of possible perturbations is vast and current attribution methods typically require significant computation time to accurately sample the space in order to achieve high-quality explanations. In this work, we introduce EVA (Explaining using Verified Perturbation Analysis) - the first explainability method which comes with guarantees that an entire set of possible perturbations has been exhaustively searched. We leverage recent progress in verified perturbation analysis methods to directly propagate bounds through a neural network to exhaustively probe a - potentially infinite-size - set of perturbations in a single forward pass. Our approach takes advantage of the beneficial properties of verified perturbation analysis, i.e., time efficiency and guaranteed complete - sampling agnostic coverage of the perturbation space - to identify image pixels that drive a model's decision. We evaluate EVA systematically and demonstrate state-of-the-art results on multiple benchmarks. Our code is freely available: ***/deel-ai/formal-explainability

关键词： Explainable computer vision

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Unsupervised Automatic Defect Inspection based on Image Matching and Local One-class Classification

Unsupervised Automatic Defect Inspection based on Image Matc...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Lv, Chengkan Zhang, Zhengtao Shen, Fei Zhang, Feng Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences The School of Artificial Intelligence Beijing China CASI Vision Technology Co. Ltd. Luoyang China

ISBN: (纸本)9798350302493

In this paper, an unsupervised defect inspection method based on anomaly detection is proposed to inspect various kinds of surface defects in the field of industrial production. This method consists of two modules: (i) An image matching module is utilized to align the input image with a pre-specified template image. Specifically, all objects to be detected will be adjusted to the same position and angle. The aligned images can reduce the difficulty of the training stage, facilitating the subsequent feature extraction and anomaly localization. (ii) After the image matching procedure, an anomaly localization module is trained to learn a mapping that concentrates normal samples in feature space. In particular, each local image region is assigned a feature center by adopting a feature map as the mapping target. Therefore, the compactness of the features extracted from the same region can be improved, which is beneficial to detect potential anomalous targets. Moreover, various artificial defective images are synthesized during the training stage to further improve the discriminatory ability of the anomaly localization module. A series of experiments are conducted on MAD dataset and the industrial production line. The experimental results verify the efficiency and versatility of the proposed method. © 2023 ieee.

关键词： Inspection

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Distilling Self-Supervised vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation

Distilling Self-Supervised Vision Transformers for Weakly-Su...

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

作者： Kang, Dahyun Koniusz, Piotr Cho, Minsu Murray, Naila Meta AI Menlo Pk CA 94025 USA POSTECH Pohang South Korea CSIRO Data61 Canberra Australia Australian Natl Univ Canberra Australia

ISBN: (纸本)9798350301298

We address the task of weakly-supervised few-shot image classification and segmentation, by leveraging a vision Transformer (ViT) pretrained with self-supervision. Our proposed method takes token representations from the self-supervised ViT and leverages their correlations, via self-attention, to produce classification and segmentation predictions through separate task heads. Our model is able to effectively learn to perform classification and segmentation in the absence of pixel-level labels during training, using only image-level labels. To do this it uses attention maps, created from tokens generated by the self-supervised ViT backbone, as pixel-level pseudo-labels. We also explore a practical setup with "mixed" supervision, where a small number of training images contains ground-truth pixel-level labels and the remaining images have only image-level labels. For this mixed setup, we propose to improve the pseudo-labels using a pseudo-label enhancer that was trained using the available ground-truth pixel-level labels. Experiments on Pascal-5(i) and COCO-20(i) demonstrate significant performance gains in a variety of supervision settings, and in particular when little-to-no pixel-level labels are available.

关键词： continual low-shot meta or long-tail learning Transfer

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Overlooked Factors in Concept-based Explanations: Dataset Choice, Concept Learnability, and Human Capability

Overlooked Factors in Concept-based Explanations: Dataset Ch...

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

作者： Ramaswamy, Vikram V. Kim, Sunnie S. Y. Fong, Ruth Russakovsky, Olga Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350301298

Concept-based interpretability methods aim to explain a deep neural network model's components and predictions using a pre-defined set of semantic concepts. These methods evaluate a trained model on a new, "probe" dataset and correlate the model's outputs with concepts labeled in that dataset. Despite their popularity, they suffer from limitations that are not well-understood and articulated in the literature. In this work, we identify and analyze three commonly overlooked factors in concept-based explanations. First, we find that the choice of the probe dataset has a profound impact on the generated explanations. Our analysis reveals that different probe datasets lead to very different explanations, suggesting that the generated explanations are not generalizable outside the probe dataset. Second, we find that concepts in the probe dataset are often harder to learn than the target classes they are used to explain, calling into question the correctness of the explanations. We argue that only easily learnable concepts should be used in concept-based explanations. Finally, while existing methods use hundreds or even thousands of concepts, our human studies reveal a much stricter upper bound of 32 concepts or less, beyond which the explanations are much less practically useful. We discuss the implications of our findings and provide suggestions for future development of concept-based interpretability methods. Code for our analysis and user interface can be found at https://***/princetonvisualai/OverlookedFactors

关键词： Explainable computer vision

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N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution

N-Gram in Swin Transformers for Efficient Lightweight Image ...

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

作者： Choi, Haram Lee, Jeongmin Yang, Jihoon Sogang Univ Dept Comp Sci & Engn Seoul South Korea LG Innotek Seoul South Korea

ISBN: (纸本)9798350301298

While some studies have proven that Swin Transformer (Swin) with window self-attention (WSA) is suitable for single image super-resolution (SR), the plain WSA ignores the broad regions when reconstructing high-resolution images due to a limited receptive field. In addition, many deep learning SR methods suffer from intensive computations. To address these problems, we introduce the N-Gram context to the low-level vision with Transformers for the first time. We define N-Gram as neighboring local windows in Swin, which differs from text analysis that views N-Gram as consecutive characters or words. N-Grams interact with each other by sliding-WSA, expanding the regions seen to restore degraded pixels. Using the N-Gram context, we propose NGswin, an efficient SR network with SCDP bottleneck taking multi-scale outputs of the hierarchical encoder. Experimental results show that NGswin achieves competitive performance while maintaining an efficient structure when compared with previous leading methods. Moreover, we also improve other Swin-based SR methods with the N-Gram context, thereby building an enhanced model: SwinIR-NG. Our improved SwinIR-NG outperforms the current best lightweight SR approaches and establishes state-of-the-art results. Codes are available at https://***/rami0205/NGramSwin.

关键词： Low-level vision

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EfficientViT: Memory Efficient vision Transformer with Cascaded Group Attention

EfficientViT: Memory Efficient Vision Transformer with Casca...

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

作者： Liu, Xinyu Peng, Houwen Zheng, Ningxin Yang, Yuqing Hu, Han Yuan, Yixuan Chinese Univ Hong Kong Hong Kong Peoples R China Microsoft Res Redmond WA USA

ISBN: (纸本)9798350301298

vision transformers have shown great success due to their high model capabilities. However, their remarkable performance is accompanied by heavy computation costs, which makes them unsuitable for real-time applications. In this paper, we propose a family of high-speed vision transformers named EfficientViT. We find that the speed of existing transformer models is commonly bounded by memory inefficient operations, especially the tensor reshaping and element-wise functions in MHSA. Therefore, we design a new building block with a sandwich layout, i.e., using a single memory-bound MHSA between efficient FFN layers, which improves memory efficiency while enhancing channel communication. Moreover, we discover that the attention maps share high similarities across heads, leading to computational redundancy. To address this, we present a cascaded group attention module feeding attention heads with different splits of the full feature, which not only saves computation cost but also improves attention diversity. Comprehensive experiments demonstrate EfficientViT outperforms existing efficient models, striking a good trade-off between speed and accuracy. For instance, our EfficientViT-M5 surpasses MobileNetV3-Large by 1.9% in accuracy, while getting 40.4% and 45.2% higher throughput on Nvidia V100 GPU and Intel Xeon CPU, respectively. Compared to the recent efficient model MobileViT-XXS, EfficientViT-M2 achieves 1.8% superior accuracy, while running 5.8x/3.7x faster on the GPU/CPU, and 7.4x faster when converted to ONNX format. Code and models are available at here.

关键词： Efficient and scalable vision

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Win-Fail Action recognition

Win-Fail Action Recognition

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22nd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Parmar, Paritosh Morris, Brendan Univ British Columbia Vancouver BC Canada Univ Nevada Las Vegas NV 89154 USA

ISBN: (纸本)9781665458245

Current video/action understanding systems have demonstrated impressive performance on large recognition tasks. However, they might be limiting themselves to learning to recognize spatiotemporal patterns, rather than attempting to thoroughly understand the actions. To spur progress in the direction of a more comprehensive understanding of videos, we introduce the task of win-fail action recognition - differentiating between successful and failed attempts at various activities. We introduce a first of its kind paired win-fail action understanding dataset with samples from the following domains: "General Stunts," "Internet Wins-Fails," "Trick Shots," & "Party Games." Unlike existing action recognition datasets, intra-class variation is high making the task challenging, yet feasible. Using a battery of experiments, including a novel video retrieval test, we systematically analyze the characteristics of our win-fail task/dataset, and determine its suitability to serve as a video understanding problem benchmark. While current prototypical action recognition methods work well on our task/dataset, they still leave a large gap to achieve high performance. We hope to motivate more work towards the true understanding of actions/videos. Dataset will be available from: https://***/ParitoshParmar/Win-Fail-Action-recognition.

关键词： Analytical models Pediatrics Limiting Benchmark testing Spatial databases Spatiotemporal phenomena pattern recognition

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InternImage: Exploring Large-Scale vision Foundation Models with Deformable Convolutions

InternImage: Exploring Large-Scale Vision Foundation Models ...

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

作者： Wang, Wenhai Dai, Jifeng Chen, Zhe Huang, Zhenhang Li, Zhiqi Zhu, Xizhou Hu, Xiaowei Lu, Tong Lu, Lewei Li, Hongsheng Wang, Xiaogang Qiao, Yu Shanghai AI Lab Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China Nanjing Univ Nanjing Peoples R China SenseTime Res Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state. This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs. Different from the recent CNNs that focus on large dense kernels, InternImage takes deformable convolution as the core operator, so that our model not only has the large effective receptive field required for downstream tasks such as detection and segmentation, but also has the adaptive spatial aggregation conditioned by input and task information. As a result, the proposed InternImage reduces the strict inductive bias of traditional CNNs and makes it possible to learn stronger and more robust patterns with large-scale parameters from massive data like ViTs. The effectiveness of our model is proven on challenging benchmarks including ImageNet, COCO, and ADE20K. It is worth mentioning that InternImage-H achieved a new record 65.4 mAP on COCO test-dev and 62.9 mIoU on ADE20K, outperforming current leading CNNs and ViTs.

关键词： detection recognition: Categorization retrieval

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