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The Unreasonable Effectiveness of CLIP Features for Image Captioning: An Experimental Analysis

The Unreasonable Effectiveness of CLIP Features for Image Ca...

引用

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

作者： Barraco, Manuele Cornia, Marcella Cascianelli, Silvia Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Generating textual descriptions from visual inputs is a fundamental step towards machine intelligence, as it entails modeling the connections between the visual and textual modalities. For years, image captioning models have relied on pre-trained visual encoders and object detectors, trained on relatively small sets of data. Recently, it has been observed that large-scale multi-modal approaches like CLIP (Contrastive Language-Image Pre-training), trained on a massive amount of image-caption pairs, provide a strong zero-shot capability on various vision tasks. In this paper, we study the advantage brought by CLIP in image captioning, employing it as a visual encoder. Through extensive experiments, we show how CLIP can significantly out-perform widely-used visual encoders and quantify its role under different architectures, variants, and evaluation protocols, ranging from classical captioning performance to zero-shot transfer.

关键词： Visualization computer vision Protocols conferences Detectors Distance measurement Data models

来源：评论

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Gated Recurrent Unit-Based RNN for Remote Photoplethysmography Signal Segmentation

Gated Recurrent Unit-Based RNN for Remote Photoplethysmograp...

引用

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

作者： Sabour, Rita Meziati Benezeth, Yannick Univ Bourgogne Franche Comte Dijon France Univ Bourgogne Franche Comte ImViA Lab Dijon France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Remote Photoplethysmography (rPPG) enables quantifying blood volume variations in the skin tissues from an input video recording, using a regular RGB camera. Obtained pulse signals often contain noisy portions due to motion, leading researchers to put aside a great number of rPPG signals in their studies. In this paper, an approach using a Gated Recurrent Unit-based neural network model in order to identify reliable portions in rPPG signals is proposed. This is done by classifying rPPG signal samples into reliable and unreliable samples. For this purpose, rPPG and electrocardiography signals (ECG) were collected from 11 participants, rPPG signal samples were labeled (ECG was used as ground truth), and data were augmented to reach a total number of 11000 1-minute-long rPPG signals. We developed a model composed of a unidimensional CNN and a Bidirectional GRU (1D-CNN+B-GRU) for this study, and obtained an accuracy rate of 85.88%.

关键词： computer network reliability Neural networks Electrocardiography Logic gates Photoplethysmography Skin pattern recognition

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AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data

AuxMix: Semi-Supervised Learning with Unconstrained Unlabele...

引用

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

作者： Banitalebi-Dehkordi, Amin Gujjar, Pratik Zhang, Yong Huawei Technol Canada Co Ltd Vancouver BC Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Semi-supervised learning (SSL) has seen great strides when labeled data is scarce but unlabeled data is abundant. Critically, most recent work assume that such unlabeled data is drawn from the same distribution as the labeled data. In this work, we show that state-of-the-art SSL algorithms suffer a degradation in performance in the presence of unlabeled auxiliary data that does not necessarily possess the same class distribution as the labeled set. We term this problem as Auxiliary-SSL and propose AuxMix, an algorithm that leverages self-supervised learning tasks to learn generic features in order to mask auxiliary data that are not semantically similar to the labeled set. We also propose to regularize learning by maximizing the predicted entropy for dissimilar auxiliary samples. We show an improvement of 5% over existing baselines on a ResNet-50 model when trained on CIFAR10 dataset with 4k labeled samples and all unlabeled data is drawn from the Tiny-Imagenet dataset. We report competitive results on several datasets and conduct ablation studies.

关键词： Degradation computer vision conferences Self-supervised learning Semisupervised learning Prediction algorithms Entropy

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Unpaired Faces to Cartoons: Improving XGAN

Unpaired Faces to Cartoons: Improving XGAN

引用

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

作者： Ramos, Stev H. Cabrera, Joel Ibanez, Daniel Jimenez-Panta, Alejandro B. Beltran-Castanon, Cesar Villanueva, Edwin Pontifical Catholic Univ Peru Lima Peru

ISBN: (纸本)9781665487399

Domain Adaptation is a task that aims to translate an image from a source domain to a desired target domain. Current methods in domain adaptation use adversarial training based on Generative Adversarial Networks (GAN). In the present work, we focus on the task of domain adaptation from real faces to cartoon face images. We start from a baseline architecture called XGAN and introduce some improvements to it. Our proposed model is called W-XDGAN, which uses a form of GAN called Wasserstein-GAN, learns to approximate the Wasserstein Distance, and adds a denoiser to smooth the output cartoons. Whereas the original XGAN paper only presented a qualitative analysis, the advantages of this solution are demonstrated both quantitatively and qualitatively by comparing the results with models such as UNIT and original XGAN. Our code and models are publicly available at https://github. com/IAmigos/avatar-image-generator.

关键词： Training computer vision Codes Computational modeling conferences computer architecture Generative adversarial networks

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Less is More: Proxy Datasets in NAS approaches

Less is More: Proxy Datasets in NAS approaches

引用

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

作者： Moser, Brian Raue, Federico Hees, Joern Dengel, Andreas German Res Ctr Artificial Intelligence DFKI Darmstadt Germany TU Kaiserslautern Kaiserslautern Germany

ISBN: (纸本)9781665487399

Neural Architecture Search (NAS) defines the design of Neural Networks as a search problem. Unfortunately, NAS is computationally intensive because of various possibilities depending on the number of elements in the design and the possible connections between them. In this work, we extensively analyze the role of the dataset size based on several sampling approaches for reducing the dataset size (unsupervised and supervised cases) as an agnostic approach to reduce search time. We compared these techniques with four common NAS approaches in NAS-Bench-201 in roughly 1,400 experiments on CIFAR-100. One of our surprising findings is that in most cases we can reduce the amount of training data to 25%, consequently also reducing search time to 25%, while at the same time maintaining the same accuracy as if training on the full dataset. In addition, some designs derived from subsets out-perform designs derived from the full dataset by up to 22 p.p. accuracy.

关键词： Training computer vision conferences Neural networks Training data computer architecture Search problems

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Interaction Classification with Key Actor Detection in Multi-Person Sports Videos

Interaction Classification with Key Actor Detection in Multi...

引用

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

作者： Askari, Farzaneh Ramaprasad, Rohit Clark, James J. Levine, Martin D. McGill Univ Montreal PQ Canada Birla Inst Technol & Sci Pilani Rajasthan India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Interaction recognition from multi-person videos is a challenging yet essential task in computer vision. Often the videos depict actions with multiple actors involved, some of whom participate in the main event, and the rest are present in the scene without being part of the actual event. This paper proposes a model to tackle the problem of interaction recognition from multi-person videos. Our model consists of a Recurrent Neural Network (RNN) equipped with a time-varying attention mechanism. It receives scene features and localized actors features to predict the interaction class. Additionally, the attention model identifies the people responsible for the main event. We chose penalty classification from ice hockey broadcast videos as our application. These videos are multi-persons and depict complex interactions between players in a non-laboratory recording setup. We evaluate our model on a new dataset of ice hockey penalty videos and report 93.93% classification accuracy. We include a qualitative analysis of the attention mechanism by visualizing the attention weights. Our code is publicly available (1).

关键词： Visualization computer vision Recurrent neural networks Annotations Computational modeling Ice Recording

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Self-Supervised Learning of Pose-Informed Latents

Self-Supervised Learning of Pose-Informed Latents

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

作者： Jean, Raphael St-Charles, Pierre-Luc Pirk, Soren Brodeur, Simon Menya Solut Sherbrooke PQ Canada Mila Montreal PQ Canada AMLRT Montreal PQ Canada Google Res Mountain View CA USA

ISBN: (纸本)9781665487399

Siamese network architectures trained for self-supervised instance recognition can learn powerful visual representations that are useful in various tasks. Many such approaches maximize the similarity between representations of augmented images of the same object. In this paper, we depart from traditional self-supervised learning benchmarks by defining a novel methodology for new challenging tasks such as zero shot pose estimation. Our goal is to show that common Siamese networks can effectively be trained on frame pairs from video sequences to generate pose-informed representations. Unlike parallel efforts that focus on introducing new image-space operators for data augmentation, we argue that extending the augmentation strategy by using different frames of a video leads to more powerful representations. To show the effectiveness of this approach, we use the Objectron and UCF101 datasets to learn representations and evaluate them on pose estimation, action recognition, and object re-identification. Furthermore, we carefully validate our method against a number of baselines.

关键词： Training computer vision Visualization Pose estimation Video sequences Self-supervised learning Benchmark testing

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CarlaScenes: A synthetic dataset for odometry in autonomous driving

CarlaScenes: A synthetic dataset for odometry in autonomous ...

引用

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

作者： Kloukiniotis, Andreas Papandreou, Andreas Anagnostopoulos, Christos Lalos, Aris Kapsalas, Petros Nguyen, D-, V Moustakas, Konstantinos Univ Patras Patras Greece ISI Ind Syst Inst Patras Patras Greece Panasonic Automot Langen Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Despite the great scientific effort to capture adequately the complex environments in which autonomous vehicles (AVs) operate there are still use-cases that even SoA methods fail to handle. Specifically in odometry problems, on the one hand, geometric solutions operate with certain assumptions that are often breached in AVs, and on the other hand, deep learning methods do not achieve high accuracy. To contribute to that we present CarlaScenes, a large-scale simulation dataset captured using the CARLA simulator. The dataset is oriented to address the challenging odometry scenarios that cause the current state of art odometers to deviate from their normal operations. Based on a case study of failures presented in experiments we distinguished 7 different sequences of data. CarlaScenes besides providing consistent reference poses, includes data with semantic annotation at the instance level for both image and lidar. The full dataset is available at https://***/CarlaScenes/***.

关键词： Deep learning computer vision Laser radar conferences Semantics XML Trajectory

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A Two-Stage Shake-Shake Network for Long-Tailed recognition of SAR Aerial View Objects

A Two-Stage Shake-Shake Network for Long-Tailed Recognition ...

引用

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

作者： Li, Gongzhe Pan, Linpeng Qiu, Linwei Tan, Zhiwen Xie, Fengying Zhang, Haopeng Beihang Univ Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Synthetic Aperture Radar (SAR) has received more attention due to its complementary superiority on capturing significant information in the remote sensing area. However, for an Aerial View Object Classification (AVOC) task, SAR images still suffer from the long-tailed distribution of the aerial view objects. This disparity limit the performance of classification methods, especially for the data-sensitive deep learning models. In this paper, we propose a two-stage shake-shake network to tackle the long-tailed learning problem. Specifically, it decouples the learning procedure into the representation learning stage and the classification learning stage. Moreover, we apply the test time augmentation (TTA) and the classification with alternating normalization (CAN) to improve the accuracy. In the PBVS 1 2022 Multi-modal Aerial View Object Classification Challenge Track 1, our method achieves 21.82% and 27.97% accuracy in the development phase and testing phase respectively, which wins the top-tier among all the participants.

关键词： Training Representation learning Feature extraction Radar tracking Radar polarimetry pattern recognition Task analysis

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Linear Combination Approximation of Feature for Channel Pruning

Linear Combination Approximation of Feature for Channel Prun...

引用

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

作者： Joo, Donggyu Kim, Doyeon Yi, Eojindl Kim, Junmo Korea Adv Inst Sci & Technol KAIST Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Network pruning is an effective method that reduces the computation of neural networks while maintaining high performance. This enables the operation of deep neural networks in resource-limited environments. In a general large network, the roles of each channel often inevitably overlap with those of others. Therefore, for more effective pruning, it is important to observe the correlation between features in the network. In this paper, we propose a novel channel pruning method, namely, the linear combination approximation of features (LCAF). We approximate each feature map by a linear combination of other feature maps in the same layer, and then remove the most approximated one. Additionally, by exploiting the linearity of the convolution operation, we propose a supporting method called weight modification, to further reduce the loss change that occurs during pruning. Extensive experiments show that LCAF achieves state-of-the-art performance in several benchmarks. Furthermore, ablations on the LCAF demonstrate the effectiveness of our approach in a variety of ways.

关键词： Deep learning computer vision Correlation Convolution conferences Neural networks Linearity

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