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

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Learning To Count Everything

Learning To Count Everything

引用

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

作者： Ranjan, Viresh Sharma, Udbhav Thu Nguyen Hoai, Minh SUNY Stony Brook Stony Brook NY 11794 USA VinAI Res Hanoi Vietnam

ISBN: (纸本)9781665445092

Existing works on visual counting primarily focus on one specific category at a time, such as people, animals, and cells. In this paper, we are interested in counting everything, that is to count objects from any category given only a few annotated instances from that category. To this end, we pose counting as a few-shot regression task. To tackle this task, we present a novel method that takes a query image together with a few exemplar objects from the query image and predicts a density map for the presence of all objects of interest in the query image. We also present a novel adaptation strategy to adapt our network to any novel visual category at test time, using only a few exemplar objects from the novel category. We also introduce a dataset of 147 object categories containing over 6000 images that are suitable for the few-shot counting task. The images are annotated with two types of annotation, dots and bounding boxes, and they can be used for developing few-shot counting models. Experiments on this dataset shows that our method outperforms several state-of-the-art object detectors and few-shot counting approaches. Our code and dataset can be found at https://***/cvlab-stonybrook/LearningToCountEverything.

关键词： Visualization computer vision Codes Annotations Animals Detectors pattern recognition

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Exploring Facial Expression recognition through Semi-Supervised Pre-training and Temporal Modeling

Exploring Facial Expression Recognition through Semi-Supervi...

引用

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

作者： Jun Yu Zhihong Wei Zhongpeng Cai Gongpeng Zhao Zerui Zhang Yongqi Wang Guochen Xie Jichao Zhu Wangyuan Zhu Qingsong Liu Jiaen Liang University of Science and Technology of China Unisound AI Technology Co. Ltd

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Facial Expression recognition (FER) plays a crucial role in computer vision and finds extensive applications across various fields. This paper aims to present our approach for the 6th Affective Behavior Analysis in-the-Wild (ABAW) competition, scheduled to be held at CVPR2024. In the facial expression recognition task, the limited size of the FER dataset poses a challenge to the expression recognition model’s generalization ability, resulting in subpar recognition performance. To address this problem, we employ a Semi-supervised learning technique to generate expression category pseudo labels for unlabeled face data. At the same time, we uniformly sampled the labeled facial expression samples and implemented a debiased feedback learning strategy to address the problem of category imbalance in the dataset and the possible data bias in semi-supervised learning. Moreover, to further compensate for the limitation and bias of features obtained only from static images, we introduced a Temporal Encoder to capture temporal relationships between neighbouring expression image features. In the 6th ABAW competition, our method achieved the third place in the official test set, a result that fully demonstrates the effectiveness and competitiveness of our proposed method.

关键词： computer vision Image recognition Accuracy Face recognition conferences Genetic expression Semisupervised learning

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Fast and Accurate Model Scaling

Fast and Accurate Model Scaling

引用

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

作者： Dollar, Piotr Singh, Mannat Girshick, Ross Facebook AI Res FAIR Menlo Pk CA 94025 USA

ISBN: (纸本)9781665445092

In this work we analyze strategies for convolutional neural network scaling;that is, the process of scaling a base convolutional network to endow it with greater computational complexity and consequently representational power. Example scaling strategies may include increasing model width, depth, resolution, etc. While various scaling strategies exist, their tradeoffs are not fully understood. Existing analysis typically focuses on the interplay of accuracy and flops (floating point operations). Yet, as we demonstrate, various scaling strategies affect model parameters, activations, and consequently actual runtime quite differently. In our experiments we show the surprising result that numerous scaling strategies yield networks with similar accuracy but with widely varying properties. This leads us to propose a simple fast compound scaling strategy that encourages primarily scaling model width, while scaling depth and resolution to a lesser extent. Unlike currently popular scaling strategies, which result in about O(s) increase in model activation w.r.t. scaling flops by a factor of s, the proposed fast compound scaling results in close to O(root s) increase in activations, while achieving excellent accuracy. Fewer activations leads to speedups on modern memory-bandwidth limited hardware (e.g., GPUs). More generally, we hope this work provides a framework for analyzing scaling strategies under various computational constraints.

关键词： computer vision Runtime Computational modeling Hardware pattern recognition Compounds Convolutional neural networks

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A Review and Efficient Implementation of Scene Graph Generation Metrics

A Review and Efficient Implementation of Scene Graph Generat...

引用

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

作者： Julian Lorenz Robin Schön Katja Ludwig Rainer Lienhart University of Augsburg Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Scene graph generation has emerged as a prominent research field in computer vision, witnessing significant advancements in the recent years. However, despite these strides, precise and thorough definitions for the metrics used to evaluate scene graph generation models are lacking. In this paper, we address this gap in the literature by providing a review and precise definition of commonly used metrics in scene graph generation. Our comprehensive examination clarifies the underlying principles of these metrics and can serve as a reference or introduction to scene graph ***, to facilitate the usage of these metrics, we introduce a standalone Python package called SGBench that efficiently implements all defined metrics, ensuring their accessibility to the research community. Additionally, we present a scene graph benchmarking web service, that enables researchers to compare scene graph generation methods and increase visibility of new methods in a central *** of our code can be found under https://***/sgbench/.

关键词： Measurement computer vision Solid modeling Reviews Web services conferences Computational modeling

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Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods

Enhance Curvature Information by Structured Stochastic Quasi...

引用

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

作者： Yang, Minghan Xu, Dong Chen, Hongyu Wen, Zaiwen Chen, Mengyun Peking Univ Sch Math Sci Beijing Peoples R China Peking Univ Beijing Int Ctr Math Res Beijing Peoples R China Peking Univ Ctr Data Sci Beijing Peoples R China Peking Univ Natl Engn Lab Big Data Anal & Applicat Beijing Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781665445092

In this paper, we consider stochastic second-order methods for minimizing a finite summation of nonconvex functions. One important key is to find an ingenious but cheap scheme to incorporate local curvature information. Since the true Hessian matrix is often a combination of a cheap part and an expensive part, we propose a structured stochastic quasi-Newton method by using partial Hessian information as much as possible. By further exploiting either the low-rank structure or the kronecker-product properties of the quasi-Newton approximations, the computation of the quasi-Newton direction is affordable. Global convergence to stationary point and local superlinear convergence rate are established under some mild assumptions. Numerical results on logistic regression, deep autoencoder networks and deep convolutional neural networks show that our proposed method is quite competitive to the state-of-the-art methods.

关键词： Training computer vision pattern recognition Convolutional neural networks Artificial intelligence Portfolios Optimization

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Learning Contextual Causality between Daily Events from Time-consecutive Images

Learning Contextual Causality between Daily Events from Time...

引用

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

作者： Zhang, Hongming Huo, Yintong Zhao, Xinran Song, Yangqiu Roth, Dan HKUST Hong Kong Peoples R China CUHK Hong Kong Peoples R China UPenn Philadelphia PA 19104 USA

ISBN: (纸本)9781665448994

Conventional textual-based causal knowledge acquisition methods typically require laborious and expensive human annotations. As a result, their scale is often limited. Moreover, as no context is provided during the annotation, the resulting causal knowledge records (e.g., ConceptNet) typically do not consider the context. In this paper, we move out of the textual domain to explore a more scalable way of acquiring causal knowledge and investigate the possibility of learning contextual causality from the visual signal. Specifically, we first propose a high-quality dataset Vis-Causal and then conduct experiments to demonstrate that with good language and visual representations, it is possible to discover meaningful causal knowledge from the videos. Further analysis also shows that the contextual property of causal relations indeed exists and considering the contextual property can help better predict the causal relation between events. The Vis-Causal dataset and experiment code are available at https://***/HKUST-KnowComp/Vis_Causal.

关键词： Training Visualization computer vision Annotations Knowledge acquisition conferences pattern recognition

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Double low-rank representation with projection distance penalty for clustering

Double low-rank representation with projection distance pena...

引用

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

作者： Fu, Zhiqiang Zhao, Yao Chang, Dongxia Zhang, Xingxing Wang, Yiming Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China Beijing Key Lab Adv Informat Sci & Network Techno Beijing Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

This paper presents a novel, simple yet robust self-representation method, i.e., Double Low-Rank Representation with Projection Distance penalty (DLRRPD) for clustering. With the learned optimal projected representations, DLRRPD is capable of obtaining an effective similarity graph to capture the multi-subspace structure. Besides the global low-rank constraint, the local geometrical structure is additionally exploited via a projection distance penalty in our DLRRPD, thus facilitating a more favorable graph. Moreover, to improve the robustness of DLRRPD to noises, we introduce a Laplacian rank constraint, which can further encourage the learned graph to be more discriminative for clustering tasks. Meanwhile, Frobenius norm (instead of the popularly used nuclear norm) is employed to enforce the graph to be more block-diagonal with lower complexity. Extensive experiments have been conducted on synthetic, real, and noisy data to show that the proposed method outperforms currently available alternatives by a margin of 1.0%similar to 10.1%.

关键词： computer vision Laplace equations Robustness pattern recognition Complexity theory Noise measurement Task analysis

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Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning

Supervised Contrastive Replay: Revisiting the Nearest Class ...

引用

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

作者： Mai, Zheda Li, Ruiwen Kim, Hyunwoo Sanner, Scott Univ Toronto Toronto ON Canada LG AI Res Seoul South Korea

ISBN: (纸本)9781665448994

Online class-incremental continual learning (CL) studies the problem of learning new classes continually from an online non-stationary data stream, intending to adapt to new data while mitigating catastrophic forgetting. While memory replay has shown promising results, the recency bias in online learning caused by the commonly used Softmax classifier remains an unsolved challenge. Although the Nearest-Class-Mean (NCM) classifier is significantly undervalued in the CL community, we demonstrate that it is a simple yet effective substitute for the Softmax classifier. It addresses the recency bias and avoids structural changes in the fully-connected layer for new classes. Moreover, we observe considerable and consistent performance gains when replacing the Softmax classifier with the NCM classifier for several state-of-the-art replay methods. To leverage the NCM classifier more effectively, data embeddings belonging to the same class should be clustered and well-separated from those with a different class label. To this end, we contribute Supervised Contrastive Replay (SCR), which explicitly encourages samples from the same class to cluster tightly in embedding space while pushing those of different classes further apart during replay-based training. Overall, we observe that our proposed SCR substantially reduces catastrophic forgetting and outperforms state-of-the-art CL methods by a significant margin on a variety of datasets.

关键词： Training computer vision conferences Performance gain pattern recognition

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Do Deepfakes Feel Emotions? A Semantic Approach to Detecting Deepfakes Via Emotional Inconsistencies

Do Deepfakes Feel Emotions? A Semantic Approach to Detecting...

引用

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

作者： Hosler, Brian Salvi, Davide Murray, Anthony Antonacci, Fabio Bestagini, Paolo Tubaro, Stefano Stamm, Matthew C. Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9781665448994

Recent advances in deep learning and computer vision have spawned a new class of media forgeries known as deepfakes, which typically consist of artificially generated human faces or voices. The creation and distribution of deepfakes raise many legal and ethical concerns. As a result, the ability to distinguish between deepfakes and authentic media is vital. While deepfakes can create plausible video and audio, it may be challenging for them to to generate content that is consistent in terms of high-level semantic features, such as emotions. Unnatural displays of emotion, measured by features such as valence and arousal, can provide significant evidence that a video has been synthesized. In this paper, we propose a novel method for detecting deepfakes of a human speaker using the emotion predicted from the speaker's face and voice. The proposed technique leverages Long Short-Term Memory (LSTM) networks that predict emotion from audio and video Low-Level Descriptors (LLDs). Predicted emotion in time is used to classify videos as authentic or deepfakes through an additional supervised classifier.

关键词： computer vision Emotion recognition Law Semantics Speech recognition Media Forgery

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Stochastic Whitening Batch Normalization

Stochastic Whitening Batch Normalization

引用

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

作者： Zhang, Shengdong Nezhadarya, Ehsan Fashandi, Homa Liu, Jiayi Graham, Darin Shah, Mohak LG Elect Canada Toronto AI Lab Toronto ON Canada LG Elect USA Amer R&D Lab Santa Clara CA USA

ISBN: (纸本)9781665445092

Batch Normalization (BN) is a popular technique for training Deep Neural Networks (DNNs). BN uses scaling and shifting to normalize activations of mini-batches to accelerate convergence and improve generalization. The recently proposed Iterative Normalization (IterNorm) method improves these properties by whitening the activations iteratively using Newton's method. However, since Newton's method initializes the whitening matrix independently at each training step, no information is shared between consecutive steps. In this work, instead of exact computation of whitening matrix at each time step, we estimate it gradually during training in an online fashion, using our proposed Stochastic Whitening Batch Normalization (SWBN) algorithm. We show that while SWBN improves the convergence rate and generalization of DNNs, its computational overhead is less than that of IterNorm. Due to the high efficiency of the proposed method, it can be easily employed in most DNN architectures with a large number of layers. We provide comprehensive experiments and comparisons between BN, IterNorm, and SWBN layers to demonstrate the effectiveness of the proposed technique in conventional (many-shot) image classification and few-shot classification tasks.

关键词： Training Deep learning computer vision Stochastic processes pattern recognition Iterative methods Convolutional neural networks

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