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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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A Circular-Structured Representation for Visual Emotion Distribution Learning

A Circular-Structured Representation for Visual Emotion Dist...

引用

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

作者： Yang, Jingyuan Li, Jie Li, Leida Wang, Xiumei Gao, Xinbo Xidian Univ Sch Elect Engn Xian Peoples R China Xidian Univ Sch Artificial Intelligence Xian Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing Peoples R China

ISBN: (纸本)9781665445092

Visual Emotion Analysis (VEA) has attracted increasing attention recently with the prevalence of sharing images on social networks. Since human emotions are ambiguous and subjective, it is more reasonable to address VEA in a label distribution learning (LDL) paradigm rather than a single-label classification task. Different from other LDL tasks, there exist intrinsic relationships between emotions and unique characteristics within them, as demonstrated in psychological theories. Inspired by this, we propose a well-grounded circular-structured representation to utilize the prior knowledge for visual emotion distribution learning. To be specific, we first construct an Emotion Circle to unify any emotional state within it. On the proposed Emotion Circle, each emotion distribution is represented with an emotion vector, which is defined with three attributes (i.e., emotion polarity, emotion type, emotion intensity) as well as two properties (i.e., similarity, additivity). Besides, we design a novel Progressive Circular (PC) loss to penalize the dissimilarities between predicted emotion vector and labeled one in a coarse-to-fine manner, which further boosts the learning process in an emotion-specific way. Extensive experiments and comparisons are conducted on public visual emotion distribution datasets, and the results demonstrate that the proposed method outperforms the state-of-the-art methods.

关键词： Visualization computer vision Social networking (online) Psychology pattern recognition Task analysis

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Learned Binary Spectral Shape Descriptor for 3D Shape Correspondence 29

Learned Binary Spectral Shape Descriptor for 3D Shape Corres...

引用

2016 IEEE conference on computer vision and pattern recognition (CVPR)

作者： Xie, Jin Wang, Meng Fang, Yi NYU Multimedia & Visual Comp Lab Abu Dhabi U Arab Emirates New York Univ Abu Dhabi Dept Elect & Comp Engn Abu Dhabi U Arab Emirates NYU Tandon Sch Engn Dept Elect & Comp Engn Brooklyn NY USA

ISBN: (纸本)9781467388511

Dense 3D shape correspondence is an important problem in computer vision and computer graphics. Recently, the local shape descriptor based 3D shape correspondence approaches have been widely studied, where the local shape descriptor is a real-valued vector to characterize the geometrical structure of the shape. Different from these real-valued local shape descriptors, in this paper, we propose to learn a novel binary spectral shape descriptor with the deep neural network for 3D shape correspondence. The binary spectral shape descriptor can require less storage space and enable fast matching. First, based on the eigen-vectors of the Laplace-Beltrami operator, we construct a neural network to form a nonlinear spectral representation to characterize the shape. Then, for the defined positive and negative points on the shapes, we train the constructed neural network by minimizing the errors between the outputs and their corresponding binary descriptors, minimizing the variations of the outputs of the positive points and maximizing the variations of the outputs of the negative points, simultaneously. Finally, we binarize the output of the neural network to form the binary spectral shape descriptor for shape correspondence. The proposed binary spectral shape descriptor is evaluated on the SCAPE and TOSCA 3D shape datasets for shape correspondence. The experimental results demonstrate the effectiveness of the proposed binary shape descriptor for the shape correspondence task.

关键词： Three dimensional computer graphics

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Semi-supervised Learning for Few-shot Image-to-Image Translation

Semi-supervised Learning for Few-shot Image-to-Image Transla...

引用

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

作者： Wang, Yaxing Khan, Salman Gonzalez-Garcia, Abel van de Weijer, Joost Khan, Fahad Shahbaz Univ Autonoma Barcelona Comp Vis Ctr Barcelona Spain Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Linkoping Univ CVL Linkoping Sweden

ISBN: (纸本)9781728171685

In the last few years, unpaired image-to-image translation has witnessed remarkable progress. Although the latest methods are able to generate realistic images, they crucially rely on a large number of labeled images. Recently, some methods have tackled the challenging setting of few-shot image-to-image translation, reducing the labeled data requirements for the target domain during inference. In this work, we go one step further and reduce the amount of required labeled data also from the source domain during training. To do so, we propose applying semi-supervised learning via a noise-tolerant pseudo-labeling procedure. We also apply a cycle consistency constraint to further exploit the information from unlabeled images, either from the same dataset or external. Additionally, we propose several structural modifications to facilitate the image translation task under these circumstances. Our semi-supervised method for few-shot image translation, called SEMIT, achieves excellent results on four different datasets using as little as 10% of the source labels, and matches the performance of the main fully-supervised competitor using only 20% labeled data. Our code and models are made public at: https://***/yaxingwang/SEMIT.

关键词： computer vision

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CAGE: Circumplex Affect Guided Expression Inference

CAGE: Circumplex Affect Guided Expression Inference

引用

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

作者： Wagner, Niklas Maetzler, Felix Vossberg, Samed R. Schneider, Helen Pavlitska, Svetlana Zoellner, J. Marius Karlsruhe Inst Technol KIT Karlsruhe Germany FZI Res Ctr Informat Technol Karlsruhe Germany

ISBN: (纸本)9798350365474

Understanding emotions and expressions is a task of interest across multiple disciplines, especially for improving user experiences. Contrary to the common perception, it has been shown that emotions are not discrete entities but instead exist along a continuum. People understand discrete emotions differently due to a variety of factors, including cultural background, individual experiences, and cognitive biases. Therefore, most approaches to expression understanding, particularly those relying on discrete categories, are inherently biased. In this paper, we present a comparative in-depth analysis of two common datasets (AffectNet and EMOTIC) equipped with the components of the circumplex model of affect. Further, we propose a model for the prediction of facial expressions tailored for lightweight applications. Using a small-scaled MaxViT-based model architecture, we evaluate the impact of discrete expression category labels in training with the continuous valence and arousal labels. We show that considering valence and arousal in addition to discrete category labels helps to significantly improve expression inference. The proposed model outperforms the current state-of-the-art models on AffectNet, establishing it as the best-performing model for inferring valence and arousal achieving a 7% lower RMSE. Training scripts and trained weights to reproduce our results can be found here: https:// ***/wagner-niklas/CAGE_expression_inference.

关键词： computer vision Expression Inference Transformer

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Unbalanced Optimal Transport: A Unified Framework for Object Detection

Unbalanced Optimal Transport: A Unified Framework for Object...

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

作者： De Plaen, Henri De Plaen, Pierre-Francois Suykens, Johan A. K. Proesmans, Marc Tuytelaars, Tinne Van Gool, Luc Katholieke Univ Leuven ESAT STADIUS Leuven Belgium Katholieke Univ Leuven ESAT PSI Leuven Belgium Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9798350301298

During training, supervised object detection tries to correctly match the predicted bounding boxes and associated classification scores to the ground truth. This is essential to determine which predictions are to be pushed towards which solutions, or to be discarded. Popular matching strategies include matching to the closest ground truth box (mostly used in combination with anchors), or matching via the Hungarian algorithm (mostly used in anchor-free methods). Each of these strategies comes with its own properties, underlying losses, and heuristics. We show how Unbalanced Optimal Transport unifies these different approaches and opens a whole continuum of methods in between. This allows for a finer selection of the desired properties. Experimentally, we show that training an object detection model with Unbalanced Optimal Transport is able to reach the state-of-the-art both in terms of Average Precision and Average Recall as well as to provide a faster initial convergence. The approach is well suited for GPU implementation, which proves to be an advantage for large-scale models.

关键词： computer vision theory

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Tracking People by Predicting 3D Appearance, Location and Pose

Tracking People by Predicting 3D Appearance, Location and Po...

引用

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

作者： Rajasegaran, Jathushan Pavlakos, Georgios Kanazawa, Angjoo Malik, Jitendra Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present an approach for tracking people in monocular videos by predicting their fixture 3D representations. To achieve this, we first lift people to 3D from a single frame in a robust manner. This lifting includes information about the 3D pose of the person, their location in the 3D space, and the 3D appearance. As we track a person, we collect 3D observations over time in a tracklet representation. Given the 3D nature of our observations, we build temporal models for each one of the previous attributes. We use these models to predict the future state of the tracklet, including 3D appearance, 3D location, and 3D pose. For a future frame, we compute the similarity between the predicted state of a tracklet and the single frame observations in a probabilistic manner. Association is solved with simple Hungarian matching, and the matches are used to update the respective tracklets. We evaluate our approach on various benchmarks and report state-of-the-art results. Code and models are available at: https: //***/PHALP.

关键词： Solid modeling computer vision Three-dimensional displays Codes Computational modeling Predictive models Benchmark testing

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VCoder: Versatile vision Encoders for Multimodal Large Language Models

VCoder: Versatile Vision Encoders for Multimodal Large Langu...

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

作者： Jain, Jitesh Yang, Jianwei Shi, Humphrey Georgia Tech SHI Labs Atlanta GA 30332 USA Microsoft Res Redmond WA USA Picsart AI Res PAIR Atlanta GA USA

ISBN: (纸本)9798350353006

Humans possess the remarkable skill of Visual Perception, the ability to see and understand the seen, helping them make sense of the visual world and, in turn, reason. Multimodal Large Language Models (MLLM) have recently achieved impressive performance on vision-language tasks ranging from visual question-answering and image captioning to visual reasoning and image generation. However, when prompted to identify or count (perceive) the entities in a given image, existing MLLM systems fail. Working towards developing an accurate MLLM system for perception and reasoning, we propose using Versatile vision enCoders (VCoder) as perception eyes for Multimodal LLMs. We feed the VCoder with perception modalities such as segmentation or depth maps, improving the MLLM's perception abilities. Secondly, we leverage the images from COCO and outputs from off-the-shelf vision perception models to create our COCO Segmentation Text (COST) dataset for training and evaluating MLLMs on the object perception task. Thirdly, we introduce metrics to assess the object perception abilities in MLLMs on our COST dataset. Lastly, we provide extensive experimental evidence proving the VCoder's improved object-level perception skills over existing Multimodal LLMs, including GPT-4V. We open-source our dataset, code, and models to promote research.

关键词： Visual languages

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Uncalibrated Photometric Stereo under Natural Illumination 31

Uncalibrated Photometric Stereo under Natural Illumination

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31st IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Mo, Zhipeng Shi, Boxin Lu, Feng Yeung, Sai-Kit Matsushita, Yasuyuki Singapore Univ Technol & Design Informat Syst Technol & Design Singapore Singapore Peking Univ Sch EECS Natl Engn Lab Video Technol Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn State Key Lab VRTS Beijing Peoples R China Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka Japan

ISBN: (纸本)9781538664209

This paper presents a photometric stereo method that works with unknown natural illuminations without any calibration object. To solve this challenging problem, we propose the use of an equivalent directional lighting model for small surface patches consisting of slowly varying normals, and solve each patch up to an arbitrary rotation ambiguity. Our method connects the resulting patches and unifies the local ambiguities to a global rotation one through angular distance propagation defined over the whole surface. After applying the integrability constraint, our final solution contains only a binary ambiguity, which could be easily removed. Experiments using both synthetic and real-world datasets show our method provides even comparable results to calibrated methods.

关键词： computer vision pattern recognition

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MSTR: Multi-Scale Transformer for End-to-End Human-Object Interaction Detection

MSTR: Multi-Scale Transformer for End-to-End Human-Object In...

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

作者： Kim, Bumsoo Mun, Jonghwan On, Kyoung-Woon Shin, Minchul Lee, Junhyun Kim, Eun-Sol LG AI Res Geumsan South Korea Kakao Brain Seongnam South Korea Korea Univ Seoul South Korea Hanyang Univ Dept Comp Sci Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Human-Object Interaction (HOI) detection is the task of identifying a set of (human, object, interaction) triplets from an image. Recent work proposed transformer encoder-decoder architectures that successfully eliminated the need for many hand-designed components in HOI detection through end-to-end training. However, they are limited to single-scale feature resolution, providing suboptimal performance in scenes containing humans, objects, and their interactions with vastly different scales and distances. To tackle this problem, we propose a Multi-Scale TRansformer (MSTR) for HOI detection powered by two novel HOIaware deformable attention modules called Dual-Entity attention and Entity-conditioned Context attention. While existing deformable attention comes at a huge cost in HOI detection performance, our proposed attention modules of MSTR learn to effectively attend to sampling points that are essential to identify interactions. In experiments, we achieve the new state-of-the-art performance on two HOI detection benchmarks.

关键词： Training Image resolution Semantics Detectors computer architecture Benchmark testing Transformers

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CompletionFormer: Depth Completion with Convolutions and vision Transformers

CompletionFormer: Depth Completion with Convolutions and Vis...

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

作者： Zhang, Youmin Guo, Xianda Poggi, Matteo Zhu, Zheng Huang, Guan Mattoccia, Stefano Univ Bologna Bologna Italy PhiGent Robot Beijing Peoples R China

ISBN: (纸本)9798350301298

Given sparse depths and the corresponding RGB images, depth completion aims at spatially propagating the sparse measurements throughout the whole image to get a dense depth prediction. Despite the tremendous progress of deep-learning-based depth completion methods, the locality of the convolutional layer or graph model makes it hard for the network to model the long-range relationship between pixels. While recent fully Transformer-based architecture has reported encouraging results with the global receptive field, the performance and efficiency gaps to the well-developed CNN models still exist because of its deteriorative local feature details. This paper proposes a Joint Convolutional Attention and Transformer block (JCAT), which deeply couples the convolutional attention layer and vision Transformer into one block, as the basic unit to construct our depth completion model in a pyramidal structure. This hybrid architecture naturally benefits both the local connectivity of convolutions and the global context of the Transformer in one single model. As a result, our Completion-Former outperforms state-of-the-art CNNs-based methods on the outdoor KITTI Depth Completion benchmark and indoor NYUv2 dataset, achieving significantly higher efficiency (nearly 1/3 FLOPs) compared to pure Transformer-based methods. Code is available at https://***/youmi-zym/CompletionFormer.

关键词： 3D from multi-view and sensors

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