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检索条件"任意字段=2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020"

共 11281 条记录，以下是501-510 订阅

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Using Language-Aligned Gesture Embeddings for Understanding Gestures Accompanying Math Terms

Using Language-Aligned Gesture Embeddings for Understanding ...

引用

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

作者： Maidment, Tristan Patel, Purav J. Walker, Erin Kovashka, Adriana Univ Pittsburgh Intelligent Syst Program Pittsburgh PA 15260 USA Univ Pittsburgh Comp Sci Pittsburgh PA 15260 USA Univ Pittsburgh Learning Res & Dev Ctr Pittsburgh PA 15260 USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9798350365474

In this paper, we introduce an approach for recognizing and classifying gestures that accompany mathematical terms, in a new collection we name the "GAMT" dataset. Our method uses language as a means of providing context to classify gestures. Specifically, we use a CLIP-style framework to construct a shared embedding space for gestures and language, experimenting with various methods for encoding gestures within this space. We evaluate our method on our new dataset containing a wide array of gestures associated with mathematical terms. The shared embedding space leads to a substantial improvement in gesture classification. Furthermore, we identify an efficient model that excelled at classifying gestures from our unique dataset, thus contributing to the further development of gesture recognition in diverse interaction scenarios.

关键词： action clip cnn comunication dataset embeddings gesture hci language-aligned multimodal non-verbal recognition semantic tcn transformers

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UMat: Uncertainty-Aware Single Image High Resolution Material Capture

UMat: Uncertainty-Aware Single Image High Resolution Materia...

引用

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

作者： Rodriguez-Pardo, Carlos Dominguez-Elvira, Henar Pascual-Hernandez, David Garces, Elena SEDDI Madrid Spain Univ Rey Juan Carlos Madrid Spain

ISBN: (纸本)9798350301298

We propose a learning-based method to recover normals, specularity, and roughness from a single diffuse image of a material, using microgeometry appearance as our primary cue. Previous methods that work on single images tend to produce over-smooth outputs with artifacts, operate at limited resolution, or train one model per class with little room for generalization. In contrast, in this work, we propose a novel capture approach that leverages a generative network with attention and a U-Net discriminator, which shows outstanding performance integrating global information at reduced computational complexity. We showcase the performance of our method with a real dataset of digitized textile materials and show that a commodity flatbed scanner can produce the type of diffuse illumination required as input to our method. Additionally, because the problem might be illposed -more than a single diffuse image might be needed to disambiguate the specular reflection- or because the training dataset is not representative enough of the real distribution, we propose a novel framework to quantify the model's confidence about its prediction at test time. Our method is the first one to deal with the problem of modeling uncertainty in material digitization, increasing the trustworthiness of the process and enabling more intelligent strategies for dataset creation, as we demonstrate with an active learning experiment.

关键词： vision + graphics

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An End-to-End Approach for Handwriting recognition: From Handwritten Text Lines to Complete Pages

An End-to-End Approach for Handwriting Recognition: From Han...

引用

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

作者： Castro, Dayvid Bezerra, Byron Leite Dantas Zanchettin, Cleber Univ Fed Pernambuco Recife PE Brazil

ISBN: (纸本)9798350365474

Handwritten Document recognition (HDR) has emerged as a challenging task integrating text and layout information recognition to tackle manuscripts end-to-end. Despite advancements, the computational efficiency of processing entire documents remains a critical challenge, limiting the practical applicability of these models. This paper presents the Document Attention Network for Computationally Efficient recognition (DANCER). The model differs from existing approaches with its unique encoder-decoder structure, where the encoder reduces spatial redundancy and enhances spatial attention, and the decoder, comprising transformer layers, efficiently decodes the text using optimized attention operations. This design results in a fast, memory-efficient model capable of effectively transcribing and understanding complex manuscript layouts. We evaluated DANCER's efficacy on the ICFHR 2016 READ competition dataset, focusing on recognizing single and doublepage historical documents. We demonstrate how DANCER can triple the training batch size compared to prior models within the same memory limits and reduce memory usage by up to 65% without compromising recognition quality. The proposed approach sets new standards in efficiency and accuracy for HDR solutions, paving the way for practical and scalable applications in diverse contexts.

关键词： Attention Networks Full-Page recognition Handwriting recognition Handwritten Document recognition Transformer Network

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FIQA-FAS: Face Image Quality Assessment Based Face Anti-Spoofing

FIQA-FAS: Face Image Quality Assessment Based Face Anti-Spoo...

引用

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

作者： Liang, Ya-Chi Qiu, Min-Xuan Lai, Shang-Hong Natl Tsing Hua Univ Hsinchu Taiwan

ISBN: (纸本)9798350365474

Face anti-spoofing (FAS) is to protect facial recognition systems against presentation attacks. However, recent research on FAS often neglects real-world conditions, such as changing illumination, varying angles of face, and motion blur within a video. These conditions lead to inconsistent feature quality across face images, where low-quality features can cause the model to learn unreliable information during training. Moreover, frames with low feature quality within videos result in inaccurate decisions. To address this issue, we propose the Face Image Quality Assessment Based Face Anti-Spoofing System (FIQA-FAS), which integrates a face image quality assessment module with a face anti-spoofing module. FIQA-FAS assesses the feature quality extracted from each face image and uses the quality score to compute a weighted prediction for deciding if the face in a video is live or spoof. We demonstrate the effectiveness of FIQA-FAS through experiments on the SIW and SIW-M datasets. To further demonstrate our model's capabilities, we introduce a novel simulated scenario that mimics the real world, where our model outperforms other SOTA.

关键词： Face recognition

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Neural Fourier Filter Bank

Neural Fourier Filter Bank

引用

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

作者： Wu, Zhijie Jin, Yuhe Yi, Kwang Moo Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9798350301298

We present a novel method to provide efficient and highly detailed reconstructions. Inspired by wavelets, we learn a neural field that decompose the signal both spatially and frequency-wise. We follow the recent grid-based paradigm for spatial decomposition, but unlike existing work, encourage specific frequencies to be stored in each grid via Fourier features encodings. We then apply a multi-layer perceptron with sine activations, taking these Fourier encoded features in at appropriate layers so that higher-frequency components are accumulated on top of lower-frequency components sequentially, which we sum up to form the final output. We demonstrate that our method outperforms the state of the art regarding model compactness and convergence speed on multiple tasks: 2D image fitting, 3D shape reconstruction, and neural radiance fields. Our code is available at https://***/ubc-vision/NFFB.

关键词： vision + graphics

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RGB no more: Minimally-decoded JPEG vision Transformers

RGB no more: Minimally-decoded JPEG Vision Transformers

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

作者： Park, Jeongsoo Johnson, Justin Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9798350301298

Most neural networks for computer vision are designed to infer using RGB images. However, these RGB images are commonly encoded in JPEG before saving to disk;decoding them imposes an unavoidable overhead for RGB networks. Instead, our work focuses on training vision Transformers (ViT) directly from the encoded features of JPEG. This way, we can avoid most of the decoding overhead, accelerating data load. Existing works have studied this aspect but they focus on CNNs. Due to how these encoded features are structured, CNNs require heavy modification to their architecture to accept such data. Here, we show that this is not the case for ViTs. In addition, we tackle data augmentation directly on these encoded features, which to our knowledge, has not been explored in-depth for training in this setting. With these two improvements - ViT and data augmentation - we show that our ViT-Ti model achieves up to 39.2% faster training and 17.9% faster inference with no accuracy loss compared to the RGB counterpart. (1)

关键词： and reasoning language vision

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Leveraging Pre-trained Multi-task Deep Models for Trustworthy Facial Analysis in Affective Behaviour Analysis in-the-Wild

Leveraging Pre-trained Multi-task Deep Models for Trustworth...

引用

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

作者： Saychenko, Andrey V. r Sber AI Lab Moscow Russia HSE Univ Lab Algorithms & Technol Network Anal Nizhnii Novgorod Russia

ISBN: (纸本)9798350365474

This article presents our results for the sixth Affective Behavior Analysis in-the-wild (ABAW) competition. To improve the trustworthiness of facial analysis, we study the possibility of using pre-trained deep models that extract reliable emotional features without the need to fine-tune the neural networks for a downstream task. In particular, we introduce several lightweight models based on MobileViT, MobileFaceNet, EfficientNet, and DDAMFN architectures trained in multi-task scenarios to recognize facial expressions, valence, and arousal on static photos. These neural networks extract frame-level features fed into a simple classifier, e.g., linear feed-forward neural network, to predict emotion intensity, compound expressions, and valence/arousal. Experimental results for three tasks from the sixth ABAW challenge demonstrate that our approach lets us significantly improve quality metrics on validation sets compared to existing non-ensemble techniques. As a result, our solutions took second place in the compound expression recognition competition.

关键词： Compound facial expressition recognition Emotional Mimicry Intensity estimation HSEmotion library lightweight neural networks Valence-Arousal prediction

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What, when, and where? Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions

What, when, and where? Self-Supervised Spatio-Temporal Groun...

引用

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

作者： Chen, Brian Shvetsova, Nina Rouditchenko, Andrew Kondermann, Daniel Thomas, Samuel Chang, Shih-Fu Feris, Rogerio Glass, James Kuehne, Hilde Columbia Univ New York NY 10027 USA Goethe Univ Frankfurt Frankfurt Germany Univ Bonn Bonn Germany MIT CSAIL Cambridge MA USA Qual Match GmbH Heidelberg Germany IBM Res AI Yorktown Hts NY USA MIT IBM Watson Lab Cambridge MA USA

ISBN: (纸本)9798350353006

Spatio-temporal grounding describes the task of localizing events in space and time, e.g., in video data, based on verbal descriptions only. Models for this task are usually trained with human-annotated sentences and bounding box supervision. This work addresses this task from a multimodal supervision perspective, proposing a frame-work for spatio-temporal action grounding trained on loose video and subtitle supervision only, without human annotation. To this end, we combine local representation learning, which focuses on leveraging fine-grained spatial information, with a global representation encoding that captures higher-level representations and incorporates both in a joint approach. To evaluate this challenging task in a real-life setting, a new benchmark dataset is proposed, providing dense spatio-temporal grounding annotations in long, untrimmed, multi-action instructional videos for over 5K events. We evaluate the proposed approach and other methods on the proposed and standard downstream tasks, showing that our method improves over current baselines in various settings, including spatial, temporal, and untrimmed multi-action spatio-temporal grounding.

关键词： Grounding Multimodal learning Self-supervised learning Video understanding vision and language

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Seeing the Unseen: Visual Common Sense for Semantic Placement

Seeing the Unseen: Visual Common Sense for Semantic Placemen...

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

作者： Ramrakhya, Ram Kembhavi, Aniruddha Batra, Dhruv Kira, Zsolt Zeng, Kuo-Hao Weihs, Luca Georgia Inst Technol Atlanta GA 30332 USA PRIOR Allen Inst AI Seattle WA USA PRIOR AI2 Seattle WA USA

ISBN: (纸本)9798350353006

computer vision tasks typically involve describing what is present in an image (e.g. classification, detection, segmentation, and captioning). We study a visual common sense task that requires understanding 'what is not present'. Specifically, given an image (e.g. of a living room) and a name of an object ("cushion"), a vision system is asked to predict semantically-meaningful regions (masks or bounding boxes) in the image where that object could be placed or is likely be placed by humans (e.g. on the sofa). We call this task: Semantic Placement (SP) and believe that such common-sense visual understanding is critical for assitive robots (tidying a house), AR devices (automatically rendering an object in the user's space), and visually-grounded chatbots with common sense. Studying the invisible is hard. Datasets for image description are typically constructed by curating relevant images (e.g. via image search with object names) and asking humans to annotate the contents of the image;neither of those two steps are straightforward for objects not present in the image. We overcome this challenge by operating in the opposite direction: we start with an image of an object in context, which is easy to find online, and then remove that object from the image via inpainting. This automated pipeline converts unstructured web data into a dataset comprising pairs of images with/without the object. With this proposed data generation pipeline, we collect a novel dataset, containing similar to 1.3M images across 9 object categories. We then train a SP prediction model, called CLIP-UNet, on our dataset. The CLIP-UNet outperforms existing VLMs and baselines that combine semantic priors with object detectors, generalizes well to real-world and simulated images and exhibits semantics-aware reasoning for object placement. In our user studies, we find that the SP masks predicted by CLIP-UNet are favored 43.7% and 31.3% times when comparing against the 4 SP baselines on real and simulated

关键词： Common Sense Reasoning computer vision Embodied AI

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SIFU: Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction

SIFU: Side-view Conditioned Implicit Function for Real-world...

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

作者： Zhang, Zechuan Yang, Zongxin Yang, Yi Zhejiang Univ CCAL ReLER Hangzhou Peoples R China

ISBN: (纸本)9798350353006

Creating high-quality 3D models of clothed humans from single images for real-world applications is crucial. Despite recent advancements, accurately reconstructing humans in complex poses or with loose clothing from in-the-wild images, along with predicting textures for unseen areas, remains a significant challenge. A key limitation of previous methods is their insufficient prior guidance in transitioning from 2D to 3D and in texture prediction. In response, we introduce SIFU (Side-view Conditioned Implicit Function for Real-world Usable Clothed Human Reconstruction), a novel approach combining a Side-view Decoupling Transformer with a 3D Consistent Texture Refinement pipeline. SIFU employs a cross-attention mechanism within the transformer, using SMPL-X normals as queries to effectively decouple side-view features in the process of mapping 2D features to 3D. This method not only improves the precision of the 3D models but also their robustness, especially when SMPL-X estimates are not perfect. Our texture refinement process leverages text-to-image diffusion-based prior to generate realistic and consistent textures for invisible views. Through extensive experiments, SIFU surpasses SOTA methods in both geometry and texture reconstruction, showcasing enhanced robustness in com-lex scenarios and achieving an unprecedented Chamfer and P2S measurement. Our approach extends to practical applications such as 3D printing and scene building, demonstrating its broad utility in real-world scenarios.

关键词： 3D reconstruction 3D vision Clothed human diffusion model single image to 3D

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