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Transformer Tracking with Cyclic Shifting Window Attention

Transformer Tracking with Cyclic Shifting Window Attention

引用

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

作者： Song, Zikai Yu, Junqing Chen, Yi-Ping Phoebe Yang, Wei Huazhong Univ Sci & Technol Wuhan Peoples R China La Trobe Univ Bundoora Vic Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Transformer architecture has been showing its great strength in visual object tracking, for its effective attention mechanism. Existing transformer-based approaches adopt the pixel-to-pixel attention strategy on flattened image features and unavoidably ignore the integrity of objects. In this paper, we propose a new transformer architecture with multi-scale cyclic shifting window attention for visual object tracking, elevating the attention from pixel to window level. The cross-window multi-scale attention has the advantage of aggregating attention at different scales and generates the best fine-scale match for the target object. Furthermore, the cyclic shifting strategy brings greater accuracy by expanding the window samples with positional information, and at the same time saves huge amounts of computational power by removing redundant calculations. Extensive experiments demonstrate the superior performance of our method, which also sets the new state-of-the-art records on five challenging datasets, along with the VOT2020, UAV123, LaSOT, TrackingNet, and GOT-10k benchmarks. Our project is available at https://***/SkyeSong38/CSWinTT.

关键词： Visualization Target tracking Image recognition Optimization methods computer architecture Benchmark testing Transformers

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OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas

OmniLayout: Room Layout Reconstruction from Indoor Spherical...

引用

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

作者： Rao, Shivansh Kumar, Vikas Kifer, Daniel Giles, C. Lee Mali, Ankur Penn State Univ University Pk PA 16802 USA

ISBN: (纸本)9781665448994

Given a single RGB panorama, the goal of 3D layout reconstruction is to estimate the room layout by predicting the corners, floor boundary, and ceiling boundary. A common approach has been to use standard convolutional networks to predict the corners and boundaries, followed by post-processing to generate the 3D layout. However, the space-varying distortions in panoramic images are not compatible with the translational equivariance property of standard convolutions, thus degrading performance. Instead, we propose to use spherical convolutions. The resulting network, which we call OmniLayout performs convolutions directly on the sphere surface, sampling according to inverse equirectangular projection and hence invariant to equirectangular distortions. Using a new evaluation metric, we show that our network reduces the error in the heavily distorted regions (near the poles) by approximate to 25% when compared to standard convolutional networks. Experimental results show that OmniLayout outperforms the state-of-the-art by approximate to 4% on two different benchmark datasets (PanoContext and Stanford 2D-3D). Code is available at https://***/rshivansh/OmniLayout.

关键词： Convolutional codes Three-dimensional displays Layout Benchmark testing Distortion Predistortion pattern recognition

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Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution

Extracurricular Learning: Knowledge Transfer Beyond Empirica...

引用

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

作者： Pouransari, Hadi Javaheripi, Mojan Sharma, Vinay Tuzel, Oncel Apple Cupertino CA 95014 USA UCSD La Jolla CA USA

ISBN: (纸本)9781665448994

Knowledge distillation has been used to transfer knowledge learned by a sophisticated model (teacher) to a simpler model (student). This technique is widely used to compress model complexity. However, in most applications the compressed student model suffers from an accuracy gap with its teacher. We propose extracurricular learning, a novel knowledge distillation method, that bridges this gap by (1) modeling student and teacher output distributions;(2) sampling examples from an approximation to the underlying data distribution;and (3) matching student and teacher output distributions over this extended set including uncertain samples. We conduct rigorous evaluations on regression and classification tasks and show that compared to the standard knowledge distillation, extracurricular learning reduces the gap by 46 % to 68 %. This leads to major accuracy improvements compared to the empirical risk minimization-based training for various recent neural network architectures: 16% regression error reduction on the MPIIGaze dataset, +3.4% to +9.1% improvement in top-1 classification accuracy on the CIFAR100 dataset, and +2.9% top-1 improvement on the ImageNet dataset.

关键词： Training Uncertainty Neural networks Estimation Data models pattern recognition Task analysis

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Difficulty Estimation with Action Scores for computer vision Tasks

Difficulty Estimation with Action Scores for Computer Vision...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Octavio Arriaga Sebastian Palacio Matias Valdenegro-Toro University of Bremen German Research Center for Artificial Intelligence University of Groningen

As more machine learning models are now being applied in real world scenarios it has become crucial to evaluate their difficulties and biases. In this paper we present an unsupervised method for calculating a difficulty score based on the accumulated loss per epoch. Our proposed method does not require any modification to the model, neither any external supervision, and it can be easily applied to a wide range of machine learning tasks. We provide results for the tasks of image classification, image segmentation, and object detection. We compare our score against similar metrics and provide theoretical and empirical evidence of their difference. Furthermore, we show applications of our proposed score for detecting incorrect labels, and test for possible biases.

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Does Image Anonymization Impact computer vision Training?

Does Image Anonymization Impact Computer Vision Training?

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Håkon Hukkelås Frank Lindseth Deparment of Computer Science Norwegian University of Science and Technology

Image anonymization is widely adapted in practice to comply with privacy regulations in many regions. However, anonymization often degrades the quality of the data, reducing its utility for computer vision development. In this paper, we investigate the impact of image anonymization for training computer vision models on key computer vision tasks (detection, instance segmentation, and pose estimation). Specifically, we benchmark the recognition drop on common detection datasets, where we evaluate both traditional and realistic anonymization for faces and full bodies. Our comprehensive experiments reflect that traditional image anonymization substantially impacts final model performance, particularly when anonymizing the full body. Furthermore, we find that realistic anonymization can mitigate this decrease in performance, where our experiments reflect a minimal performance drop for face anonymization. Our study demonstrates that realistic anonymization can enable privacy-preserving computer vision development with minimal performance degradation across a range of important computer vision benchmarks.

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FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning

FReTAL: Generalizing Deepfake Detection using Knowledge Dist...

引用

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

作者： Kim, Minha Tariq, Shahroz Woo, Simon S. Sungkyunkwan Univ Coll Comp & Informat Seoul South Korea Sungkyunkwan Univ Dept Appl Data Sci Seoul South Korea

ISBN: (纸本)9781665448994

As GAN-based video and image manipulation technologies become more sophisticated and easily accessible, there is an urgent need for effective deepfake detection technologies. Moreover, various deepfake generation techniques have emerged over the past few years. While many deepfake detection methods have been proposed, their performance suffers from new types of deepfake methods on which they are not sufficiently trained. To detect new types of deepfakes, the model should learn from additional data without losing its prior knowledge about deepfakes (catastrophic forgetting), especially when new deepfakes are significantly different. In this work, we employ the Representation Learning (ReL) and Knowledge Distillation (KD) paradigms to introduce a transfer learning-based Feature Representation Transfer Adaptation Learning (FReTAL) method. We use FReTAL to perform domain adaptation tasks on new deepfake datasets, while minimizing the catastrophic forgetting. Our student model can quickly adapt to new types of deepfake by distilling knowledge from a pre-trained teacher model and applying transfer learning without using source domain data during domain adaptation. Through experiments on FaceForensics++ datasets, we demonstrate that FReTAL outperforms all baselines on the domain adaptation task with up to 86.97% accuracy on low-quality deepfakes.

关键词： Adaptation models computer vision conferences Computational modeling Transfer learning Data models pattern recognition

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Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness

Renofeation: A Simple Transfer Learning Method for Improved ...

引用

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

作者： Chin, Ting-Wu Zhang, Cha Marculescu, Diana Carnegie Mellon Univ Pittsburgh PA 15213 USA Microsoft Cloud & AI Redmond WA USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665448994

Fine-tuning through knowledge transfer from a pre-trained model on a large-scale dataset is a widely spread approach to effectively build models on small-scale datasets. In this work, we show that a recent adversarial attack designed for transfer learning via re-training the last linear layer can successfully deceive models trained with transfer learning via end-to-end fine-tuning. This raises security concerns for many industrial applications. In contrast, models trained with random initialization without transfer are much more robust to such attacks, although these models often exhibit much lower accuracy. To this end, we propose noisy feature distillation, a new transfer learning method that trains a network from random initialization while achieving clean-data performance competitive with fine-tuning.

关键词： computer vision Computational modeling conferences Transfer learning Robustness pattern recognition Noise measurement

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Video Class Agnostic Segmentation Benchmark for Autonomous Driving

Video Class Agnostic Segmentation Benchmark for Autonomous D...

引用

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

作者： Siam, Mennatullah Kendall, Alex Jagersand, Martin Univ Alberta Edmonton AB Canada Wayve London England

ISBN: (纸本)9781665448994

Semantic segmentation approaches are typically trained on large-scale data with a closed finite set of known classes without considering unknown objects. In certain safety-critical robotics applications, especially autonomous driving, it is important to segment all objects, including those unknown at training time. We formalize the task of video class agnostic segmentation from monocular video sequences in autonomous driving to account for unknown objects. Video class agnostic segmentation can be formulated as an open-set or a motion segmentation problem. We discuss both formulations and provide datasets and benchmark different baseline approaches for both tracks. In the motion-segmentation track we benchmark real-time joint panoptic and motion instance segmentation, and evaluate the effect of ego-flow suppression. In the open-set segmentation track we evaluate baseline methods that combine appearance, and geometry to learn prototypes per semantic class. We then compare it to a model that uses an auxiliary contrastive loss to improve the discrimination between known and unknown objects. Datasets and models are publicly released at https://***/vca/.

关键词： Training computer vision Tracking Motion segmentation Semantics Video sequences Benchmark testing

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Multi-Scale Selective Residual Learning for Non-Homogeneous Dehazing

Multi-Scale Selective Residual Learning for Non-Homogeneous ...

引用

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

作者： Jo, Eunsung Sim, Jae-Young Ulsan Natl Inst Sci & Technol Grad Sch Artificial Intelligence Ulsan South Korea Ulsan Natl Inst Sci & Technol Dept Elect Engn Ulsan South Korea

ISBN: (纸本)9781665448994

As the particles in hazy medium cause the absorption and scattering of light, the images captured under such environment suffer from quality degradation such as low contrast and color distortion. While numerous single image dehazing methods have been proposed to reconstruct clean images from hazy images, non-homogeneous dehazing has been rarely studied. In this paper, we design an end-to-end network to remove non-homogeneous dense haze. We employ the selective residual blocks to adaptively improve the visibility of resulting images, where the input feature and the residual feature are combined with fully trainable weights. Experimental results including the ablation study demonstrate that the proposed method is a promising tool for non-homogeneous dehazing that enhances the contrast of hazy images effectively while restoring colorful appearance faithfully.

关键词： Degradation computer vision Image color analysis conferences Scattering Tools Distortion

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End-to-End Learned Image Compression with Augmented Normalizing Flows

End-to-End Learned Image Compression with Augmented Normaliz...

引用

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

作者： Ho, Yung-Han Chan, Chih-Chun Peng, Wen-Hsiao Hang, Hsueh-Ming Natl Chiao Tung Univ Comp Sci Dept Hsinchu Taiwan Natl Chiao Tung Univ Elect Engn Dept Hsinchu Taiwan Natl Chiao Tung Univ Pervas AI Res PAIR Labs Hsinchu Taiwan

ISBN: (纸本)9781665448994

This paper presents a new attempt at using augmented normalizing flows (ANF) for lossy image compression. ANF is a specific type of normalizing flow models that augment the input with an independent noise, allowing a smoother transformation from the augmented input space to the latent space. Inspired by the fact that ANF can offer greater expressivity by stacking multiple variational autoencoders (VAE), we generalize the popular VAE-based compression framework by the autoencoding transforms of ANF. When evaluated on Kodak dataset, our ANF-based model provides 3.4% higher BD-rate saving as compared with a VAE-based baseline that implements hyper-prior with mean prediction. Interestingly, it benefits even more from the incorporation of a post-processing network, showing 11.8% rate saving as compared to 6.0% with the baseline plus post-processing.

关键词： computer vision Image coding conferences Stacking Low-pass filters Transforms Predictive models

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