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Why Object Detectors Fail: Investigating the Influence of the Dataset

Why Object Detectors Fail: Investigating the Influence of th...

引用

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

作者： Miller, Dimity Goode, Georgia Bennie, Callum Moghadam, Peyman Jurdak, Raja Queensland Univ Technol Brisbane Qld Australia CSIRO Data61 Robot & Autonomous Syst Melbourne Vic Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

A false negative in object detection describes an object that was not correctly localised and classified by a detector. In prior work, we introduced five 'false negative mechanisms' that identify the specific component inside the detector architecture that failed to detect the object. Using these mechanisms, we explore how different computer vision datasets and their inherent characteristics can influence object detector failures. Specifically, we investigate the false negative mechanisms of Faster R-CNN and RetinaNet across five computer vision datasets, namely Microsoft COCO, Pascal VOC, ExDark, ObjectNet, and COD10K. Our results show that object size and class influence the false negative mechanisms of object detectors. We also show that comparing the false negative mechanisms of a single object class across different datasets can highlight potentially unknown biases in datasets.

关键词： computer vision conferences Detectors Object detection computer architecture Market research pattern recognition

来源：评论

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Dual-Branch Collaborative Transformer for Virtual Try-On

Dual-Branch Collaborative Transformer for Virtual Try-On

引用

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

作者： Fenocchi, Emanuele Morelli, Davide Cornia, Marcella Baraldi, Lorenzo Cesari, Fabio Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy YOOX NET PORTER GRP Milan Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Image-based virtual try-on has recently gained a lot of attention in both the scientific and fashion industry communities due to its challenging setting and practical real-world applications. While pure convolutional approaches have been explored to solve the task, Transformer-based architectures have not received significant attention yet. Following the intuition that self- and cross-attention operators can deal with long-range dependencies and hence improve the generation, in this paper we extend a Transformer-based virtual try-on model by adding a dual-branch collaborative module that can exploit cross-modal information at generation time. We perform experiments on the VITON dataset, which is the standard benchmark for the task, and on a recently collected virtual try-on dataset with multi-category clothing, Dress Code. Experimental results demonstrate the effectiveness of our solution over previous methods and show that Transformer-based architectures can be a viable alternative for virtual try-on.

关键词： Convolutional codes Industries Clothing Collaboration computer architecture Transformers pattern recognition

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PAND: Precise Action recognition on Naturalistic Driving

PAND: Precise Action Recognition on Naturalistic Driving

引用

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

作者： Zhao, Hangyue Xiao, Yuchao Zhao, Yanyun Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Temporal action localization for untrimmed videos is a difficult problem in computer vision. It is challenge to infer the start and end of activity instances on small-scale datasets covering multi-view information accurately. In this paper, we propose an effective activity temporal localization and classification method to localize the temporal boundaries and predict the class label of activities for naturalistic driving. Our approach includes (i) a distraction behavior recognition and localization method in naturalistic driving videos on small-scale data sets, (ii) a strategy that uses multi-branch network to make full use of information from different channels, (iii)a post-processing method for selecting and correcting temporal range to ensure that our system finds accurate boundaries. In addition, the frame-level object detection information is also utilized. Extensive experiments prove the effectiveness of our method and we rank the 6th on the Test-A2 of the 6th AI City Challenge track 3.

关键词： Location awareness computer vision Urban areas Pipelines Object detection Data models pattern recognition

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Domain Adaptable Normalization for Semi-Supervised Action recognition in the Dark

Domain Adaptable Normalization for Semi-Supervised Action Re...

引用

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

作者： Liang, Zixi Chen, Jiajun Chen, Rui Zheng, Bingbing Zhou, Mingyue Gao, Huaien Lin, Shan Guangzhou Xi Ma Informat Technol Co Guangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Action recognition in the dark is gaining more and more attention with the rapid development of intelligent recognition applications in real-world applications, e.g. self-driving at night and night surveillance. However, limited by the expensive labeling cost, it is impractical to produce a large-scale labeled dataset only for dark environments. Therefore, a practical solution adopted is to transfer models trained from clear environments to dark environments through semi-supervised learning. However, prior works rely heavily on additional efforts such as extra annotations, or extra sensors. To this end, we proposed a novel and simple Domain Adaptable Normalization (DANorm) method to align different domains directly, which consists of feature normalization, angle constraint and the Pseudo-Label. Specifically, the proposed DANorm method enables the model automatically learning the associated features between labeled source domain and unlabeled target domain by constraining the feature subspace vectors. Experimental results show that our model achieves superiority performance on Semi-supervised ARID dataset.

关键词： Adaptation models computer vision Costs Surveillance conferences Semisupervised learning pattern recognition

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Dataset Distillation by Matching Training Trajectories

Dataset Distillation by Matching Training Trajectories

引用

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

作者： Cazenavette, George Wang, Tongzhou Torralba, Antonio Efros, Alexei A. Zhu, Jun-Yan Carnegie Mellon Univ Pittsburgh PA 15213 USA MIT Cambridge MA 02139 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Dataset distillation is the task of synthesizing a small dataset such that a model trained on the synthetic set will match the test accuracy of the model trained on the full dataset. In this paper, we propose a new formulation that optimizes our distilled data to guide networks to a similar state as those trained on real data across many training steps. Given a network, we train it for several iterations on our distilled data and optimize the distilled data with respect to the distance between the synthetically trained parameters and the parameters trained on real data. To efficiently obtain the initial and target network parameters for large-scale datasets, we pre-compute and store training trajectories of expert networks trained on the real dataset. Our method handily outperforms existing methods and also allows us to distill higher-resolution visual data.

关键词： Training Visualization computer vision conferences Trajectory Task analysis pattern matching

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The Effect of Improving Annotation Quality on Object Detection Datasets: A Preliminary Study

The Effect of Improving Annotation Quality on Object Detecti...

引用

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

作者： Ma, Jiaxin Ushiku, Yoshitaka Sagara, Miori OMRON SINIC X Corp Tokyo Japan Baobab Inc Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this study, we partially reannotate conventional benchmark datasets for object detection and check whether there is performance improvement/drop compared with the original annotations. Recent studies on the annotation qualities of ImageNet for image classification revealed some issues of how to associate only a single label to each image accurately. Object detection, on the other hand, should have other nontrivial issues because there are multiple objects in a single image, and realizing consistency among bounding boxes is challenging. A team of professional annotators was formed for MS COCO and Google Open Images datasets. To realize highly-consistent annotations, we prepared carefully designed guidelines for each category and selected quality inspectors who checked the annotation quality of each annotator. Finally, we applied conventional object detection methods for reannotated parts of each dataset. We found mixed results: whether the performance dropped or improved depended on each category and dataset.

关键词： Annotations conferences Object detection Machine learning Benchmark testing Internet pattern recognition

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ScanpathNet: A Recurrent Mixture Density Network for Scanpath Prediction

ScanpathNet: A Recurrent Mixture Density Network for Scanpat...

引用

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

作者： de Belen, Ryan Anthony Jalova Bednarz, Tomasz Sowmya, Arcot Univ New South Wales Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Understanding the mechanisms underlying human visual attention is an important research problem in cognitive neuroscience and computer vision. While existing models predict salient regions (i.e., saliency maps) and temporal sequences of eye fixations (i.e., scanpaths) in images, their designs often partially follow theoretical frameworks. Here, we introduce ScanpathNet, a deep learning model inspired by the latest theoretical model in neuroscience. It is 'guided' by a dynamic priority map influenced by semantic content and fixation history. The model leverages convolutional neural networks to extract rich semantic features, convolutional long short-term memory networks to model the inhibition of return mechanism and sequential dependencies of fixations, and mixture density networks to predict probability distributions of fixations for each pixel. Simulated human scanpaths can then be generated by sequentially sampling the output of the proposed model. Despite its simplicity, ScanpathNet showed promising qualitative and quantitative scanpath prediction performance in extensive experiments on numerous eye-tracking benchmark datasets.

关键词： Deep learning Visualization computer vision Computational modeling Semantics Predictive models Visual systems

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A Neural-network Enhanced Video Coding Framework beyond VVC

A Neural-network Enhanced Video Coding Framework beyond VVC

引用

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

作者： Li, Junru Li, Yue Lin, Chaoyi Zhang, Kai Zhang, Li Bytedance Inc Multimedia Lab San Diego CA 92122 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents a hybrid video compression framework, aiming at providing a demonstration of applying deep learning-based approaches beyond conventional coding framework. The proposed hybrid framework is established over the Enhanced Compression Model (ECM) of which the core is the Versatile Video Coding (VVC) standard. We propose to integrate a series of enhanced coding tools, such as block partitioning, intra prediction, and inter prediction to further remove the spatial and temporal redundancy. Moreover, deep learning-based technologies including loop filter and super resolution are involved to restore the compression distortion. Compared with the VVC software VTM-11.0, experimental results demonstrate the effectiveness of the proposed learning-based framework, leading to 25.81%, 35.08%, and 37.54% bit-rate savings for Y, Cb and Cr components, respectively under random access configuration. In addition, the proposed framework achieves 39.313 and 32.050 PSNRs in the test set under 1 Mbps and 0.1 Mbps video compression tracks of CLIC-2022. 33.522, 30.758, and 28.300 in terms of PSNR are obtained in 0.3 bpp, 0.15 bpp, and 0.075 bpp image compression tracks.

关键词： Video coding Image coding Filtering Redundancy Video compression Software pattern recognition

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Enriched Robust Multi-View Kernel Subspace Clustering

Enriched Robust Multi-View Kernel Subspace Clustering

引用

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

作者： Zhang, Mengyuan Liu, Kai Clemson Univ Clemson SC 29631 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Subspace clustering is to find underlying low-dimensional subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. Most existing methods suffer from two critical issues. First, they usually adopt a two-stage framework and isolate the processes of affinity learning, multi-view information fusion and clustering. Second, they assume the data lies in a linear subspace which may fail in practice as most real-world datasets may have non-linearity structures. To address the above issues, in this paper we propose a novel Enriched Robust Multi-View Kernel Subspace Clustering framework where the consensus affinity matrix is learned from both multi-view data and spectral clustering. Due to the objective and constraints which is difficult to optimize, we propose an iterative optimization method which is easy to implement and can yield closed solution in each step. Extensive experiments have validated the superiority of our method over state-of-the-art clustering methods.

关键词： computer vision Clustering methods conferences Optimization methods Benchmark testing Iterative methods Kernel

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Learned Compression of High Dimensional Image Datasets

Learned Compression of High Dimensional Image Datasets

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

作者： Cole, Elizabeth Meng, Qingxi Pauly, John Vasanawala, Shreyas Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Dept Radiol Stanford CA 94305 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In many applications, such as burst photography and magnetic resonance imaging (MRI), multiple images are acquired to reduce the noise of the eventual reconstructed image. However, this leads to very high dimensional datasets which have redundant information across the various acquired images. In MRI, multiple images are acquired via multiple RF coil arrays in the scanner. Afterwards, coil compression is performed to convert the original set of coil images into a smaller set of virtual coil images to enable smaller datasets and faster computation time. However, traditional iterative coil compression methods are lossy and time-consuming. In this work, we propose a novel neural network-based coil compression method in pursuit of higher reconstruction accuracy and faster coil compression. Our learned compression method achieves up to 1.5x lower NRMSE and up to 10 times runtime speed compared to traditional methods on a benchmark test dataset.

关键词： Radio frequency Photography Image coding Runtime Magnetic resonance imaging conferences pattern recognition

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