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RV-GAN: Recurrent GAN for Unconditional Video Generation

RV-GAN: Recurrent GAN for Unconditional Video Generation

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

作者： Gupta, Sonam Keshari, Arti Das, Sukhendu Indian Inst Technol Dept Comp Sci Engn Visualizat & Percept Lab Madras Tamil Nadu India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Generative models aiming to generate content from noise have achieved high-fidelity synthesis for image data. However, obtaining comparable performance in the field of unconditional video generation still remains challenging. In this work, we propose a recurrent GAN architecture to model the high-dimensional video data distribution. Recurrent networks by design are able to generate complex, long sequences in an autoregressive fashion. However, the standard LSTM unit for videos (ConvLSTM) is not ideally suited for the task of unconditional video generation. Therefore, we propose a simple yet effective LSTM variant called as TransConv LSTM (TC-LSTM) by modulating the conventional ConvLSTM to have a transpose convolutional structure in input-to-state transitions. This enables the network to model both spatial and temporal relationships across layers simultaneously inside the TC-LSTM unit. TC-LSTM unit acts as a building block of our generator. Extensive quantitative and qualitative analysis shows that RV-GAN outperforms state-of-the-art methods by a significant margin on Moving MNIST, MUG, Weizmann and UCF101 datasets. Additionally, owing to the recurrent structure, our method is able to generate high-quality videos, up to 2 times longer (32 frames) than training videos at inference time. Further analysis confirms that the proposed architecture is generic and can be easily adapted to other tasks like class-conditional video synthesis and text-to-video synthesis.

关键词： Training Architecture computer architecture Generative adversarial networks Data models Hybrid power systems Generators

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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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CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection

CFA: Constraint-based Finetuning Approach for Generalized Fe...

引用

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

作者： Guirguis, Karim Hendawy, Ahmed Eskandar, George Abdelsamad, Mohamed Kayser, Matthias Beyerer, Juergen Robert Bosch GmbH Stuttgart Germany Univ Stuttgart Stuttgart Germany Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany

ISBN: (纸本)9781665487399

Few-shot object detection (FSOD) seeks to detect novel categories with limited data by leveraging prior knowledge from abundant base data. Generalized few-shot object detection (G-FSOD) aims to tackle FSOD without forgetting previously seen base classes and, thus, accounts for a more realistic scenario, where both classes are encountered during test time. While current FSOD methods suffer from catastrophic forgetting, G-FSOD addresses this limitation yet exhibits a performance drop on novel tasks compared to the state-of-the-art FSOD. In this work, we propose a constraint-based finetuning approach (CFA) to alleviate catastrophic forgetting, while achieving competitive results on the novel task without increasing the model capacity. CFA adapts a continual learning method, namely Average Gradient Episodic Memory (A-GEM) to G-FSOD. Specifically, more constraints on the gradient search strategy are imposed from which a new gradient update rule is derived, allowing for better knowledge exchange between base and novel classes. To evaluate our method, we conduct extensive experiments on MS-COCO and PASCAL-VOC datasets. Our method outperforms current FSOD and G-FSOD approaches on the novel task with minor degeneration on the base task. Moreover, CFA is orthogonal to FSOD approaches and operates as a plug-and-play module without increasing the model capacity or inference time.

关键词： Learning systems Adaptation models computer vision conferences Object detection Search problems pattern recognition

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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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GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification

GAF-NAU: Gramian Angular Field encoded Neighborhood Attentio...

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

作者： Paheding, Sidike Reyes, Abel A. Kasaragod, Anush Oommen, Thomas Michigan Technol Univ Houghton MI 49931 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Hyperspectral image (HSI) classification is the most vibrant area of research in the hyperspectral community due to the rich spectral information contained in HSI can greatly aid in identifying objects of interest. However, inherent non-linearity between materials and the corresponding spectral profiles brings two major challenges in HSI classification: interclass similarity and intraclass variability. Many advanced deep learning methods have attempted to address these issues from the perspective of a region/patch-based approach, instead of a pixel-based alternate. However, the patch-based approaches hypothesize that neighborhood pixels of a target pixel in a fixed spatial window belong to the same class. And this assumption is not always true. To address this problem, we herein propose a new deep learning architecture, namely Gramian Angular Field encoded Neighborhood Attention U-Net (GAF-NAU), for pixel-based HSI classification. The proposed method does not require regions or patches centered around a raw target pixel to perform 2D-CNN based classification, instead, our approach transforms 1D pixel vector in HSI into 2D angular feature space using Gramian Angular Field (GAF) and then embed it to a new neighborhood attention network to suppress irrelevant angular feature while emphasizing on pertinent features useful for HSI classification task. Evaluation results on three publicly available HSI datasets demonstrate the superior performance of the proposed model. The source code available at https://***/MAIN-Lab/GAF-NAU/

关键词： Training Deep learning Transforms Logic gates pattern recognition Object recognition Task analysis

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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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TempT: Temporal consistency for Test-time adaptation

TempT: Temporal consistency for Test-time adaptation

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Mutlu, Onur Cezmi Honarmand, Mohammadmahdi Surabhi, Saimourya Wall, Dennis P. Stanford University United States

ISBN: (纸本)9798350302493

We introduce Temporal consistency for Test-time adaptation (TempT), a novel method for test-time adaptation on videos through the use of temporal coherence of predictions across sequential frames as a self-supervision signal. TempT is an approach with broad potential applications in computer vision tasks, including facial expression recognition (FER) in videos. We evaluate TempT's performance on the AffWild2 dataset. Our approach focuses solely on the unimodal visual aspect of the data and utilizes a popular 2D CNN backbone, in contrast to larger sequential or attention-based models used in other approaches. Our preliminary experimental results demonstrate that TempT has competitive performance compared to the previous years' reported performances, and its efficacy provides a compelling proof-of-concept for its use in various real-world applications. © 2023 ieee.

关键词： computer vision

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Cross-modal Image Synthesis within Dual-Energy X-ray Security Imagery

Cross-modal Image Synthesis within Dual-Energy X-ray Securit...

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

作者： Isaac-Medina, Brian K. S. Bhowmik, Neelanjan Willcocks, Chris G. Breckon, Toby P. Univ Durham Dept Comp Sci Durham England Univ Durham Dept Engn Durham England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Dual-energy X-ray scanners are used for aviation security screening given their capability to discriminate materials inside passenger baggage. To facilitate manual operator inspection, a pseudo-colouring is assigned to the effective composition of the material. Recently, paired image to image translation models based on conditional Generative Adversarial Networks (cGAN) have shown to be effective for image colourisation. In this work, we investigate the use of such a model to translate from the raw X-ray energy responses (high, low, effective-Z) to the pseudo-coloured images and vice versa. Specifically, given N X-ray modalities, we train a cGAN conditioned in N - m domains to generate the remaining m representation. Our method achieves a mean squared error (MSE) of 16.5 and a structural similarity index (SSIM) of 0.9815 when using the raw modalities to generate the pseudo-colour representation. Additionally, raw X-ray high energy, low energy and effective-Z projections were generated given the pseudo-colour image with minimum MSE of 2.57, 5.63 and 1.43, and maximum SSIM of 0.9953, 0.9901 and 0.9921. Furthermore, we assess the quality of our synthesised pseudo-colour reconstructions by measuring the performance of two object detection models originally trained on real X-ray pseudo-colour images over our generated pseudo-colour images. Interestingly, our generated pseudo-colour images obtain marginally improved detection performance than the corresponding real X-ray pseudo-colour images, showing that meaningful representations are synthesized and that these reconstructions are applicable for differing aviation security tasks.

关键词： computer architecture Predictive models Generative adversarial networks Generators Data models pattern recognition Security

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M2FNet: Multi-modal Fusion Network for Emotion recognition in Conversation

M2FNet: Multi-modal Fusion Network for Emotion Recognition i...

引用

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

作者： Chudasama, Vishal Kar, Purbayan Gudmalwar, Ashish Shah, Nirmesh Wasnik, Pankaj Onoe, Naoyuki Sony Res India Media Anal Grp Bangalore Karnataka India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Emotion recognition in Conversations (ERC) is crucial in developing sympathetic human-machine interaction. In conversational videos, emotion can be present in multiple modalities, i.e., audio, video, and transcript. However, due to the inherent characteristics of these modalities, multi-modal ERC has always been considered a challenging undertaking. Existing ERC research focuses mainly on using text information in a discussion, ignoring the other two modalities. We anticipate that emotion recognition accuracy can be improved by employing a multi-modal approach. Thus, in this study, we propose a Multi-modal Fusion Network (M2FNet) that extracts emotion-relevant features from visual, audio, and text modality. It employs a multi-head attention-based fusion mechanism to combine emotion-rich latent representations of the input data. We introduce a new feature extractor to extract latent features from the audio and visual modality. The proposed feature extractor is trained with a novel adaptive margin-based triplet loss function to learn emotion-relevant features from the audio and visual data. In the domain of ERC, the existing methods perform well on one benchmark dataset but not on others. Our results show that the proposed M2FNet architecture outperforms all other methods in terms of weighted average F1 score on well-known MELD and IEMOCAP datasets and sets a new state-of-the-art performance in ERC.

关键词： Human computer interaction Emotion recognition Visualization Adaptation models Benchmark testing Feature extraction Robustness

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DRT: A Lightweight Single Image Deraining Recursive Transformer

DRT: A Lightweight Single Image Deraining Recursive Transfor...

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

作者： Liang, Yuanchu Anwar, Saeed Liu, Yang Australian Natl Univ Canberra Australia CSIRO Data61 Sydney Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Over parameterization is a common technique in deep learning to help models learn and generalize sufficiently to the given task;nonetheless, this often leads to enormous network structures and consumes considerable computing resources during training. Recent powerful transformer-based deep learning models on vision tasks usually have heavy parameters and bear training difficulty. However, many dense-prediction low-level computer vision tasks, such as rain streak removing, often need to be executed on devices with limited computing power and memory in practice. Hence, we introduce a recursive local window-based self-attention structure with residual connections and propose deraining a recursive transformer (DRT), which enjoys the superiority of the transformer but requires a small amount of computing resources. In particular, through recursive architecture, our proposed model uses only similar to 1.3% of the number of parameters of the current best performing model in deraining while exceeding the state-of-the-art methods on the Rain100L benchmark by at least 0.33 dB. Ablation studies also investigate the impact of recursions on derain outcomes. Moreover, since the model contains no deliberate design for deraining, it can also be applied to other image restoration tasks. Our experiment shows that it can achieve competitive results on desnowing. The source code and pretrained model can be found at https://***/YC-Liang/DRT.

关键词： Training Deep learning computer vision Rain Computational modeling computer architecture Transformers

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