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

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Review of Semantic Segmentation by Using Deep learning methods 1

Review of Semantic Segmentation by Using Deep learning metho...

引用

1st International conference on Social and Sustainable Innovations in Technology and Engineering (SASI-ITE)

作者： Rajeswari, B. Ram, J. Mani Kumar, D. V. T. Praveen Harshith, K. L. V. V. Lakireddy Bali Reddy Coll Engn Dept Elect & Commun Engn Mylavaram AP India

ISBN: (纸本)9798350360806;9798350360790

Semantic segmentation, a critical task in computer vision, involves pixel-level classification of images to assign each pixel to a specific semantic category. Over the years, numerous authors have extensively explored and applied semantic segmentation techniques across diverse domains. This paper provides a brief overview of the highlights of its prominent role in advancing image understanding. Authors have proposed several pioneering architectures specifically tailored for semantic segmentation The subsequent years saw the development of variants such as SegNet and Deep Lab, each addressing unique challenges in semantic segmentation tasks. Semantic segmentation fmds applications in diverse fields, including medical imaging, autonomous vehicles, and augmented reality. Authors have demonstrated its utility in tasks such as tumor detection, road scene understanding, and object recognition, showcasing its versatility and potential societal impact. Recent trends include the integration of semantic segmentation with other computer vision tasks, such as instance segmentation and panoptic segmentation Authors are exploring methods for improving interpretability, robustness to domain shifts, and efficiency in resource-constrained environments.

关键词： Semantic Segmentation Deep learning Convolutional Neural Network (CNNs) Image analysis computer vision

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Achieving robustness in classification using optimal transport with hinge regularization

Achieving robustness in classification using optimal transpo...

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

作者： Serrurier, Mathieu Mamalet, Franck Gonzalez-Sanz, Alberto Boissin, Thibaut Loubes, Jean-Michel del Barrio, Eustasio Univ Paul Sabatier Toulouse France IRT St Exupery Toulouse France Univ Valladolid Valladolid Spain

ISBN: (纸本)9781665445092

Adversarial examples have pointed out Deep Neural Network's vulnerability to small local noise. It has been shown that constraining their Lipschitz constant should enhance robustness, but make them harder to learn with classical loss functions. We propose a new framework for binary classification, based on optimal transport, which integrates this Lipschitz constraint as a theoretical requirement. We propose to learn 1-Lipschitz networks using a new loss that is an hinge regularized version of the Kantorovich-Rubinstein dual formulation for the Wasserstein distance estimation. This loss function has a direct interpretation in terms of adversarial robustness together with certifiable robustness bound. We also prove that this hinge regularized version is still the dual formulation of an optimal transportation problem, and has a solution. We also establish several geometrical properties of this optimal solution, and extend the approach to multi-class problems. Experiments show that the proposed approach provides the expected guarantees in terms of robustness without any significant accuracy drop. The adversarial examples, on the proposed models, visibly and meaningfully change the input providing an explanation for the classification.

关键词： computer vision Computational modeling Transportation Estimation Fasteners Robustness pattern recognition

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Temple Inscriptions recognition and Transliteration in Devanagari Script 2

Temple Inscriptions Recognition and Transliteration in Devan...

引用

2nd ieee International conference on vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Babu, B. Sathish Shetty, Sannidhi Agarwal, Anushka Sreerama, Sai Lahari Bhustali, Vaishnavi K. Sanjana, Sanka RV College of Engineering Department of Artificial Intelligence and ML Bengaluru India RV College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350347982

Ancient inscriptions, palm scripts, manuscripts, etc., have vital information about India's rich culture. recognition and understanding of these inscriptions have been challenging for epigraphers and professionals. The goal of the proposed research is to advance optical character recognition methods for archival Vatteluttu script inscriptions, which date back to the 4th or 5th century AD. This paper discusses a deep learning model to transliterate the ancient Tamil inscriptions (Vatteluttu Script), which can be extended further to other languages. The proposed work is beneficial to epigraphists, archaeological researchers, and the general public who are interested in this topic. The developed deep learning model has achieved an accuracy of 84.12%. © 2023 ieee.

关键词： Optical character recognition

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CompositeTasking: Understanding Images by Spatial Composition of Tasks

CompositeTasking: Understanding Images by Spatial Compositio...

引用

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

作者： Popovic, Nikola Paudel, Danda Pani Probst, Thomas Sun, Guolei Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Katholieke Univ Leuven ESAT PSI VISICS Leuven Belgium

ISBN: (纸本)9781665445092

We define the concept of CompositeTasking as the fusion of multiple, spatially distributed tasks, for various aspects of image understanding. Learning to perform spatially distributed tasks is motivated by the frequent availability of only sparse labels across tasks, and the desire for a compact multi-tasking network. To facilitate CompositeTasking, we introduce a novel task conditioning model - a single encoder-decoder network that performs multiple, spatially varying tasks at once. The proposed network takes an image and a set of pixel-wise dense task requests as inputs, and performs the requested prediction task for each pixel. Moreover, we also learn the composition of tasks that needs to be performed according to some CompositeTasking rules, which includes the decision of where to apply which task. It not only offers us a compact network for multi-tasking, but also allows for task-editing. Another strength of the proposed method is demonstrated by only having to supply sparse supervision per task. The obtained results are on par with our baselines that use dense supervision and a multi-headed multi-tasking design. The source code will be made publicly available at ***/nikola3794/composite-tasking.

关键词： computer vision Codes Multitasking pattern recognition Task analysis

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No frame left behind: Full Video Action recognition

No frame left behind: Full Video Action Recognition

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

作者： Liu, Xin Pintea, Silvia L. Nejadasl, Fatemeh Karimi Booij, Olaf van Gemert, Jan C. Delft Univ Technol Comp Vis Lab Delft Netherlands TomTom Amsterdam Netherlands

ISBN: (纸本)9781665445092

Not all video frames are equally informative for recognizing an action. It is computationally infeasible to train deep networks on all video frames when actions develop over hundreds of frames. A common heuristic is uniformly sampling a small number of video frames and using these to recognize the action. Instead, here we propose full video action recognition and consider all video frames. To make this computational tractable, we first cluster all frame activations along the temporal dimension based on their similarity with respect to the classification task, and then temporally aggregate the frames in the clusters into a smaller number of representations. Our method is end-to-end trainable and computationally efficient as it relies on temporally localized clustering in combination with fast Hamming distances in feature space. We evaluate on UCF101, HMDB51, Breakfast, and Something-Something V1 and V2, where we compare favorably to existing heuristic frame sampling methods.

关键词： Training computer vision Philosophical considerations Semantics Memory management Sampling methods Nonhomogeneous media

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Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map

Self-Supervised Simultaneous Multi-Step Prediction of Road D...

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

作者： Amirloo, Elmira Rohani, Mohsen Banijamali, Ershad Luo, Jun Poupart, Pascal Huawei Noahs Ark Lab Toronto ON Canada Univ Waterloo Sch Comp Sci Waterloo ON Canada

ISBN: (纸本)9781665445092

While supervised learning is widely used for perception modules in conventional autonomous driving solutions, scalability is hindered by the huge amount of data labeling needed. In contrast, while end-to-end architectures do not require labeled data and are potentially more scalable, interpretability is sacrificed. We introduce a novel architecture that is trained in a fully self-supervised fashion for simultaneous multi-step prediction of space-time cost map and road dynamics. Our solution replaces the manually designed cost function for motion planning with a learned high dimensional cost map that is naturally interpretable and allows diverse contextual information to be integrated without manual data labeling. Experiments on real world driving data show that our solution leads to lower number of collisions and road violations in long planning horizons in comparison to baselines, demonstrating the feasibility of fully self-supervised prediction without sacrificing scalability.

关键词： Costs Roads Scalability Supervised learning Dynamics computer architecture Manuals

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Body Language Decoder Using Python 5

Body Language Decoder Using Python

引用

5th ieee International conference for Emerging Technology, INCET 2024

作者： Kaur, Husanpreet Jyoti Devi, Sanjana Rahul Department of Computer Science & Engineering Chandigarh University Punjab Gharuan India

ISBN: (纸本)9798350361155

In human connection, nonverbal cues, especially body language, are extremely important. Although it might be difficult to interpret these subtle indications, doing so can provide important insights into the motivations and behaviors of people. Body Language decoder using Scikit-learn, OpenCV, MediaPipe, and Python automatically decipher and analyze body language cues from recorded or live video streams. captures and processes video frames in real-time using OpenCV, a potent computer vision toolkit. The Body Language Decoder provides a flexible foundation for comprehending human communication patterns and enabling more user-friendly interfaces, ranging from emotion recognition to gesture interpretation. By detecting gestures, facial expressions, and body postures, this initiative seeks to improve human-computer interaction while offering important insights into the intents and behavior of the user. The technology provides an invaluable resource for improving communication, comprehending social dynamics, and developing more efficient human-machine interfaces by automating the interpretation of body language clues. © 2024 ieee.

关键词： Python

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MFAN: Multi-Scale Feature Attention Network for Speech Emotion recognition 7

MFAN: Multi-Scale Feature Attention Network for Speech Emoti...

引用

7th International conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Ma, Weifeng Song, Wei Li, Xin School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350350890

Due to the variability of speech signals and the complexity of human emotions, speech emotion recognition (SER) is an important and challenging task. It is crucial in SER to extract rich emotional information. Some research relies on one type of feature to extract emotional information. However, this approach often fails to capture rich emotional information. Some studies employ multiple types of features. Different features enhance the richness of emotional information, but it may also be affected by redundant information, leading to a decrease in recognition performance. In this paper, we propose a multi-scale feature attention network (MFAN) to address these problems. The model utilizes an utterance-level feature extraction module (UFEM) and a multi-scale feature extraction module (MFEM) to extract emotional information at different scales. In UFEM, a global perspective is being used to extract utterance-level emotional features. In MFEM, a multi-scale feature attention mechanism is introduced to extract emotional information at various scales from a local perspective. The attention mechanism is to enable the model to focus on information relevant to emotion recognition, reducing the impact of redundant information and improving the overall performance of the model. The experimental results on the IEMOCAP, RAVDESS, and EMODB datasets demonstrate that MFAN exhibits excellent performance in the field of speech emotion recognition. © 2024 ieee.

关键词： Feature extraction

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Dynamic Appearance Modelling from Minimal Cameras

Dynamic Appearance Modelling from Minimal Cameras

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

作者： Bridgeman, Lewis Guillemaut, Jean-Yves Hilton, Adrian Univ Surrey CVSSP Guildford Surrey England

ISBN: (纸本)9781665448994

We present a novel method for modelling dynamic texture appearance from a minimal set of cameras. Previous methods to capture the dynamic appearance of a human from multi-view video have relied on large, expensive camera setups, and typically store texture on a frame-by-frame basis. We fit a parameterised human body model to multi-view video from minimal cameras (as few as 3), and combine the partial texture observations from multiple viewpoints and frames in a learned framework to generate full-body textures with dynamic details given an input pose. Key to our method are our multi-band loss functions, which apply separate blending functions to the high and low spatial frequencies to reduce texture artefacts. We evaluate our method on a range of multi-view datasets, and show that our model is able to accurately produce full-body dynamic textures, even with only partial camera coverage. We demonstrate that our method outperforms other texture generation methods on minimal camera setups.

关键词： computer vision Computational modeling conferences Biological system modeling Cameras pattern recognition

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Reconsidering Representation Alignment for Multi-view Clustering

Reconsidering Representation Alignment for Multi-view Cluste...

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

作者： Trosten, Daniel J. Lokse, Sigurd Jenssen, Robert Kampffmeyer, Michael UiT Arctic Univ Norway Dept Phys & Technol Tromso Norway UiT Machine Learning Grp Tromso Norway

ISBN: (纸本)9781665445092

Aligning distributions of view representations is a core component of today's state of the art models for deep multi-view clustering. However, we identify several drawbacks with naively aligning representation distributions. We demonstrate that these drawbacks both lead to less separable clusters in the representation space, and inhibit the model's ability to prioritize views. Based on these observations, we develop a simple baseline model for deep multi-view clustering. Our baseline model avoids representation alignment altogether, while performing similar to, or better than, the current state of the art. We also expand our baseline model by adding a contrastive learning component. This introduces a selective alignment procedure that preserves the model's ability to prioritize views. Our experiments show that the contrastive learning component enhances the baseline model, improving on the current state of the art by a large margin on several datasets(1).

关键词： computer vision Computational modeling Adversarial machine learning pattern recognition

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