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Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression recognition

Coarse-to-Fine Cascaded Networks with Smooth Predicting for ...

引用

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

作者： Xue, Fanglei Tan, Zichang Zhu, Yu Ma, Zhongsong Guo, Guodong Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing Peoples R China Baidu Res Inst Deep Learning Beijing Peoples R China Natl Engn Lab Deep Learning Technol & Applicat Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Facial expression recognition plays an important role in human-computer interaction. In this paper, we propose the Coarse-to-Fine Cascaded network with Smooth Predicting (CFC-SP) to improve the performance of facial expression recognition. CFC-SP contains two core components, namely Coarse-to-Fine Cascaded networks (CFC) and Smooth Predicting (SP). For CFC, it first groups several similar emotions to form a rough category, and then employs a network to conduct a coarse but accurate classification. Later, an additional network for these grouped emotions is further used to obtain fine-grained predictions. For SP, it improves the recognition capability of the model by capturing both universal and unique expression features. To be specific, the universal features denote the general characteristic of facial emotions within a period and the unique features denote the specific characteristic at this moment. Experiments on Aff-Wild2 show the effectiveness of the proposed CFSP. We achieved 3rd place in the Expression Classification Challenge of the 3rd Competition on Affective Behavior Analysis in-the-wild. The code will be released at https://***/BR-IDL/PaddleViT.

关键词： Human computer interaction computer vision Codes Face recognition conferences Behavioral sciences

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Large-Scale Bidirectional Training for Zero-Shot Image Captioning

Large-Scale Bidirectional Training for Zero-Shot Image Capti...

引用

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

作者： Kim, Taehoon Marsden, Mark Ahn, Pyunghwan Kim, Sangyun Lee, Sihaeng Sala, Alessandra Kim, Seung Hwan LG AI Res Seoul South Korea Shutterstock New York NY USA

ISBN: (纸本)9798350365474

When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To improve performance, pretraining-and-finetuning has been a key strategy for image captioning. However, we find that large-scale bidirectional training between image and text enables zero-shot image captioning. In this paper, we introduce Bidirectional Image Text Training in largER Scale, BITTERS, an efficient training and inference framework for zero-shot image captioning. We also propose a new evaluation benchmark which comprises of high quality datasets and an extensive set of metrics to properly evaluate zero-shot captioning accuracy and societal bias. We additionally provide an efficient finetuning approach for keyword extraction. We show that careful selection of large-scale training set and model architecture is the key to achieving zero-shot image captioning.

关键词： image captioning large-scale multimodal transformers vision-language zero-shot

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Guiding Attention using Partial-Order Relationships for Image Captioning

Guiding Attention using Partial-Order Relationships for Imag...

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

作者： Popattia, Murad Rafi, Muhammad Qureshi, Rizwan Nawaz, Shah Natl Univ Comp & Emerging Sci Karachi Pakistan Hamad Bin Khalifa Univ Doha Qatar Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Genoa Italy DESY Hamburg Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The use of attention models for automated image captioning has enabled many systems to produce accurate and meaningful descriptions for images. Over the years, many novel approaches have been proposed to enhance the attention process using different feature representations. In this paper, we extend this approach by creating a guided attention network mechanism, that exploits the relationship between the visual scene and text-descriptions using spatial features from the image, high-level information from the topics, and temporal context from caption generation, which are embedded together in an ordered embedding space. A pairwise ranking objective is used for training this embedding space which allows similar images, topics and captions in the shared semantic space to maintain a partial order in the visual-semantic hierarchy and hence, helps the model to produce more visually accurate captions. The experimental results based on MSCOCO dataset shows the competitiveness of our approach, with many state-of-the-art models on various evaluation metrics.

关键词： Training Measurement Visualization computer vision conferences Semantics computer architecture

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Leveraging Multi scale Backbone with Multilevel supervision for Thermal Image Super Resolution

Leveraging Multi scale Backbone with Multilevel supervision ...

引用

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

作者： Nathan, Sabari Kansal, Priya Couger Inc Shibuya Ku Tokyo Japan

ISBN: (纸本)9781665448994

This paper proposes an attention-based multi-level model with a multi-scale backbone for thermal image super-resolution. The model leverages the multi-scale backbone as well. The thermal image dataset is provided by PBVS 2020 in their thermal image super-resolution challenge. This dataset contains the images with three different resolution scales(low, medium, high) [1]. However, only the medium and high-resolution images are used to train the proposed architecture to generate the super-resolution images in x2, x4 scales. The proposed architecture is based on the Res2net blocks as the backbone of the network. Along with this, the coordinate convolution layer and dual attention are also used in the architecture. Further, multi-level supervision is implemented to supervise the output image resolution similarity with the real image at each block during training. To test the robustness of the proposed model, we evaluated our model on the Thermal-6 dataset [20]. The results show that our model is efficient to achieve state-of-the-art results on the PBVS dataset. Further the results on the Thermal-6 dataset show that the model has a decent generalization capacity.

关键词： Training Convolution conferences Superresolution computer architecture Robustness pattern recognition

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PhoneDepth: A Dataset for Monocular Depth Estimation on Mobile Devices

PhoneDepth: A Dataset for Monocular Depth Estimation on Mobi...

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

作者： Benavides, Fausto Tapia Ignatov, Andrey Timofte, Radu Swiss Fed Inst Technol Zurich Switzerland JMU Wurzburg Wurzburg Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Monocular depth estimation has been studied as a classic and learning based computer vision problem for decades. However, little attention received the efficiency and the deployment of methods on mobile hardware. All publicly available datasets have severe limitations related to their applicability to camera data captured with real mobile devices. For instance, the main issues with current datasets include (but not exhaustively) low quality of images due the cameras or collection methods, domain specifically generated datasets as is the case for autonomous driving, small number of samples, sparse depthmaps, etc. In response, we introduce PhoneDepth, a novel dataset that aims to take advantage of modern phones hardware and professional stereo cameras. Depthmaps are acquired from three sources: Time of Flight sensor, Dual Pixel sensor and stereo camera;while the images correspond to mobile phone photos. We prove its high value by training neural networks with multiple depth supervision, fine-tuning on other datasets and for depth refinement. Along with the dataset we present benchmark models and a toolbox to facilitate the dataset usage.

关键词： Training computer vision Three-dimensional displays Estimation Cameras Mobile handsets Hardware

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CORE: Consistent Representation Learning for Face Forgery Detection

CORE: Consistent Representation Learning for Face Forgery De...

引用

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

作者： Ni, Yunsheng Meng, Depu Yu, Changqian Quan, Chengbin Ren, Dongchun Zhao, Youjian Tsinghua Univ Beijing Peoples R China Univ Sci & Technol China Hefei Anhui Peoples R China Meituan Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Face manipulation techniques develop rapidly and arouse widespread public concerns. Despite that vanilla convolutional neural networks achieve acceptable performance, they suffer from the overfitting issue. To relieve this issue, there is a trend to introduce some erasing-based augmentations. We find that these methods indeed attempt to implicitly induce more consistent representations for different augmentations via assigning the same label for different augmented images. However, due to the lack of explicit regularization, the consistency between different representations is less satisfactory. Therefore, we constrain the consistency of different representations explicitly and propose a simple yet effective framework, COnsistent REpresentation Learning (CORE). Specifically, we first capture the different representations with different augmentations, then regularize the cosine distance of the representations to enhance the consistency. Extensive experiments (in-dataset and cross-dataset) demonstrate that CORE performs favorably against state-of-the-art face forgery detection methods. Our code is available at https://***/niyunsheng/CORE.

关键词： Representation learning computer vision Codes Face recognition conferences Market research Forgery

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Visual Domain Bridge: A source-free domain adaptation for cross-domain few-shot learning

Visual Domain Bridge: A source-free domain adaptation for cr...

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

作者： Yazdanpanah, Moslem Moradi, Parham Univ Kurdistan Erbil Iraq

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Due to the covariate shift, deep neural networks performance always degrades when applied to novel domains. In order to mitigate this problem, domain adaptation techniques require samples from target data during the feature extraction training, which is not always applicable in real-world scenarios. Batch Normalization is a known component of computer vision models, aiming at reducing the training-time covariate shift. However, facing distribution shift results in an internal state mismatch inside the Batch-Norm layers during the inference time. In favor of alleviating the induced mismatch, this paper proposes a source-free, lightweight and straightforward approach by introducing the "Visual Domain Bridge" concept reducing the BatchNorm's internal mismatch in the cross-domain settings. Compared to the other BatchNorm-based source-free domain adaptation techniques such as AdaBN and Prediction-BN, our method formed a new state-of-the-art cross-domain few-shot fine-tuning method neglecting extra augmentations;while improving the performance in near-domain settings too. The proposed method can integrate with other domain adaptation methods and enhance their performance requiring just a few lines of modification in the BatchNorm's implementation. Implementations are available in https://***/MosyMosy/VDB

关键词： Bridges Training Deep learning Visualization computer vision conferences Neural networks

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Can we trust bounding box annotations for object detection?

Can we trust bounding box annotations for object detection?

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

作者： Murrugarra-Llerena, Jeffri Kirsten, L. N. Jung, Claudio R. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Object detection is a classical problem in computer vision, and the vast majority of approaches require large annotated datasets for training and evaluation purposes. The most popular representations are bounding boxes (BBs), usually defined as the minimal-area rectangle that encompasses the whole object region. However, the annotation process presents some subjectiveness (particularly when occlusions are present), and its quality might get degraded when the annotators get tired. Comparing BBs is crucial for evaluation purposes, and the Intersection-over-Union (IoU) is the standard similarity metric. In this paper, we provide theoretical and experimental results indicating that the IoU can be strongly affected even by small annotation discrepancies in popular datasets used for object detection. As a consequence, the Average Precision (AP) value commonly used to evaluate object detectors is also influenced by annotation bias or noise, particularly for small objects and tighter IoU thresholds.

关键词： Degradation Training computer vision Annotations Object detection Detectors Size measurement

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3D Object Class Detection in the Wild

3D Object Class Detection in the Wild

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IEEE conference on computer vision and pattern recognition (CVPR)

作者： Pepik, Bojan Stark, Michael Gehler, Peter Ritschel, Tobias Schiele, Bernt Max Planck Inst Informat Saarbrucken Germany Max Planck Inst Intelligent Syst Stuttgart Germany

ISBN: (纸本)9781467367592

Object class detection has been a synonym for 2D bounding box localization for the longest time, fueled by the success of powerful statistical learning techniques, combined with robust image representations. Only recently, there has been a growing interest in revisiting the promise of computer vision from the early days: to precisely delineate the contents of a visual scene, object by object, in 3D. In this paper, we draw from recent advances in object detection and 2D-3D object lifting in order to design an object class detector that is particularly tailored towards 3D object class detection. Our 3D object class detection method consists of several stages gradually enriching the object detection output with object viewpoint, keypoints and 3D shape estimates. Following carefid design, in each stage it constantly improves the performance and achieves state-of-the-art performance in simultaneous 2D bounding box and viewpoint estimation on the challenging Pascal-3D+ dataset.

关键词： object class Object detection image representation images computer vision bounding boxes Three Dimensions Synonyms

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Cross Transferring Activity recognition to Word Level Sign Language Detection

Cross Transferring Activity Recognition to Word Level Sign L...

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

作者： Radhakrishnan, Srijith Mohan, Nikhil C. Varma, Manisimha Varma, Jaithra Pai, Smitha N. Manipal Acad Higher Educ Manipal Inst Technol Dept Informat & Commun Technol Manipal 576104 Karnataka India Manipal Acad Higher Educ Manipal Inst Technol Dept Comp Sci & Engn Manipal 576104 Karnataka India Manipal Acad Higher Educ Manipal Inst Technol Dept Data Sci & Comp Applicat Manipal 576104 Karnataka India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The lack of large scale labelled datasets in word-level sign language recognition (WSLR) poses a challenge to detecting sign language from videos. Most WSLR approaches operate on datasets that do not model real-world settings very well, as they do not have a high degree of variability in terms of signers, background, lighting and inter signer variation. We chose the MS-ASL dataset to overcome these limitations as they model open-world settings very well. This paper benchmarks successful action recognition architectures on the MS-ASL dataset using transfer learning. We have achieved new state-of-the-art accuracy (92.35%) with an improvement of 7.03% over the previous state-of-the-art introduced by the MS-ASL paper. We have analyzed how action-recognition architectures fair in the task of WSLR, and we propose SlowFast 8x8 ResNet 101 as a robust and suitable architecture for the task of WSLR.

关键词： computer vision conferences Transfer learning Lighting Gesture recognition computer architecture Assistive technologies

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