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Video Based Computational Coding of Movement Anomalies in ASD Children

Video Based Computational Coding of Movement Anomalies in AS...

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

作者： Singh, Priya Pathak, Abhishek Ganai, Umer Jon Bhushan, Braj Subramanian, Venkatesh K. TCS Res Mumbai Maharashtra India Indian Inst Technol Kanpur Kanpur India Airbase Labs India Pvt Ltd Bengaluru India

ISBN: (纸本)9798350365474

Autism spectrum disorder (ASD) is a neurodevelopmental disorder. Early detection and diagnosis are instrumental in early intervention, yet diagnosis often remains delayed due to the limited availability of clinical practitioners and specialists. We propose a computer vision and Machine Learning based novel framework for quantitative screening of autism spectrum disorder (ASD). This is aimed to minimize the need for trained professionals at the initial stage but not substitute for it. We designed simple activities in consultation with ASD clinical psychologists and therapists for children in the 3-7 years age group that could be performed in their natural environment (home). The temporal features extracted from these activities encode the behavioral differences between Autism Spectrum Disorder (ASD) and Typically Developing (TD) control groups. Due to the unavailability of a public dataset of children performing the designed task, we created our own video dataset of 210 videos taken in unconstrained natural settings. The dataset was collected from a single RGB camera. The proposed vision and learning-based algorithms extract features from the collected data for a comprehensive set of indicators including the visual attention span, name-calling response, neck pose of the subjects, gross motor movement and establish a parametrized automated protocol for early detection without the need to take the subjects out of their natural daily environment. This forestalls the possibility of misperformance by the subject out of nervousness due to unfamiliar surroundings. Results show that our ASD screening methodology can achieve superior performance compared to the single phenotype approaches, and thus has a prognostic value that could be helpful for both clinical and research applications.

关键词： Autism Spectrum Disorder computer vision Early detection of ASD Machine Learning

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Learning to Zoom and Unzoom

Learning to Zoom and Unzoom

引用

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

作者： Thavamani, Chittesh Li, Mengtian Ferroni, Francesco Ramanan, Deva Carnegie Mellon Univ Pittsburgh PA 15213 USA Argo AI Pittsburgh PA USA Waymo Mountain View CA USA Nvidia Santa Clara CA USA

ISBN: (纸本)9798350301298

Many perception systems in mobile computing, autonomous navigation, and AR/VR face strict compute constraints that are particularly challenging for high-resolution input images. Previous works propose nonuniform downsamplers that "learn to zoom" on salient image regions, reducing compute while retaining task-relevant image information. However, for tasks with spatial labels (such as 2D/3D object detection and semantic segmentation), such distortions may harm performance. In this work (LZU), we "learn to zoom" in on the input image, compute spatial features, and then "unzoom" to revert any deformations. To enable efficient and differentiable unzooming, we approximate the zooming warp with a piecewise bilinear mapping that is invertible. LZU can be applied to any task with 2D spatial input and any model with 2D spatial features, and we demonstrate this versatility by evaluating on a variety of tasks and datasets: object detection on Argoverse-HD, semantic segmentation on Cityscapes, and monocular 3D object detection on nuScenes. Interestingly, we observe boosts in performance even when high-resolution sensor data is unavailable, implying that LZU can be used to "learn to upsample" as well. Code and additional visuals are available at https://***/lzu/.

关键词： Efficient and scalable vision

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Efficient On-device Training via Gradient Filtering

Efficient On-device Training via Gradient Filtering

引用

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

作者： Yang, Yuedong Li, Guihong Marculescu, Radu Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350301298

Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI. The problem stems from the large number of operations (e.g., floating point multiplications and additions) and memory consumption required during training by the back-propagation algorithm. Consequently, in this paper, we propose a new gradient filtering approach which enables on-device CNN model training. More precisely, our approach creates a special structure with fewer unique elements in the gradient map, thus significantly reducing the computational complexity and memory consumption of back propagation during training. Extensive experiments on image classification and semantic segmentation with multiple CNN models (e.g., MobileNet, DeepLabV3, UPerNet) and devices (e.g., Raspberry Pi and Jetson Nano) demonstrate the effectiveness and wide applicability of our approach. For example, compared to SOTA, we achieve up to 19x speedup and 77.1% memory savings on ImageNet classification with only 0.1% accuracy loss. Finally, our method is easy to implement and deploy;over 20x speedup and 90% energy savings have been observed compared to highly optimized baselines in MKLDNN and CUDNN on NVIDIA Jetson Nano. Consequently, our approach opens up a new direction of research with a huge potential for on-device training.

关键词： Efficient and scalable vision

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Modernizing Old Photos Using Multiple References via Photorealistic Style Transfer

Modernizing Old Photos Using Multiple References via Photore...

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

作者： Gunawan, Agus Kim, Soo Ye Sim, Hyeonjun Lee, Jae-Ho Kim, Munchurl Korea Adv Inst Sci & Technol Daejeon South Korea Adobe Res San Francisco CA USA ETRI Daejeon South Korea Qualcomm San Diego CA USA

ISBN: (纸本)9798350301298

This paper firstly presents old photo modernization using multiple references by performing stylization and enhancement in a unified manner. In order to modernize old photos, we propose a novel multi-reference-based old photo modernization (MROPM) framework consisting of a network MROPM-Net and a novel synthetic data generation scheme. MROPM-Net stylizes old photos using multiple references via photorealistic style transfer (PST) and further enhances the results to produce modern-looking images. Meanwhile, the synthetic data generation scheme trains the network to effectively utilize multiple references to perform modernization. To evaluate the performance, we propose a new old photos benchmark dataset (CHD) consisting of diverse natural indoor and outdoor scenes. Extensive experiments show that the proposed method outperforms other baselines in performing modernization on real old photos, even though no old photos were used during training. Moreover, our method can appropriately select styles from multiple references for each semantic region in the old photo to further improve the modernization performance.

关键词： Low-level vision

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Crossing the Gap: Domain Generalization for Image Captioning

Crossing the Gap: Domain Generalization for Image Captioning

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

作者： Ren, Yuchen Mao, Zhendong Fang, Shancheng Lu, Yan He, Tong Du, Hao Zhang, Yongdong Ouyang, Wanli Univ Sci & Technol China Hefei Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9798350301298

Existing image captioning methods are under the assumption that the training and testing data are from the same domain or that the data from the target domain (i.e., the domain that testing data lie in) are accessible. However, this assumption is invalid in real-world applications where the data from the target domain is inaccessible. In this paper, we introduce a new setting called Domain Generalization for Image Captioning (DGIC), where the data from the target domain is unseen in the learning process. We first construct a benchmark dataset for DGIC, which helps us to investigate models' domain generalization (DG) ability on unseen domains. With the support of the new benchmark, we further propose a new framework called language-guided semantic metric learning (LSML) for the DGIC setting. Experiments on multiple datasets demonstrate the challenge of the task and the effectiveness of our newly proposed benchmark and LSML framework.

关键词： and reasoning language vision

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High-fidelity 3D Face Generation from Natural Language Descriptions

High-fidelity 3D Face Generation from Natural Language Descr...

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

作者： Wu, Menghua Zhu, Hao Huang, Linjia Zhuang, Yiyu Lu, Yuanxun Cao, Xun Nanjing Univ Nanjing Peoples R China

ISBN: (纸本)9798350301298

Synthesizing high-quality 3D face models from natural language descriptions is very valuable for many applications, including avatar creation, virtual reality, and telepresence. However, little research ever tapped into this task. We argue the major obstacle lies in 1) the lack of high-quality 3D face data with descriptive text annotation, and 2) the complex mapping relationship between descriptive language space and shape/appearance space. To solve these problems, we build DESCRIBE3D dataset, the first large-scale dataset with fine-grained text descriptions for text-to-3D face generation task. Then we propose a two-stage framework to first generate a 3D face that matches the concrete descriptions, then optimize the parameters in the 3D shape and texture space with abstract description to refine the 3D face model. Extensive experimental results show that our method can produce a faithful 3D face that conforms to the input descriptions with higher accuracy and quality than previous methods. The code and DESCRIBE3D dataset are released at https://***/zhuhao-nju/describe3d.

关键词： and reasoning language vision

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ABLE-NeRF: Attention-Based Rendering with Learnable Embeddings for Neural Radiance Field

ABLE-NeRF: Attention-Based Rendering with Learnable Embeddin...

引用

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

作者： Tang, Zhe Jun Cham, Tat-Jen Zhao, Haiyu Nanyang Technol Univ S Lab Singapore Singapore Nanyang Technol Univ Singapore Singapore SenseTime Res Singapore Singapore

ISBN: (纸本)9798350301298

Neural Radiance Field (NeRF) is a popular method in representing 3D scenes by optimising a continuous volumetric scene function. Its large success which lies in applying volumetric rendering (VR) is also its Achilles' heel in producing view-dependent effects. As a consequence, glossy and transparent surfaces often appear murky. A remedy to reduce these artefacts is to constrain this VR equation by excluding volumes with back-facing normal. While this approach has some success in rendering glossy surfaces, translucent objects are still poorly represented. In this paper, we present an alternative to the physics-based VR approach by introducing a self-attention-based framework on volumes along a ray. In addition, inspired by modern game engines which utilise Light Probes to store local lighting passing through the scene, we incorporate Learnable Embeddings to capture view dependent effects within the scene. Our method, which we call ABLE-NeRF, significantly reduces 'blurry' glossy surfaces in rendering and produces realistic translucent surfaces which lack in prior art. In the Blender dataset, ABLE-NeRF achieves SOTA results and surpasses Ref-NeRF in all 3 image quality metrics PSNR, SSIM, LPIPS.

关键词： vision + graphics

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TINC: Tree-structured Implicit Neural Compression

TINC: Tree-structured Implicit Neural Compression

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

作者： Yang, Runzhao Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9798350301298

Implicit neural representation (INR) can describe the target scenes with high fidelity using a small number of parameters, and is emerging as a promising data compression technique. However, limited spectrum coverage is intrinsic to INR, and it is non-trivial to remove redundancy in diverse complex data effectively. Preliminary studies can only exploit either global or local correlation in the target data and thus of limited performance. In this paper, we propose a Tree-structured Implicit Neural Compression (TINC) to conduct compact representation for local regions and extract the shared features of these local representations in a hierarchical manner. Specifically, we use Multi-Layer Perceptrons (MLPs) to fit the partitioned local regions, and these MLPs are organized in tree structure to share parameters according to the spatial distance. The parameter sharing scheme not only ensures the continuity between adjacent regions, but also jointly removes the local and non-local redundancy. Extensive experiments show that TINC improves the compression fidelity of INR, and has shown impressive compression capabilities over commercial tools and other deep learning based methods. Besides, the approach is of high flexibility and can be tailored for different data and parameter settings. The source code can be found at https://***/RichealYoung/TINC.

关键词： cell microscopy Medical and biological vision

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Rapid 3D Model Generation with Intuitive 3D Input

Rapid 3D Model Generation with Intuitive 3D Input

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

作者： Chen, Tianrun Ding, Chaotao Zhang, Shangzhan Yu, Chunan Zang, Ying Li, Zejian Peng, Sida Sun, Lingyun Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Huzhou Univ Sch Informat Engn Huzhou Peoples R China Zhejiang Univ Sch Software Technol Hangzhou Peoples R China Moxin Huzhou Technol Co Ltd KOKONI3D Huzhou Peoples R China

ISBN: (纸本)9798350353006

With the emergence of AR/VR, 3D models are in tremendous demand. However, conventional 3D modeling with computer-Aided Design software requires much expertise and is difficult for novice users. We find that AR/VR devices, in addition to serving as effective display mediums, can offer a promising potential as an intuitive 3D model creation tool, especially with the assistance of AI generative models. Here, we propose Deep3DVRSketch, the first 3D model generation network that inputs 3D VR sketches from novice users and generates highly consistent 3D models in multiple categories within seconds, irrespective of the users' drawing abilities. We also contribute KO3D+, the largest 3D sketch-shape dataset. Our method pre-trains a conditional diffusion model on quality 3D data, then fine-tunes an encoder to map 3D sketches onto the generator's manifold using an adaptive curriculum strategy for limited ground truths. In our experiment, our approach achieves state-of- the-art performance in both model quality and fidelity with real-world input from novice users, and users can even draw and obtain very detailed geometric structures. In our user study, users were able to complete the 3D modeling tasks over 10 times faster using our approach compared to conventional CAD software tools. We believe that our Deep3DVRSketch and KO3D+ dataset can offer a promising solution for future 3D modeling in metaverse era. Check the project page at http://***/Deep3DVRSketch.

关键词： 3D models 3D vision generative models

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StructVPR: Distill Structural Knowledge with Weighting Samples for Visual Place recognition

StructVPR: Distill Structural Knowledge with Weighting Sampl...

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

作者： Shen, Yanqing Zhou, Sanping Fu, Jingwen Wang, Ruotong Chen, Shitao Zheng, Nanning Xi An Jiao Tong Univ Natl Key Lab Human Machine Hybrid Augmented Intel Natl Engn Res Ctr Visual Informat & Applicat Inst Artificial Intelligence & Robot Xian Peoples R China

ISBN: (纸本)9798350301298

Visual place recognition (VPR) is usually considered as a specific image retrieval problem. Limited by existing training frameworks, most deep learning-based works cannot extract sufficiently stable global features from RGB images and rely on a time-consuming re-ranking step to exploit spatial structural information for better performance. In this paper, we propose StructVPR, a novel training architecture for VPR, to enhance structural knowledge in RGB global features and thus improve feature stability in a constantly changing environment. Specifically, StructVPR uses segmentation images as a more definitive source of structural knowledge input into a CNN network and applies knowledge distillation to avoid online segmentation and inference of seg-branch in testing. Considering that not all samples contain high-quality and helpful knowledge, and some even hurt the performance of distillation, we partition samples and weigh each sample's distillation loss to enhance the expected knowledge precisely. Finally, StructVPR achieves impressive performance on several benchmarks using only global retrieval and even outperforms many two-stage approaches by a large margin. After adding additional re-ranking, ours achieves state-of-the-art performance while maintaining a low computational cost.

关键词： detection recognition: Categorization retrieval

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