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GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing

GPT as Psychologist? Preliminary Evaluations for GPT-4V on V...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hao Lu Xuesong Niu Jiyao Wang Yin Wang Qingyong Hu Jiaqi Tang Yuting Zhang Kaishen Yuan Bin Huang Zitong Yu Dengbo He Shuiguang Deng Hao Chen Yingcong Chen Shiguang Shan The Hong Kong University of Science & Technology (Guangzhou) The Hong Kong University of Science & Technology Beijing Institute for General Artificial Intelligence Zhejiang University Great Bay University Hangzhou Research Institute Beihang University Chinese Academy of Sciences Institute of Computing Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. Despite its success in language understanding, it is critical to evaluate the performance of downstream tasks for better human-centric applications. This paper assesses the application of MLLMs with 5 crucial abilities for affective computing, spanning from visual affective tasks and reasoning tasks. The results show that GPT-4V has high accuracy in facial action unit recognition and micro-expression detection while its general facial expression recognition performance is not accurate. We also highlight the challenges of achieving fine-grained micro-expression recognition and the potential for further study and demonstrate the versatility and potential of GPT-4V for handling advanced tasks in emotion recognition and related fields by integrating with task-related agents for more complex tasks, such as heart rate estimation through signal processing. In conclusion, this paper provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing. Our interesting examples are at https://***/Envision-Research/GPT4Affectivity.

关键词： Heart rate Emotion recognition Visualization computer vision Affective computing Accuracy Face recognition

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The 5th AI City Challenge

The 5th AI City Challenge

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

作者： Naphade, Milind Wang, Shuo Anastasiu, David C. Tang, Zheng Chang, Ming-Ching Yang, Xiaodong Yao, Yue Zheng, Liang Chakraborty, Pranamesh Sharma, Anuj Feng, Qi Ablavsky, Vitaly Sclaroff, Stan NVIDIA Corp Santa Clara CA 95051 USA Santa Clara Univ Santa Clara CA 95053 USA SUNY Albany Albany NY 12222 USA Australian Natl Univ Canberra ACT Australia Indian Inst Technol Kanpur Kanpur Uttar Pradesh India Iowa State Univ Ames IA USA Boston Univ Boston MA 02215 USA Univ Washington Seattle WA 98195 USA Amazon Seattle WA USA QCraft Santa Clara CA USA

ISBN: (纸本)9781665448994

The AI City Challenge was created with two goals in mind: (1) pushing the boundaries of research and development in intelligent video analysis for smarter cities use cases, and (2) assessing tasks where the level of performance is enough to cause real-world adoption. Transportation is a segment ripe for such adoption. The fifth AI City Challenge attracted 305 participating teams across 38 countries, who leveraged city-scale real traffic data and high-quality synthetic data to compete in five challenge tracks. Track 1 addressed video-based automatic vehicle counting, where the evaluation being conducted on both algorithmic effectiveness and computational efficiency. Track 2 addressed city-scale vehicle re-identification with augmented synthetic data to substantially increase the training set for the task. Track 3 addressed city-scale multi-target multi-camera vehicle tracking. Track 4 addressed traffic anomaly detection. Track 5 was a new track addressing vehicle retrieval using natural language descriptions. The evaluation system shows a general leader board of all submitted results, and a public leader board of results limited to the contest participation rules, where teams are not allowed to use external data in their work. The public leader board shows results more close to real-world situations where annotated data is limited. Results show the promise of AI in Smarter Transportation. State-of-the-art performance for some tasks shows that these technologies are ready for adoption in real-world systems.

关键词： Training computer vision Three-dimensional displays Target tracking Tracking Urban areas Transportation

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Enforcing Conditional Independence for Fair Representation Learning and Causal Image Generation

Enforcing Conditional Independence for Fair Representation L...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jensen Hwa Qingyu Zhao Aditya Lahiri Adnan Masood Babak Salimi Ehsan Adeli Stanford University Weill Cornell Medicine University of California San Diego UST

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Conditional independence (CI) constraints are critical for defining and evaluating fairness in machine learning, as well as for learning unconfounded or causal representations. Traditional methods for ensuring fairness either blindly learn invariant features with respect to a protected variable (e.g., race when classifying sex from face images) or enforce CI relative to the protected attribute only on the model output (e.g., the sex label). Neither of these methods are effective in enforcing CI in high-dimensional feature spaces. In this paper, we focus on a nascent approach characterizing the CI constraint in terms of two Jensen-Shannon divergence terms, and we extend it to high-dimensional feature spaces using a novel dynamic sampling strategy. In doing so, we introduce a new training paradigm that can be applied to any encoder architecture. We are able to enforce conditional independence of the diffusion autoencoder latent representation with respect to any protected attribute under the equalized odds constraint and show that this approach enables causal image generation with controllable latent spaces. Our experimental results demonstrate that our approach can achieve high accuracy on downstream tasks while upholding equality of odds.

关键词： Training Representation learning computer vision Image synthesis conferences computer architecture Predictive models

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DECNet: A Non-Contacting Dual-Modality Emotion Classification Network for Driver Health Monitoring

DECNet: A Non-Contacting Dual-Modality Emotion Classificatio...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Zhekang Dong Chenhao Hu Shiqi Zhou Liyan Zhu Junfan Wang Yi Chen Xudong Lv Xiaoyue Ji Hangzhou Dianzi University Zhejiang Provincial Key Laboratory of Equipment Electronics Zhejiang University Tsinghua University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Negative emotions have been identified as significant factors influencing driver behavior, easily leading to extremely serious traffic accidents. Hence, there is a pressing need to develop an automatic emotion classification method for driver health monitoring and road safety improvement. Most of the existing methods predominantly focus on single modalities, resulting in suboptimal classification performance due to the underutilization of heterogeneous information. In this work, we propose a novel non-contacting dual-modality driver emotion classification network (DECNet) to address these limitations. DECNet consists of three key modules: 1) facial video modality processing module; 2) driving behavior modality processing module; 3) fusion decision module. Meanwhile, we introduce a combined multi-task learning strategy within DECNet to improve the efficacy in the driver emotion classification task. To evaluate the effectiveness of the proposed DECNet, we conducted experiments on the PPB-Emo dataset, the experimental results showcase the superiority in terms of accuracy (⩾ 6.12% Acc-7) and F1-score (⩾ 7.25% F1-7) compared to existing state-of-the art methods. The model and code will be available at https://***/fqfqngxhs/***

关键词： Smart cities Pressing Feature extraction Multitasking Road safety Safety pattern recognition

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DSTCFuse: A Method based on Dual-cycled Cross-awareness of Structure Tensor for Semantic Segmentation via Infrared and Visible Image Fusion

DSTCFuse: A Method based on Dual-cycled Cross-awareness of S...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xuan Li Rongfu Chen Jie Wang Lei Ma Li Cheng Haiwen Yuan School of Electrical and Information Engineering Wuhan Institute of Technology Hubei Key Laboratory of Optical Information and Pattern Recognition

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-modality information fusion can compensate deficiencies of single modality and provide rich scene information for 2D semantic segmentation. However, the inconsistency in the feature space between different modalities may lead to poor presentation of objects and that would affect subsequent segmented effectiveness. The idea of modal transition can reduce the modal differences and avoid biased processing during the fusion process, but it is hard to perfectly retain the contents of the source images. To address these challenges, a fusion method based on dual-cycled cross-awareness of structure tensor is proposed. Firstly, we propose a dual-cycle modality transition network based on cross-awareness consistency to learn the differences in feature space from different modalities. Secondly, a set of globally structure-tensor preserving modules are designed to enhance the capabilities of the network in capturing complementary features and perceiving global modal consistency. Under the joint constraint of globally structure-tensor awareness loss and cross-awareness loss, our network achieves a robust mapping of feature space from visible to pseudo-infrared images without relying on Ground-Truth. Finally, the pseudo-infrared images that inherit the superior qualities of two modalities are fused with the original infrared images directly, which effectively reduces the complexity of fusion. Extensive comparative experiments show that our method outperforms state-of-the-art methods in qualitative and quantitative evaluation.

关键词： Degradation computer vision Tensors Semantic segmentation conferences Interference pattern recognition

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Fake it to make it: Using synthetic data to remedy the data shortage in joint multi-modal speech-and-gesture synthesis

Fake it to make it: Using synthetic data to remedy the data ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Shivam Mehta Anna Deichler Jim O’Regan Birger Moëll Jonas Beskow Gustav Eje Henter Simon Alexanderson KTH Royal Institute of Technology Sweden Motorica AB Sweden

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Although humans engaged in face-to-face conversation simultaneously communicate both verbally and non-verbally, methods for joint and unified synthesis of speech audio and co-speech 3D gesture motion from text are a new and emerging field. These technologies hold great promise for more human-like, efficient, expressive, and robust synthetic communication, but are currently held back by the lack of suitably large datasets, as existing methods are trained on parallel data from all constituent modalities. Inspired by student-teacher methods, we propose a straightforward solution to the data shortage, by simply synthesising additional training material. Specifically, we use uni-modal synthesis models trained on large datasets to create multi-modal (but synthetic) parallel training data, and then pre-train a joint synthesis model on that material. In addition, we propose a new synthesis architecture that adds better and more controllable prosody modelling to the state-of-the-art method in the field. Our results confirm that pre-training on large amounts of synthetic data improves the quality of both the speech and the motion synthesised by the multi-modal model, with the proposed architecture yielding further benefits when pre-trained on the synthetic data.

关键词： Training computer vision Three-dimensional displays conferences Training data computer architecture Oral communication

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Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition

Bracketing Image Restoration and Enhancement with High-Low F...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Genggeng Chen Kexin Dai Kangzhen Yang Tao Hu Xiangyu Chen Yongqing Yang Wei Dong Peng Wu Yanning Zhang Qingsen Yan Xi’an University of Architecture and Technology Northwestern Polytechnical University University of Macau Xi’an Institute of Optics and Precision Mechanics of CAS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In real-world scenarios, due to a series of image degradations, obtaining high-quality, clear content photos is challenging. While significant progress has been made in synthesizing high-quality images, previous methods for image restoration and enhancement often overlooked the characteristics of different degradations. They applied the same structure to address various types of degradation, resulting in less-than-ideal restoration outcomes. Inspired by the notion that high/low frequency information is applicable to different degradations, we introduce HLNet, a Bracketing Image Restoration and Enhancement method based on high-low frequency decomposition. Specifically, we employ two modules for feature extraction: shared weight modules and non-shared weight modules. In the shared weight modules, we use SCConv to extract common features from different degradations. In the non-shared weight modules, we introduce the High-Low Frequency Decomposition Block (HLFDB), which employs different methods to handle high-low frequency information, enabling the model to address different degradations more effectively. Compared to other networks, our method takes into account the characteristics of different degradations, thus achieving higher-quality image restoration.

关键词： Degradation computer vision conferences Feature extraction Image restoration pattern recognition Image enhancement

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Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal Estimation for 3D Scene Understanding

Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jay Bhanushali Manivannan Muniyandi Praneeth Chakravarthula Indian Institute of Technology Madras UNC Chapel Hill

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present a cross-domain inference technique that learns from synthetic data to estimate depth and normals for in-the-wild omnidirectional 3D scenes encountered in real-world uncontrolled settings. To this end, we introduce UBotNet, an architecture that combines UNet and Bottleneck Transformer elements to predict consistent scene normals and depth. We also introduce the OmniHorizon synthetic dataset containing 24,335 omnidirectional images that represent a wide variety of outdoor environments, including buildings, streets, and diverse vegetation. This dataset is generated from expansive, lifelike virtual spaces and encompasses dynamic scene elements, such as changing lighting conditions, different times of day, pedestrians, and vehicles. Our experiments show that UBotNet achieves significantly improved accuracy in depth estimation and normal estimation compared to existing models. Lastly, we validate cross-domain synthetic-to-real depth and normal estimation on real outdoor images using UBot-Net trained solely on our synthetic OmniHorizon dataset, demonstrating the potential of both the synthetic dataset and the proposed network for real-world scene understanding applications. The dataset and accompanying code are available at ***.

关键词： Space vehicles Three-dimensional displays Pedestrians Accuracy Estimation Vegetation mapping computer architecture

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Image-caption difficulty for efficient weakly-supervised object detection from in-the-wild data

Image-caption difficulty for efficient weakly-supervised obj...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Giacomo Nebbia Adriana Kovashka University of Pittsburgh

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, we have witnessed the collection of larger and larger multi-modal, image-caption datasets: from hundreds of thousands such pairs to hundreds of millions. Such datasets allow researchers to build powerful deep learning models, at the cost of requiring intensive computational resources. In this work, we ask: can we use such datasets efficiently without sacrificing performance? We tackle this problem by extracting difficulty scores from each image-caption sample, and by using such scores to make training more effective and efficient. We compare two ways to use difficulty scores to influence training: filtering a representative subset of each dataset and ordering samples through curriculum learning. We analyze and compare difficulty scores extracted from a single modality—captions (i.e., caption length and number of object mentions) or images (i.e., region proposals’ size and number)—or based on alignment of image-caption pairs (i.e., CLIP and concreteness). We focus on Weakly-Supervised Object Detection where image-level labels are extracted from captions. We discover that (1) combining filtering and curriculum learning can achieve large gains in performance, but not all methods are stable across experimental settings, (2) singlemodality scores often outperform alignment-based ones, (3) alignment scores show the largest gains when training time is limited.

关键词： Training Deep learning computer vision Costs Filtering conferences Computational modeling

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NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results

NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset,...

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

作者： Perez-Pellitero, Eduardo Catley-Chandar, Sibi Leonardis, Ales Timofte, Radu Wang, Xian Li, Yong Wang, Tao Song, Fenglong Liu, Zhen Lin, Wenjie Li, Xinpeng Rao, Qing Jiang, Ting Han, Mingyan Fan, Haoqiang Sun, Jian Liu, Shuaicheng Chen, Xiangyu Liu, Yihao Zhang, Zhengwen Qiao, Yu Dong, Chao Chee, Evelyn Yi Lyn Shen, Shanlan Duan, Yubo Chen, Guannan Sun, Mengdi Gao, Yan Zhang, Lijie Akhil, K. A. Jiji, C., V Sharif, S. M. A. Naqvi, Rizwan Ali Biswas, Mithun Kim, Sungjun Xia, Chenjie Zhao, Bowen Ye, Zhangyu Lu, Xiwen Cao, Yanpeng Yang, Jiangxin Cao, Yanlong Rosh, Green K. S. Lomte, Sachin Deepak Krishnan, Nikhil Prasad, B. H. Pawan Huawei Noahs Ark Lab Shenzhen Peoples R China Megvii Technol Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Black Sesame Technol Singapore Singapore BOE Technol Co Ltd Beijing Peoples R China Coll Engn Trivandrum Trivandrum Kerala India Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou Peoples R China Zhejiang Univ Key Lab Adv Mfg Technol Hangzhou Peoples R China Sejong Univ Seoul South Korea Rigel IT Dhaka Bangladesh Samsung R&D Inst India Bangalore SRI B Bangalore Karnataka India

ISBN: (纸本)9781665448994

This paper reviews the first challenge on high-dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2021. This manuscript focuses on the newly introduced dataset, the proposed methods and their results. The challenge aims at estimating a HDR image from one or multiple respective low-dynamic range (LDR) observations, which might suffer from underor over-exposed regions and different sources of noise. The challenge is composed by two tracks: In Track 1 only a single LDR image is provided as input, whereas in Track 2 three differently-exposed LDR images with inter-frame motion are available. In both tracks, the ultimate goal is to achieve the best objective HDR reconstruction in terms of PSNR with respect to a ground-truth image, evaluated both directly and with a canonical tonemapping operation.

关键词： computer vision Tracking conferences Imaging Dynamic range Market research pattern recognition

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