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Hierarchical, Generic to Specific Multi-class Object recognition

Hierarchical, Generic to Specific Multi-class Object Recogni...

引用

International conference on pattern recognition

作者： A. Dhua F. Cutzu Computer Science Department Indiana University Bloomington IN USA

ISBN: (纸本)0769525210

Early vision systems could perform specific object recognition reasonably well but did not fare as well on identifying natural object classes. Recent advances have led to systems that can learn a representation for different object classes and achieve good generic object class recognition. However, these systems are generally unable to perform the fine distinctions required for specific object identification. It seems that the two approaches are in contrast with each other. We propose a system that addresses the problems of generic class recognition as well as specific object recognition in the same framework. Our system also possesses the property of graceful degradation, i.e., if it is unable to recognize an object as a dog, it recognizes it at least as a quadruped and so on. We automatically learn a hierarchy of the classes from the training data, progressing from the most generic class labels to the most specific object labels. This hierarchy is used during recognition. One important benefit of the hierarchical organization of the classes is that the number of comparisons performed for every input does not increase linearly with the number of classes added

关键词： Object recognition Machine vision Degradation Shape computer science Training data computer vision Humans Psychology Animation

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Unified Physical-Digital Attack Detection Challenge

Unified Physical-Digital Attack Detection Challenge

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Haocheng Yuan Ajian Liu Junze Zheng Jun Wan Jiankang Deng Sergio Escalera Hugo Jair Escalante Isabelle Guyon Zhen Lei M.U.S.T MAIS CASIA China SAI UCAS China ICL UK CVC Spain INAOE CINVESTAV Mexico ChaLearn USA CAIR HKISI CAS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face Anti-Spoofing (FAS) is crucial to safeguard Face recognition (FR) Systems. In real-world scenarios, FRs are confronted with both physical and digital attacks. However, existing algorithms often address only one type of attack at a time, which poses significant limitations in real-world scenarios where FR systems face hybrid physical-digital threats. To facilitate the research of Unified Attack Detection (UAD) algorithms, a large-scale UniAttackData dataset has been collected. UniAttackData is the largest public dataset for Unified Attack Detection, with a total of 28,706 videos, where each unique identity encompasses all advanced attack types. Based on this dataset, we organized a Unified Physical-Digital Face Attack Detection Challenge to boost the research in Unified Attack Detections. It attracted 136 teams for the development phase, with 13 qualifying for the final round. The results re-verified by the organizing team were used for the final ranking. This paper comprehensively reviews the challenge, detailing the dataset introduction, protocol definition, evaluation criteria, and a summary of published results. Finally, we focus on the detailed analysis of the highest-performing algorithms and offer potential directions for unified physical-digital attack detection inspired by this competition. Challenge Website: https://***/view/face-anti-spoofing-challenge/welcome/challengecvpr2024

关键词： Measurement computer vision Protocols Codes Reviews Face recognition conferences

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Using Dependent Regions for Object Categorization in a Generative Framework

Using Dependent Regions for Object Categorization in a Gener...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Gang Wang Ye Zhang Li Fei-Fei Department of Information Engineering Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana-Champaign IL USA

"Bag of words" models have enjoyed much attention and achieved good performances in recent studies of object categorization. In most of these works, local patches are modeled as basic building blocks of an image, analogous to words in text documents. In most previous works using the "bag of words" models (e.g. [4, 20, 7]), the local patches are assumed to be independent with each other. In this paper, we relax the independence assumption and model explicitly the inter-dependency of the local regions. Similarly to previous work , we represent images as a collection of patches, each of which belongs to a latent "theme" that is shared across images as well as categories. We learn the theme distributions and patch distributions over the themes in a hierarchical structure [22]. In particular, we introduce a linkage structure over the latent themes to encode the dependencies of the patches. This structure enforces the semantic connections among the patches by facilitating better clustering of the themes. As a result, our models for object categories tend to be more discriminative than the ones obtained under the independent patch assumption. We show highly competitive categorization results on both the Caltech 4 and Caltech 101 object category datasets. By examining the distributions of the latent themes for each object category, we construct an object taxonomy using the 101 object classes from the Caltech 101 datasets.

关键词： Wheels computer vision Couplings Taxonomy Object recognition Humans Robots Robustness Shape Solid modeling

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BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primitives

BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primit...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sainan Liu Shan Lin Jingpei Lu Alexey Supikov Michael Yip Intel Labs UCSD

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Implicit neural representations have become pivotal in robotic perception, enabling robots to comprehend 3D environments from 2D images. Given a set of camera poses and associated images, the models can be trained to synthesize novel, unseen views. To successfully navigate and interact in dynamic settings, robots require the understanding of their spatial surroundings driven by unassisted reconstruction of 3D scenes and camera poses from real-time video footage. Existing approaches like COLMAP and bundle-adjusting neural radiance field methods take hours to days to process due to the high computational demands of feature matching, dense point sampling, and training of a multi-layer perceptron structure with a large number of parameters. To address these challenges, we propose a framework called bundle-adjusting accelerated neural graphics primitives (BAA-NGP) which leverages accelerated sampling and hash encoding to expedite automatic pose refinement/estimation and 3D scene reconstruction. Experimental results demonstrate 10 to 20 × speed improvement compared to other bundle-adjusting neural radiance field methods without sacrificing the quality of pose estimation. The github repository can be found here https://***/IntelLabs/baa-ngp.

关键词： Training Graphics Three-dimensional displays Robot vision systems Video sequences Streaming media Cameras

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Exploring the Role of Audio in Video Captioning

Exploring the Role of Audio in Video Captioning

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yuhan Shen Linjie Yang Longyin Wen Haichao Yu Ehsan Elhamifar Heng Wang Northeastern University ByteDance

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent focus in video captioning has been on designing architectures that can consume both video and text modalities, and using large-scale video datasets with text transcripts for pre-training, such as HowTo100M. Though these approaches have achieved significant improvement, the audio modality is often ignored in video captioning. In this work, we present an audio-visual framework, which aims to fully exploit the potential of the audio modality for captioning. Instead of relying on text transcripts extracted via automatic speech recognition (ASR), we argue that learning with raw audio signals can be more beneficial, as audio has additional information including acoustic events, speaker identity, etc. Our contributions are twofold. First, we observed that the model overspecializes to the audio modality when pre-training with both video and audio modality, since the ground truth (i.e., text transcripts) can be solely predicted using audio. We proposed a Modality Balanced Pre-training (MBP) loss to mitigate this issue and significantly improve the performance on downstream tasks. Second, we slice and dice different design choices of the cross-modal module, which may become an information bottleneck and generate inferior results. We proposed new local-global fusion mechanisms to improve information exchange across audio and video. We demonstrate significant improvements by leveraging the audio modality on four datasets, and even outperform the state of the art on some metrics without relying on the text modality as the input.

关键词： Measurement computer vision Computational modeling conferences Predictive models Acoustics Data mining

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Vertical Parallax from Moving Shadows

Vertical Parallax from Moving Shadows

引用

conference on computer vision and pattern recognition (CVPR)

作者： Y. Caspi M. Werman Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel

This paper presents a method for capturing and computing 3D parallax. 3D parallax, as used here, refers to vertical offset from the ground plane, height. The method is based on analyzing shadows of vertical poles (e.g., a tall building’s contour) that sweep the object. Unlike existing beam-scanning approaches, such as shadow or structured light, that recover the distance of a point from the camera, our approach measures the height from the ground plane directly. Previous methods compute the distance from the camera using triangulation between rays outgoing from the light-source and the camera. Such a triangulation is difficult when the objects are far from the camera, and requires accurate knowledge of the light source position. In contrast, our approach intersects two (unknown) planes generated separately by two casting objects. This omits the need to precompute the location of the light source. Furthermore, it allows a moving light source to be used. The proposed setup is particularly useful when the camera cannot directly face the scene or when the object is far away from the camera. A good example is an urban scene captured by a single webcam.

关键词： Cameras Layout Light sources Image reconstruction computer science Strips Mathematics Casting Laser beams Pixel

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SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation

SegFormer3D: an Efficient Transformer for 3D Medical Image S...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Shehan Perera Pouyan Navard Alper Yilmaz Photogrammetric Computer Vision Lab The Ohio State University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The adoption of vision Transformers (ViTs) based architectures represents a significant advancement in 3D Medical Image (MI) segmentation, surpassing traditional Convolutional Neural Network (CNN) models by enhancing global contextual understanding. While this paradigm shift has significantly enhanced 3D segmentation performance, state-of-the-art architectures require extremely large and complex architectures with large scale computing resources for training and deployment. Furthermore, in the context of limited datasets, often encountered in medical imaging, larger models can present hurdles in both model generalization and convergence. In response to these challenges and to demonstrate that lightweight models are a valuable area of research in 3D medical imaging, we present SegFormer3D, a hierarchical Transformer that calculates attention across multiscale volumetric features. Additionally, SegFormer3D avoids complex decoders and uses an all-MLP decoder to aggregate local and global attention features to produce highly accurate segmentation masks. The proposed memory efficient Transformer preserves the performance characteristics of a significantly larger model in a compact design. SegFormer3D democratizes deep learning for 3D medical image segmentation by offering a model with 33× less parameters and a 13× reduction in GFLOPS compared to the current state-of-the-art (SOTA). We benchmark SegFormer3D against the current SOTA models on three widely used datasets Synapse, BRaTs, and ACDC, achieving competitive results. Code: https://***/OSUPCVLab/***

关键词： Training Image segmentation Solid modeling Three-dimensional displays Computational modeling computer architecture Transformers

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Deep Anchored Convolutional Neural Networks

Deep Anchored Convolutional Neural Networks

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jiahui Huang Kshitij Dwivedi Gemma Roig Singapore University of Technology and Design

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Convolutional Neural Networks (CNNs) have been proven to be extremely successful at solving computer vision tasks. State-of-the-art methods favor such deep network architectures for its accuracy performance, with the cost of having massive number of parameters and high weights redundancy. Previous works have studied how to prune such CNNs weights. In this paper, we go to another extreme and analyze the performance of a network stacked with a single convolution kernel across layers, as well as other weights sharing techniques. We name it Deep Anchored Convolutional Neural Network (DACNN). Sharing the same kernel weights across layers allows to reduce the model size tremendously, more precisely, the network is compressed in memory by a factor of L, where L is the desired depth of the network, disregarding the fully connected layer for prediction. The number of parameters in DACNN barely increases as the network grows deeper, which allows us to build deep DACNNs without any concern about memory costs. We also introduce a partial shared weights network (DACNN-mix) as well as an easy-plug-in module, coined regulators, to boost the performance of our architecture. We validated our idea on 3 datasets: CIFAR-10, CIFAR-100 and SVHN. Our results show that we can save massive amounts of memory with our model, while maintaining a high accuracy performance.

关键词： Convolution Kernel Regulators computer architecture Task analysis Neural networks Adaptation models

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Beyond Appearances: Material Segmentation with Embedded Spectral Information from RGB-D imagery

Beyond Appearances: Material Segmentation with Embedded Spec...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Fabian Perez Hoover Rueda-Chacón Universidad Industrial de Santander

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the realm of computer vision, material segmentation of natural scenes represents a challenge, driven by the complex and diverse appearances of materials. Traditional approaches often rely on RGB images, which can be deceptive given the variability in appearances due to different lighting conditions. Other methods, that employ polarization or spectral imagery, offer a more reliable material differentiation but their cost and accessibility restrict their everyday usage. In this work, we propose a deep learning framework that bridges the gap between high-fidelity material segmentation and the practical constraints of data acquisition. Our approach leverages a training strategy that employs a paired RGBD-spectral data to incorporate spectral information directly within the neural network. This encoding process is facilitated by a Spectral Feature Mapper (SFM) layer, a novel module that embeds unique spectral characteristics into the network, thus enabling the network to infer materials from standard RGB-D images. Once trained, the model allows to conduct material segmentation on widely available devices without the need for direct spectral data input. In addition, we generate the 3D point cloud from the RGB-D image pair, to provide a richer spatial context for scene understanding. Through simulations using available datasets, and real experiments conducted with an iPad Pro, our method demonstrates superior performance in material segmentation compared to other methods. Code is available at: https://***/Factral/Spectral-material-segmentation

关键词： Deep learning Training Image segmentation computer vision Visualization Three-dimensional displays Lighting

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Embedded neuromorphic vision for humanoid robots

Embedded neuromorphic vision for humanoid robots

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Chiara Bartolozzi Francesco Rea Charles Clercq Daniel B. Fasnacht Giacomo Indiveri Michael Hofstätter Giorgio Metta Italian Institute of Technology Genoa Italy University of Zurich ETH Zurich Zurich Switzerland Austrian Institute of Technology GmbH Wien Austria Università degli Studi di Genova Genoa Italy

We are developing an embedded vision system for the humanoid robot iCub, inspired by the biology of the mammalian visual system, including concepts such as stimulus-driven, asynchronous signal sensing and processing. It comprises stimulus-driven sensors, a dedicated embedded processor and an event-based software infrastructure for processing visual stimuli. These components are integrated with the existing standard machine vision modules currently implemented on the robot, in a configuration that exploits the best features of both: the high resolution, color, frame-based vision and the neuromorphic low redundancy, wide dynamic range and high temporal resolution event-based sensors. This approach seeks to combine various styles of vision hardware with sensorimotor systems to complement and extend the current state-of-the art.

关键词： Visualization Field programmable gate arrays Robot sensing systems Voltage control Feature extraction

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