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Ensemble Spatial and Temporal vision Transformer for Action Units Detection

Ensemble Spatial and Temporal Vision Transformer for Action ...

引用

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

作者： Tu Vu, Ngoc Thong Huynh, Van Nghia Nguyen, Trong Kim, Soo-Hyung Chonnam National University Department of Ai Convergence Korea Republic of

ISBN: (纸本)9798350302493

Facial Action Units detection (FAUs) represents a fine-grained classification problem that involves identifying different units on the human face, as defined by the Facial Action Coding System. In this paper, we present a simple yet efficient vision Transformer-based approach for addressing the task of Action Units (AU) detection in the context of Affective Behavior Analysis in-the-wild (ABAW) competition. We employ the Video vision Transformer(ViViT) Network to capture the temporal facial change in the video. Besides, to reduce massive size of the vision Transformers model, we replace the ViViT feature extraction layers with the CNN backbone (Regnet). Our model outperform the baseline model of ABAW 2023 challenge [8], with a notable 14% difference in result. Our team has achieved a position within the top 5 teams in the ABAW 2023 competition, scoring slightly below the top three and four teams by a narrow margin of 0.27% and 0.43%, respectively. © 2023 ieee.

关键词： computer vision

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

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

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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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A New Dataset and Transformer for Stereoscopic Video Super-Resolution

A New Dataset and Transformer for Stereoscopic Video Super-R...

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

作者： Imani, Hassan Islam, Md Baharul Wong, Lai-Kuan Bahcesehir Univ Istanbul Turkey Amer Univ Malta Cospicua Malta Multimedia Univ Cyberjaya Malaysia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Stereo video super-resolution (SVSR) aims to enhance the spatial resolution of the low-resolution video by reconstructing the high-resolution video. The key challenges in SVSR are preserving the stereo-consistency and temporal-consistency, without which viewers may experience 3D fatigue. There are several notable works on stereoscopic image super-resolution, but there is little research on stereo video super-resolution. In this paper, we propose a novel Transformer-based model for SVSR, namely Trans-SVSR. Trans-SVSR comprises two key novel components: a spatio-temporal convolutional self-attention layer and an optical flow-based feed-forward layer that discovers the correlation across different video frames and aligns the features. The parallax attention mechanism (PAM) that uses the cross-view information to consider the significant disparities is used to fuse the stereo views. Due to the lack of a benchmark dataset suitable for the SVSR task, we collected a new stereoscopic video dataset, SVSR-Set, containing 71 full high-definition (HD) stereo videos captured using a professional stereo camera. Extensive experiments on the collected dataset, along with two other datasets, demonstrate that the Trans-SVSR can achieve competitive performance compared to the state-of-the-art methods. Project code and additional results are available at https://***/H-deep/Trans-SVSR/.

关键词： Three-dimensional displays Stereo image processing Superresolution Transformers Optical imaging pattern recognition Task analysis

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Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation

Multiple Degradation and Reconstruction Network for Single I...

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

作者： Li, Juncheng Yang, Hanhui Yi, Qiaosi Fang, Faming Gao, Guangwei Zeng, Tieyong Zhang, Guixu Chinese Univ Hong Kong Hong Kong Peoples R China East China Normal Univ Shanghai Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning. However, the proposed methods are often accompanied by plenty of parameters, which greatly limits their application scenarios. Different from previous works that blindly increase the depth of the network, we explore the degradation mechanism of the noisy image and propose a lightweight Multiple Degradation and Reconstruction Network (MDRN) to progressively remove noise. Meanwhile, we propose two novel Heterogeneous Knowledge Distillation Strategies (HMDS) to enable MDRN to learn richer and more accurate features from heterogeneous models, which make it possible to reconstruct higher-quality denoised images under extreme conditions. Extensive experiments show that our MDRN achieves favorable performance against other SID models with fewer parameters. Meanwhile, plenty of ablation studies demonstrate that the introduced HMDS can improve the performance of tiny models or the model under high noise levels, which is extremely useful for related applications.

关键词： Degradation Knowledge engineering Computational modeling Resists pattern recognition Image restoration Noise measurement

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Ante-Hoc Generation of Task-Agnostic Interpretation Maps

Ante-Hoc Generation of Task-Agnostic Interpretation Maps

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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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Emphasizing Complementary Samples for Non-literal Cross-modal Retrieval

Emphasizing Complementary Samples for Non-literal Cross-moda...

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

作者： Thomas, Christopher Kovashka, Adriana Columbia Univ New York NY 10027 USA Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Existing cross-modal retrieval methods assume a straightforward relationship where images and text contain portrayals or mentions of the same objects. In contrast, real-world image-text pairs (e.g. an image and its caption in a news article) often feature more complex relations. Importantly, not all image-text pairs have the same relationship: in some pairs, image and text may be more closely aligned, while others are more loosely aligned hence complementary. In order to ensure the model learns a semantically robust space which captures nuanced relationships, care must be taken that loosely-aligned image-text pairs have a strong enough impact on learning. In this paper, we propose a novel approach to prioritize loosely-aligned samples. Unlike prior sample weighting methods, ours relies on estimating to what extent semantic similarity is preserved in the separate channels (images/text) in the learned multimodal space. In particular, the image-text pair weights in the retrieval loss focus learning towards samples from diverse or discrepant neighborhoods: samples where images or text that were close in a semantic space, are distant in the cross-modal space (diversity), or where neighbor relations are asymmetric (discrepancy). Experiments on three challenging datasets exhibiting abstract image-text relations, as well as COCO, demonstrate significant performance gains compared to recent state-of-the-art models and sample weighting approaches.

关键词： computer vision Spatial diversity conferences Semantics Channel estimation Performance gain Benchmark testing

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Where did I leave my keys? - Episodic-Memory-Based Question Answering on Egocentric Videos

Where did I leave my keys? - Episodic-Memory-Based Question ...

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

作者： Baermann, Leonard Waibel, Alex Karlsruhe Inst Technol Interact Syst Lab Karlsruhe Germany

ISBN: (纸本)9781665487399

Humans have a remarkable ability to organize, compress and retrieve episodic memories throughout their daily life. Current AI systems, however, lack comparable capabilities as they are mostly constrained to an analysis with access to the raw input sequence, assuming an unlimited amount of data storage which is not feasible in realistic deployment scenarios. For instance, existing Video Question Answering (VideoQA) models typically reason over the video while already being aware of the question, thus requiring to store the complete video in case the question is not known in advance. yIn this paper, we address this challenge with three main contributions: First, we propose the Episodic Memory Question Answering (EMQA) task as a specialization of VideoQA. Specifically, EMQA models are constrained to keep only a constant-sized representation of the video input, thus automatically limiting the computation requirements at query time. Second, we introduce a new egocentric VideoQA dataset called QAEGO4D, far larger than existing egocentric VideoQA datasets and featuring video length unprecedented in VideoQA datasets in general. Third, we present extensive experiments on the new dataset, comparing various baseline models in both the VideoQA and the EMQA setting. To facilitate future research on egocentric VideoQA as well as episodic memory representation and retrieval, we publish our code and dataset.

关键词： computer vision Limiting Codes Computational modeling conferences Memory management Question answering (information retrieval)

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Focused Feature Differentiation Network for Image Quality Assessment

Focused Feature Differentiation Network for Image Quality As...

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

作者： He, Gang Wang, Yong Xu, Li Zhang, Wenli Sun, Ming Wen, Xing Xidian Univ Xian Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Image quality assessment (IQA) intended to assess the perceptual quality of images has been an essential problem in both human and machine vision. Recently, with the help of deep neural network (DNN), IQA algorithms can extract more valuable differences between the distorted and reference images than the traditional algorithms, and thus the performance of DNN-based algorithms is more satisfactory than that of previous algorithms. However, the accuracy for different distorted images preference rating of the existing DNN-based quality assessment methods will be decreased when multiple distorted images are quite similar to each other or to the reference image. To tackle this problem, we propose a focused feature differentiation network (FFDN) to highlight the feature maps with greater distorted and reference differentiation. Furthermore, we use the multi-scale feature fusion module to fuse the focused differentiation features at different scale receptive fields. To further improve the accuracy of our method, we predict the mean opinion score and differentiation score by stages and combine them with different self-learning weights. Finally, we convert the weighted score into different image preference degrees. Experimental results on the validation dataset of CLIC2022 and test dataset of CLIC2021 show that the accuracy of our model FFDN is higher than other excellent quality assessment methods.

关键词： Image quality Deep learning Fuses Convolution Machine vision conferences Neural networks

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Learning CLIP Guided Visual-Text Fusion Transformer for Video-based Pedestrian Attribute recognition

Learning CLIP Guided Visual-Text Fusion Transformer for Vide...

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

作者： Zhu, Jun Jin, Jiandong Yang, Zihan Wu, Xiaohao Wang, Xiao Anhui University School of Computer Science and Technology Hefei230601 China Anhui University School of Artificial Intelligence Hefei230601 China

ISBN: (纸本)9798350302493

Existing pedestrian attribute recognition (PAR) algorithms are mainly developed based on a static image. However, the performance is not reliable for images with challenging factors, such as heavy occlusion, motion blur, etc. In this work, we propose to understand human attributes using video frames that can make full use of temporal information. Specifically, we formulate the video-based PAR as a vision-language fusion problem and adopt pre-trained big models CLIP to extract the feature embeddings of given video frames. To better utilize the semantic information, we take the attribute list as another input and transform the attribute words/phrase into the corresponding sentence via split, expand, and prompt. Then, the text encoder of CLIP is utilized for language embedding. The averaged visual tokens and text tokens are concatenated and fed into a fusion Transformer for multi-modal interactive learning. The enhanced tokens will be fed into a classification head for pedestrian attribute prediction. Extensive experiments on a large-scale video-based PAR dataset fully validated the effectiveness of our proposed framework. Both the source code and pre-trained models will be released at https://***/Event-AHU/VTF-PAR. © 2023 ieee.

关键词： Embeddings

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Multi-level Domain Adaptation for Lane Detection

Multi-level Domain Adaptation for Lane Detection

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

作者： Li, Chenguang Zhang, Boheng Shi, Jia Cheng, Guangliang SenseTime Res Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We focus on bridging domain discrepancy in lane detection among different scenarios to greatly reduce extra annotation and re-training costs for autonomous driving. Critical factors hinder the performance improvement of cross-domain lane detection that conventional methods only focus on pixel-wise loss while ignoring shape and position priors of lanes. To address the issue, we propose the Multi-level Domain Adaptation (MLDA) framework, a new perspective to handle cross-domain lane detection at three complementary semantic levels of pixel, instance and category. Specifically, at pixel level, we propose to apply cross-class confidence constraints in self-training to tackle the imbalanced confidence distribution of lane and background. At instance level, we go beyond pixels to treat segmented lanes as instances and facilitate discriminative features in target domain with triplet learning, which effectively rebuilds the semantic context of lanes and contributes to alleviating the feature confusion. At category level, we propose an adaptive inter-domain embedding module to utilize the position prior of lanes during adaptation. In two challenging datasets, i.e. TuSimple and CULane, our approach improves lane detection performance by a large margin with gains of 8.8% on accuracy and 7.4% on F1-score respectively, compared with state-of-the-art domain adaptation algorithms.

关键词： computer vision Costs Lane detection Shape Image edge detection conferences Semantics

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