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Ante-Hoc Generation of Task-Agnostic Interpretation Maps

Ante-Hoc Generation of Task-Agnostic Interpretation Maps

引用

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

作者： Akash Guna, R.T. Benitez, Raul Sikha, O.K. Amrita School of Engineering Department of Computer Science and Engineering Coimbatore India Universitat Politècnica de Catalunya BarcelonaTech Departament of Automatic Control Spain

ISBN: (纸本)9798350302493

Existing explainability approaches for convolutional neural networks (CNNs) are mainly applied after training (post-hoc) which is generally unreliable. Ante-hoc explainers trained simultaneously with the CNN are more reliable. However, current ante-hoc explanation methods mainly generate inexplicit concept-based explanations tailored to specific tasks. To address these limitations, we propose a task-agnostic ante-hoc framework that can generate interpretation maps to visually explain any CNN. Our framework simultaneously trains the CNN and a weighting network - an explanation generation module. The generated maps are self-explanatory, eliminating the need for manual identification of concepts. We demonstrate that our method can interpret classification, facial landmark detection, and image captioning tasks. We show that our framework is explicit, faithful, and stable through experiments. To the best of our knowledge, this is the first ante-hoc CNN explanation strategy that produces visual explanations generic to CNNs for different tasks. © 2023 ieee.

关键词： computer vision

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Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution

Fast and Memory-Efficient Network Towards Efficient Image Su...

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

作者： Du, Zongcai Liu, Ding Liu, Jie Tang, Jie Wu, Gangshan Fu, Lean Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China ByteDance Inc Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Runtime and memory consumption are two important aspects for efficient image super-resolution (EISR) models to be deployed on resource-constrained devices. Recent advances in EISR [16, 32] exploit distillation and aggregation strategies with plenty of channel split and concatenation operations to fully use limited hierarchical features. In contrast, sequential network operations avoid frequently accessing preceding states and extra nodes, and thus are beneficial to reducing the memory consumption and runtime overhead. Following this idea, we design our lightweight network backbone by mainly stacking multiple highly optimized convolution and activation layers and decreasing the usage of feature fusion. We propose a novel sequential attention branch, where every pixel is assigned an important factor according to local and global contexts, to enhance high-frequency details. In addition, we tailor the residual block for EISR and propose an enhanced residual block (ERB) to further accelerate the network inference. Finally, combining all the above techniques, we construct a fast and memory-efficient network (FMEN) and its small version FMEN-S, which runs 33% faster and reduces 74% memory consumption compared with the state-of-the-art EISR model: E-RFDN, the champion in [49]. Besides, FMEN-S achieves the lowest memory consumption and the second shortest runtime in NTIRE 2022 challenge on efficient super-resolution [28]. Code is available at https://***/NJU-Jet/FMEN.

关键词： computer vision Runtime Codes Convolution conferences Memory management Superresolution

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EFE: End-to-end Frame-to-Gaze Estimation

EFE: End-to-end Frame-to-Gaze Estimation

引用

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

作者： Balim, Haldun Park, Seonwook Wang, Xi Zhang, Xucong Hilliges, Otmar ETH Zürich Department of Computer Science Switzerland Lunit Inc. Delft University of Technology Computer Vision Lab Netherlands

ISBN: (纸本)9798350302493

Despite the recent development of learning-based gaze estimation methods, most methods require one or more eye or face region crops as inputs and produce a gaze direction vector as output. Cropping results in a higher resolution in the eye regions and having fewer confounding factors (such as clothing and hair) is believed to benefit the final model performance. However, this eye/face patch cropping process is expensive, erroneous, and implementation-specific for different methods. In this paper, we propose a frame-to-gaze network that directly predicts both 3D gaze origin and 3D gaze direction from the raw frame out of the camera without any face or eye cropping. Our method demonstrates that direct gaze regression from the raw downscaled frame, from FHD/HD to VGA/HVGA resolution, is possible despite the challenges of having very few pixels in the eye region. The proposed method achieves comparable results to state-of-the-art methods in Point-of-Gaze (PoG) estimation on three public gaze datasets: GazeCapture, MPIIFaceGaze, and EVE, and generalizes well to extreme camera view changes. © 2023 ieee.

关键词： Cameras

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Dual Attention Poser: Dual Path Body Tracking Based on Attention

Dual Attention Poser: Dual Path Body Tracking Based on Atten...

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

作者： Di, Xinhan Dai, Xiaokun Zhang, Xinkang Chen, Xinrong Fudan Universiry Academy for Engineering&Technology China Deepearthgo Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China

ISBN: (纸本)9798350302493

Currently, mixed reality head-mounted displays tracking the full body of users is an important human-computer interaction mode through the pose of the head and the hands. Unfortunately, users' virtual representation and experience is limited due to high reconstruction error when simple transformer network architecture is applied. In this paper, we present a novel model, named Dual Attention Poser, which can learn the whole body reconstruction at a high accuracy. The proposed model consists of three key modules. Among them, dual-path attention encoder is designed to extract feature of the sparse signals. Cross attention mixer module enable the fusion of representation in the double path. Attention-gated-mlp decoder is applied to decode the hidden feature from the sparse input through attention gate. Test results on the AMASS dataset show that Dual Attention Poser can reduce the error by up to 18.2% in comparison with the state-of-the-art results. © 2023 ieee.

关键词： Network architecture

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MARRS: Modern Backbones Assisted Co-training for Rapid and Robust Semi-Supervised Domain Adaptation

MARRS: Modern Backbones Assisted Co-training for Rapid and R...

引用

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

作者： Jain, Saurabh Kumar Das, Sukhendu Indian Institute of Technology Visualization and Perception Lab Department of Computer Science Engineering Madras India

ISBN: (纸本)9798350302493

Semi-Supervised Domain Adaptation (SSDA) aims to develop domain invariant models from scarcely labeled target domain in addition to the fully labeled source domain. Current SSDA works are often applied in conjunction with ResNet34 backbone, which makes them overlook the advantages of utilizing other backbones. Hence, in this paper, we investigate the impact of employing different modern backbones in SSDA and propose a novel solution named Modern Backbones Assisted Co-training for Rapid and Robust Semi-Supervised Domain Adaptation (MARRS), that uses discriminative features of two modern backbones for training linear classifiers using the well established co-training framework. To induce diversity among classifiers for effective co-training, we propose a novel module that produces diversity at three levels, namely image, backbone, and feature distribution levels. Experiments reveal that MARRS not only achieves state-of-the-art performance across all popular SSDA datasets, but also drastically cuts the computation time compared to other SSDA approaches, making MARRS a rapid and robust solution for SSDA. We also provide extensive ablation experiments to verify our framework's vitality and primary design choices. © 2023 ieee.

关键词： computer vision

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A Hybrid Network of CNN and Transformer for Lightweight Image Super-Resolution

A Hybrid Network of CNN and Transformer for Lightweight Imag...

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

作者： Fang, Jinsheng Lin, Hanjiang Chen, Xinyu Zeng, Kun Minnan Normal Univ Zhangzhou Peoples R China Minjiang Univ Fuzhou Fujian Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recently, a number of CNN based methods have made great progress in single image super-resolution. However, these existing architectures commonly build massive number of network layers, bringing high computational complexity and heavy memory consumption, which is inappropriate to be applied on embedded terminals such as mobile platforms. In order to solve this problem, we propose a hybrid network of CNN and Transformer (HNCT) for lightweight image super-resolution. In general, HNCT consists of four parts, which are shallow feature extraction module, Hybrid Blocks of CNN and Transformer (HBCTs), dense feature fusion module and up-sampling module, respectively. By combining CNN and Transformer, HBCT extracts deep features beneficial for super-resolution reconstruction in consideration of both local and non-local priors, while being lightweight and flexible enough. Enhanced spatial attention is introduced in HBCT to further improve performance. Extensive experimental results show our HNCT is superior to the state-of-the-art methods in terms of super-resolution performance and model complexity. Moreover, we won the second best PSNR and the least activation operations in NTIRE 2022 Efficient SR Challenge. Code is available at https://***/lhjthp/HNCT.

关键词： computer vision Codes conferences Superresolution Memory management Transformers Feature extraction

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CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search

CLIP2Protect: Protecting Facial Privacy using Text-Guided Ma...

引用

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

作者： Shamshad, Fahad Naseer, Muzammal Nandakumar, Karthik Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350301298

The success of deep learning based face recognition systems has given rise to serious privacy concerns due to their ability to enable unauthorized tracking of users in the digital world. Existing methods for enhancing privacy fail to generate "naturalistic" images that can protect facial privacy without compromising user experience. We propose a novel two-step approach for facial privacy protection that relies on finding adversarial latent codes in the low-dimensional manifold of a pretrained generative model. The first step inverts the given face image into the latent space and finetunes the generative model to achieve an accurate reconstruction of the given image from its latent code. This step produces a good initialization, aiding the generation of high-quality faces that resemble the given identity. Subsequently, user-defined makeup text prompts and identity-preserving regularization are used to guide the search for adversarial codes in the latent space. Extensive experiments demonstrate that faces generated by our approach have stronger black-box transferability with an absolute gain of 12.06% over the state-of-the-art facial privacy protection approach under the face verification task. Finally, we demonstrate the effectiveness of the proposed approach for commercial face recognition systems. Our code is available at https://***/fahadshamshad/Clip2Protect.

关键词： accountability ethics in vision fairness privacy Transparency

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A Unified Model for Face Matching and Presentation Attack Detection using an Ensemble of vision Transformer Features

A Unified Model for Face Matching and Presentation Attack De...

引用

23rd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Al-Refai, Rouqaiah Nandakumar, Karthik Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350320565

A typical automated face recognition system is composed of three main component tasks: face detection and alignment (FDA), face presentation attack detection (FPAD), and face representation and matching (FRM). These tasks are often treated as standalone problems and deep neural network (DNN)-based solutions have been proposed to address them individually. However, in resource-constrained scenarios it would be ideal to have a unified DNN model that can perform all the three tasks together. As a first step towards realizing this goal, this work attempts to perform joint FRM and FPAD based on a single vision Transformer (ViT) backbone. Recent work demonstrating the ability of ViT to extract a diverse set of feature representations gives rise to the tantalising possibility of building an end-to-end face recognition system using a single ViT model. The standard approach for designing multi-task DNNs is to implement different classification heads (e.g., for FRM and FPAD) based on a common stem/base and learn these heads either individually or jointly. A key contribution of this work is to demonstrate that this naive multi-head approach results in sub-optimal performance for either FRM or FPAD, because the features required by these tasks are very different. While good FPAD performance depends on accurately characterizing the micro textures, face matching demands attention towards more global characteristics. Hence, we propose a novel feature ensemble approach, where an ensemble of local features extracted from the intermediate blocks of a ViT are utilized for FPAD, while face matching is performed based on the ViT class token. Experiments demonstrate that the proposed ViT feature ensemble approach is able to achieve good performance for both face matching and FPAD compared to the multi-head approach.

关键词： Deep learning Face recognition conferences Neural networks Feature extraction Transformers Multitasking

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Contrastive Learning-based Robust Object Detection under Smoky Conditions

Contrastive Learning-based Robust Object Detection under Smo...

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

作者： Wu, Wei Chang, Hao Zheng, Yonghua Li, Zhu Chen, Zhiwen Zhang, Ziheng Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO 64110 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Object detection is to effectively find out interested targets in images and then accurately determine their categories and positions. Recently many excellent methods have been developed to provide powerful detection capability. However, their performance may degrade significantly under severe weather such as smoky conditions. In this paper, we propose a contrastive learning-based robust object detection algorithm for smoke images. The proposed object detector consists of two modules: contrastive learning module and object bounding box prediction module. The first module learns representation vectors by maximizing agreement between different augmented views of the same smoke image. These representations are then sent to the second module to yield the bounding box for each object. In addition, we also propose a novel affine data augmentation method. Extensive experiments have been conducted on A2I2-Haze dataset which is the first real haze dataset with in-situ smoke measurement aligned to aerial and ground imagery. This dataset is also the only dataset used in the 5th UG(2)+ challenges of CVPR 2022 for both training and testing. Compared with state-of-the-art methods, evaluation results show the superiority of our proposed object detector.

关键词： Training computer vision conferences Object detection Detectors Transforms Prediction algorithms

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Strengthening the Transferability of Adversarial Examples Using Advanced Looking Ahead and Self-CutMix

Strengthening the Transferability of Adversarial Examples Us...

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

作者： Jang, Donggon Son, Sanghyeok Kim, Dae-Shik Korea Adv Inst Sci & Technol KAIST Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep neural networks (DNNs) are vulnerable to adversarial examples generated by adding malicious noise imperceptible to a human. The adversarial examples successfully fool the models under the white-box setting, but the performance of attacks under the black-box setting degrades significantly, which is known as the low transferability problem. Various methods have been proposed to improve transferability, yet they are not effective against adversarial training and defense models. In this paper, we introduce two new methods termed Lookahead Iterative Fast Gradient Sign Method (LI-FGSM) and Self-CutMix (SCM) to address the above issues. LI-FGSM updates adversarial perturbations with the accumulated gradient obtained by looking ahead. A previous gradient-based attack is used for looking ahead during N steps to explore the optimal direction at each iteration. It allows the optimization process to escape the sub-optimal region and stabilize the update directions. SCM leverages the modified CutMix, which copies a patch from the original image and pastes it back at random positions of the same image, to preserve the internal information. SCM makes it possible to generate more transferable adversarial examples while alleviating the overfitting to the surrogate model employed. Our two methods are easily incorporated with the previous iterative gradient-based attacks. Extensive experiments on ImageNet show that our approach acquires state-of-the-art attack success rates not only against normally trained models but also against adversarial training and defense models.

关键词： Training Deep learning computer vision Computational modeling Perturbation methods conferences Neural networks

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