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

共 31020 条记录，以下是4211-4220 订阅

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Micro-Expression Classification based on Landmark Relations with Graph Attention Convolutional Network

Micro-Expression Classification based on Landmark Relations ...

引用

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

作者： Kumar, Ankith Jain Rakesh Bhanu, Bir Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

ISBN: (纸本)9781665448994

Facial micro-expressions are brief, rapid, spontaneous gestures of the facial muscles that express an individual's genuine emotions. Because of their short duration and subtlety, detecting and classifying these micro-expressions by humans and machines is difficult. In this paper, a novel approach is proposed that exploits relationships between landmark points and the optical flow patch for the given landmark points. It consists of a two-stream graph attention convolutional network that extracts the relationships between the landmark points and local texture using an optical flow patch. A graph structure is built to draw-out temporal information using the triplet of frames. One stream is for node feature location, and the other one is for a patch of optical-flow information. These two streams (node location stream and optical flow stream) are fused for classification. The results are shown on, CASME II and SAMM, publicly available datasets, for three classes and five classes of micro-expressions. The proposed approach outperforms the state-of-the-art methods for 3 and 5 categories of expressions.

关键词： computer vision conferences Feature extraction Facial muscles pattern recognition Optical flow

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Make Pixels Dance: High-Dynamic Video Generation

Make Pixels Dance: High-Dynamic Video Generation

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yan Zeng Guoqiang Wei Jiani Zheng Jiaxin Zou Yang Wei Yuchen Zhang Hang Li ByteDance Research

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortu-nately, current state-of-the-art video generation methods, primarily focusing on text-to- video generation, tend to pro-duce video clips with minimal motions despite maintaining high fidelity. We argue that relying solely on text in-structions is insufficient and suboptimal for video generation. In this paper, we introduce PixelDance, a novel approach based on diffusion models that incorporates image instructions for both the first and last frames in conjunction with text instructions for video generation. Comprehensive experimental results demonstrate that PixelDance trained with public data exhibits significantly better proficiency in synthesizing videos with complex scenes and intricate motions, setting a new standard for video generation.

关键词： Training Humanities computer vision Focusing Diffusion models Visual effects pattern recognition

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Total Selfie: Generating Full-Body Selfies

Total Selfie: Generating Full-Body Selfies

引用

conference on computer vision and pattern recognition (CVPR)

作者： Bowei Chen Brian Curless Ira Kemelmacher-Shlizerman Steven M. Seitz University of Washington

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present a method to generate full-body selfies from photographs originally taken at arms length. Because self-captured photos are typically taken close up, they have lim-ited field of view and exaggerated perspective that distorts facial shapes. We instead seek to generate the photo some one else would take of you from a few feet away. Our approach takes as input four selfies of your face and body, a background image, and generates a full-body selfie in a de-sired target pose. We introduce a novel diffusion-based approach to combine all of this information into high-quality, well-composed photos of you with the desired pose and background.

关键词： computer vision Shape Face recognition

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Deep Video Codec Control for vision Models

Deep Video Codec Control for Vision Models

引用

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

作者： Christoph Reich Biplob Debnath Deep Patel Tim Prangemeier Daniel Cremers Srimat Chakradhar NEC Laboratories America Inc. TU Munich TU Darmstadt Munich Center for Machine Learning (MCML)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Standardized lossy video coding is at the core of almost all real-world video processing pipelines. Rate control is used to enable standard codecs to adapt to different network bandwidth conditions or storage constraints. However, standard video codecs (e.g., H.264) and their rate control modules aim to minimize video distortion w.r.t. human quality assessment. We demonstrate empirically that standard-coded videos vastly deteriorate the performance of deep vision models. To overcome the deterioration of vision performance, this paper presents the first end-to-end learnable deep video codec control that considers both bandwidth constraints and downstream deep vision performance, while adhering to existing standardization. We demonstrate that our approach better preserves downstream deep vision performance than traditional standard video coding.

关键词： Video coding Computational modeling Face recognition Pipelines Bandwidth Self-supervised learning Distortion

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Single View Geocentric Pose in the Wild

Single View Geocentric Pose in the Wild

引用

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

作者： Christie, Gordon Foster, Kevin Hagstrom, Shea Hager, Gregory D. Brown, Myron Z. Johns Hopkins Univ Appl Phys Lab Baltimore MD 21218 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA

ISBN: (纸本)9781665448994

Current methods for Earth observation tasks such as semantic mapping, map alignment, and change detection rely on near-nadir images;however, often the first available images in response to dynamic world events such as natural disasters are oblique. These tasks are much more difficult for oblique images due to observed object parallax. There has been recent success in learning to regress an object's geocentric pose, defined as height above ground and orientation with respect to gravity, by training with airborne lidar registered to satellite images. We present a model for this novel task that exploits affine invariance properties to outperform state of the art performance by a wide margin. We also address practical issues required to deploy this method in the wild for real-world applications. Our data and code are publicly available(1).

关键词： Training Earth computer vision Satellites Laser radar conferences Semantics

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Omnimatte: Associating Objects and Their Effects in Video

Omnimatte: Associating Objects and Their Effects in Video

引用

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

作者： Lu, Erika Cole, Forrester Dekel, Tali Zisserman, Andrew Freeman, William T. Rubinstein, Michael Google Res Mountain View CA 94043 USA Univ Oxford Oxford England Weizmann Inst Sci Rehovot Israel

ISBN: (纸本)9781665445092

computer vision is increasingly effective at segmenting objects in images and videos;however, scene effects related to the objects-shadows, reflections, generated smoke, etc.-are typically overlooked. Identifying such scene effects and associating them with the objects producing them is important for improving our fundamental understanding of visual scenes, and can also assist a variety of applications such as removing, duplicating, or enhancing objects in video. In this work, we take a step towards solving this novel problem of automatically associating objects with their effects in video. Given an ordinary video and a rough segmentation mask over time of one or more subjects of interest, we estimate an omnimatte for each subject-an alpha matte and color image that includes the subject along with all its related time-varying scene elements. Our model is trained only on the input video in a self-supervised manner, without any manual labels, and is generic-it produces omnimattes automatically for arbitrary objects and a variety of effects. We show results on real-world videos containing interactions between different types of subjects (cars, animals, people) and complex effects, ranging from semitransparent elements such as smoke and reflections, to fully opaque effects such as objects attached to the subject.

关键词： Training Image segmentation computer vision Color Visual effects Reflection pattern recognition

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11th IAPR TC3 workshop on Artificial Neural Networks in pattern recognition, ANNPR 2024

11th IAPR TC3 workshop on Artificial Neural Networks in Patt...

引用

11th IAPR TC3 workshop on Artificial Neural Networks in pattern recognition, ANNPR 2024

ISBN: (纸本)9783031716010

The proceedings contain 27 papers. The special focus in this conference is on Artificial Neural Networks in pattern recognition. The topics include: Neural Decompiling of Tracr Transformers;pitfalls in Processing Infinite-Length Sequences with Popular Approaches for Sequential Data;robust Clustering with McDonald’s Beta-Liouville Mixture Models for Proportional Data;evaluating Support Vector Machines with Multiple Kernels by Random Search;Automatic Interpretation of 18F-Fluorocholine PET/CT Findings in Patients with Primary Hyperparathyroidism: A Novel Dataset with Benchmarks;a Hybrid Neuroevolutionary Approach to the Design of Convolutional Neural Networks for 2D and 3D Medical Image Segmentation;An Improved Pix2Pix GAN for Medical Image Generation;vision Transformer Features-Based Leukemia Classification;comparative Study of Deep Learning Models in Melanoma Detection;a Metaheuristic Optimization Based Deep Feature Selection for Oral Cancer Classification;machine Learning for Clinical Score Prediction from Longitudinal Dataset: A Case Study on Parkinson’s Disease;explaining Network Decision Provides Insights on the Causal Interaction Between Brain Regions in a Motor Imagery Task;Multi-modal Decoding of Reach-to-Grasping from EEG and EMG via Neural Networks;VAeViT: Fusing Multi-views for Complete 3D Object recognition;leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos;palmprint Classification via Filter Faces and Feature Extraction;deep Multi-label Classification of Personality with Handwriting Analysis;license Plate Detection and Character recognition Using Deep Learning and Font Evaluation;experiments in Modeling Disagreement;deep Multiresolution Wavelet Transform for Speech Emotion Assessment of High-Risk Suicide Callers;Dynamic HumTrans: Humming Transcription Using CNNs and Dynamic Programming;Leveraging LSTM Embeddings for River Water Temperature Modeling;research on the Identification of Common Economic Shellfish in Jiang

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End-to-End Spatio-Temporal Action Localisation with Video Transformers

End-to-End Spatio-Temporal Action Localisation with Video Tr...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Alexey A. Gritsenko Xuehan Xiong Josip Djolonga Mostafa Dehghani Chen Sun Mario Lučić Cordelia Schmid Anurag Arnab Google

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The most performant spatio-temporal action localisation models use external person proposals and complex external memory banks. We propose a fully end-to-end, purely-transformer based model that directly ingests an input video, and outputs tubelets - a sequence of bounding boxes and the action classes at each frame. Our flexible model can be trained with either sparse bounding-box supervision on individual frames, or full tubelet annotations. And in both cases, it predicts coherent tubelets as the output. Moreover, our end-to-end model requires no additional pre-processing in the form of proposals, or post-processing in terms of non-maximal suppression. We perform extensive ablation experiments, and significantly advance the state-of-the-art on five different spatio-temporal action localisation benchmarks with both sparse keyframes and full tubelet annotations.

关键词： computer vision Annotations Benchmark testing Transformers pattern recognition Proposals

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Contrastive Learning for Sports Video: Unsupervised Player Classification

Contrastive Learning for Sports Video: Unsupervised Player C...

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

作者： Koshkina, Maria Pidaparthy, Hemanth Elder, James H. York Univ Toronto ON Canada

ISBN: (纸本)9781665448994

We address the problem of unsupervised classification of players in a team sport according to their team affiliation, when jersey colours and design are not known a priori. We adopt a contrastive learning approach in which an embedding network learns to maximize the distance between representations of players on different teams relative to players on the same team, in a purely unsupervised fashion, without any labelled data. We evaluate the approach using a new hockey dataset and find that it outperforms prior unsupervised approaches by a substantial margin, particularly for real-time application when only a small number of frames are available for unsupervised learning before team assignments must be made. Remarkably, we show that our contrastive method achieves 94% accuracy after unsupervised training on only a single frame, with accuracy rising to 97% within 500 frames (17 seconds of game time). We further demonstrate how accurate team classification allows accurate team-conditional heat maps of player positioning to be computed.

关键词： Training Heating systems computer vision Image color analysis conferences Games Real-time systems

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Architectural Adversarial Robustness: The Case for Deep Pursuit

Architectural Adversarial Robustness: The Case for Deep Purs...

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

作者： Cazenavette, George Murdock, Calvin Lucey, Simon Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781665445092

Despite their unmatched performance, deep neural networks remain susceptible to targeted attacks by nearly imperceptible levels of adversarial noise. While the underlying cause of this sensitivity is not well understood, theoretical analyses can be simplified by refraining each layer of a feed forward network as an approximate solution to a sparse coding problem. Iterative solutions using basis pursuit are theoretically more stable and have improved adversarial robustness. However, cascading layer-wise pursuit implementations suffer from error accumulation in deeper networks. In contrast, our new method of deep pursuit approximates the activations of all layers as a single global optimization problem, allowing us to consider deepen real-world architectures with skip connections such as residual networks. Experimentally, our approach demonstrates improved robustness to adversarial noise.

关键词： Deep learning Resistance computer vision Sensitivity computer architecture Sparse representation Robustness

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