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UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

UP-DETR: Unsupervised Pre-training for Object Detection with...

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

作者： Dai, Zhigang Cai, Bolun Lin, Yugeng Chen, Junying South China Univ Technol Sch Software Engn Guangzhou Peoples R China Tencent Wechat AI Shenzhen Peoples R China South China Univ Technol Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou Peoples R China

ISBN: (纸本)9781665445092

Object detection with transformers (DETR) reaches competitive performance with Faster R-CNN via a transformer encoder-decoder architecture. Inspired by the great success of pre-training transformers in natural language processing, we propose a pretext task named random query patch detection to Unsupervisedly Pre-train DETR (UP-DETR) for object detection. Specifically, we randomly crop patches from the given image and then feed them as queries to the decoder. The model is pre-trained to detect these query patches from the original image. During the pre-training, we address two critical issues: multi-task learning and multi-query localization. (1) To trade off classification and localization preferences in the pretext task, we freeze the CNN backbone and propose a patch feature reconstruction branch which is jointly optimized with patch detection. (2) To perform multi-query localization, we introduce UP-DETR from single-query patch and extend it to multi-query patches with object query shuffle and attention mask. In our experiments, UP-DETR significantly boosts the performance of DETR with faster convergence and higher average precision on object detection, one-shot detection and panoptic segmentation.

关键词： Location awareness Training data Object detection Transformers Feature extraction Natural language processing pattern recognition

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LFNAT 2023 Challenge on Light Field Depth Estimation: Methods and Results

LFNAT 2023 Challenge on Light Field Depth Estimation: Method...

引用

2023 ieee/cvf conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Sheng, Hao Liu, Yebin Yu, Jingyi Wu, Gaochang Xiong, Wei Guo, Longzhao Xie, Yanlin Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Wang, Tun Yang, Da Cui, Zhenglong Wang, Sizhe Zhao, Mingyuan Wang, Qiong Chen, Qianyu Liang, Zhengyu Wang, Yingqian Yang, Jungang Yang, Xueting Deng, Junli Cong, Ruixuan Chen, Rongshan State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China Tsinghua University China Shanghaitech University China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Beijing Meet Yuan Co. Ltd China Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada College of Computer Science and Technology Zhejiang University of Technology China National University of Defense Technology China School of Information and Communication Engineering Communication University of China China

ISBN: (纸本)9798350302493

This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at predicting disparity information of central view image in a light field (i.e., pixel offset between central view image and adjacent view image). Compared to multi-view stereo matching, light field depth estimation emphasizes efficient utilization of the 2D angular information from multiple regularly varying views. This challenge specifies UrbanLF [20] light field dataset as the sole data source. There are two phases in total: submission phase and final evaluation phase, in which 75 registered participants successfully submit their predicted results in the first phase and 7 eligible teams compete in the second phase. The performance of all submissions is carefully reviewed and shown in this paper as a new standard for the current state-of-the-art in light field depth estimation. Moreover, the implementation details of these methods are also provided to stimulate related advanced research. © 2023 ieee.

关键词： Stereo image processing

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Towards Accurate 3D Human Motion Prediction from Incomplete Observations

Towards Accurate 3D Human Motion Prediction from Incomplete ...

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

作者： Cui, Qiongjie Sun, Huaijiang Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665445092

Predicting accurate and realistic future human poses from historically observed sequences is a fundamental task in the intersection of computer vision, graphics, and artificial intelligence. Recently, continuous efforts have been devoted to addressing this issue, which has achieved remarkable progress. However, the existing work is seriously limited by complete observation, that is, once the historical motion sequence is incomplete (with missing values), it can only produce unexpected predictions or even deformities. Furthermore, due to inevitable reasons such as occlusion and the lack of equipment precision, the incompleteness of motion data occurs frequently, which hinders the practical application of current algorithms. In this work, we first notice this challenging problem, i.e., how to generate high-fidelity human motion predictions from incomplete observations. To solve it, we propose a novel multi-task graph convolutional network (MT-GCN). Specifically, the model involves two branches, in which the primary task is to focus on forecasting future 3D human actions accurately, while the auxiliary one is to repair the missing value of the incomplete observation. Both of them are integrated into a unified framework to share the spatio-temporal representation, which improves the final performance of each collaboratively. On three large-scale datasets, for various data missing scenarios in the real world, extensive experiments demonstrate that our approach is consistently superior to the state-of-the-art methods in which the missing values from incomplete observations are not explicitly analyzed.

关键词： Graphics computer vision Solid modeling Three-dimensional displays Predictive models Maintenance engineering Prediction algorithms

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Stacked Deep Multi-Scale Hierarchical Network for Fast Bokeh Effect Rendering from a Single Image

Stacked Deep Multi-Scale Hierarchical Network for Fast Bokeh...

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

作者： Dutta, Saikat Das, Sourya Dipta Shah, Nisarg A. Tiwari, Anil Kumar IIT Madras Chennai Tamil Nadu India Jadavpur Univ Kolkata India IIT Jodhpur Jodhpur Rajasthan India

ISBN: (纸本)9781665448994

The Bokeh Effect is one of the most desirable effects in photography for rendering artistic and aesthetic photos. Usually, it requires a DSLR camera with different aperture and shutter settings and certain photography skills to generate this effect. In smartphones, computational methods and additional sensors are used to overcome the physical lens and sensor limitations to achieve such effect. Most of the existing methods utilized additional sensor's data or pre-trained network for fine depth estimation of the scene and sometimes use portrait segmentation pretrained network module to segment salient objects in the image. Because of these reasons, networks have many parameters, become runtime intensive and unable to run in mid-range devices. In this paper, we used an end-to-end Deep Multi-Scale Hierarchical Network (DMSHN) model for direct Bokeh effect rendering of images captured from the monocular camera. To further improve the perceptual quality of such effect, a stacked model consisting of two DMSHN modules is also proposed. Our model does not rely on any pretrained network module for Monocular Depth Estimation or Saliency Detection, thus significantly reducing the size of model and run time. Stacked DMSHN achieves state-of-the-art results on a large scale EBB! dataset with around 6x less runtime compared to the current state-of-the-art model in processing HD quality images.

关键词： Photography Image segmentation Runtime Estimation Rendering (computer graphics) Cameras Real-time systems

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Spatially-invariant Style-codes Controlled Makeup Transfer

Spatially-invariant Style-codes Controlled Makeup Transfer

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

作者： Deng, Han Han, Chu Cai, Hongmin Han, Guoqiang He, Shengfeng South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Guangdong Prov Peoples Hosp Guangdong Acad Med Sci Guangzhou Peoples R China

ISBN: (纸本)9781665445092

Transferring makeup from the misaligned reference image is challenging. Previous methods overcome this barrier by computing pixel-wise correspondences between two images, which is inaccurate and computational-expensive. In this paper, we take a different perspective to break down the makeup transfer problem into a two-step extraction-assignment process. To this end, we propose a Style-based Controllable GAN model that consists of three components, each of which corresponds to target style-code encoding, face identity features extraction, and makeup fusion, respectively. In particular, a Part-specific Style Encoder encodes the component-wise makeup style of the reference image into a style-code in an intermediate latent space W. The style-code discards spatial information and therefore is invariant to spatial misalignment. On the other hand, the style-code embeds component-wise information, enabling flexible partial makeup editing from multiple references. This style-code, together with source identity features, is integrated into a Makeup Fusion Decoder equipped with multiple AdaIN layers to generate the final result. Our proposed method demonstrates great flexibility on makeup transfer by supporting makeup removal, shade-controllable makeup transfer, and part-specific makeup transfer, even with large spatial misalignment. Extensive experiments demonstrate the superiority of our approach over state-of-the-art methods.

关键词： computer vision Codes Face recognition Computational modeling Aerospace electronics Feature extraction Encoding

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Hierarchical Video Prediction using Relational Layouts for Human-Object Interactions

Hierarchical Video Prediction using Relational Layouts for H...

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

作者： Bodla, Navaneeth Shrivastava, Gaurav Chellappa, Rama Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9781665445092

Learning to model and predict how humans interact with objects while performing an action is challenging, and most of the existing video prediction models are ineffective in modeling complicated human-object interactions. Our work builds on hierarchical video prediction models, which disentangle the video generation process into two stages: predicting a high-level representation, such as pose sequence, and then learning a pose-to-pixels translation model for pixel generation. An action sequence for a human-object interaction task is typically very complicated, involving the evolution of pose, person's appearance, object locations, and object appearances over time. To this end, we propose a Hierarchical Video Prediction model using Relational Layouts. In the first stage, we learn to predict a sequence of layouts. A layout is a high-level representation of the video containing both pose and objects' information for every frame. The layout sequence is learned by modeling the relationships between the pose and objects using relational reasoning and recurrent neural networks. The layout sequence acts as a strong structure prior to the second stage that learns to map the layouts into pixel space. Experimental evaluation of our method on two datasets, UMD-HOI and Bimanual, shows significant improvements in standard video evaluation metrics such as LPIPS, PSNR, and SSIM. We also perform a detailed qualitative analysis of our model to demonstrate various generalizations.

关键词： Measurement computer vision Recurrent neural networks Computational modeling Layout Predictive models Cognition

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Towards Extremely Compact RNNs for Video recognition with Fully Decomposed Hierarchical Tucker Structure

Towards Extremely Compact RNNs for Video Recognition with Fu...

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

作者： Yin, Miao Liao, Siyu Liu, Xiao-Yang Wang, Xiaodong Yuan, Bo Rutgers State Univ Newark NJ 07101 USA Amazon Seattle WA USA Columbia Univ New York NY 10027 USA

ISBN: (纸本)9781665445092

Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling. However, when processing high-dimensional data, RNNs typically require very large model sizes, thereby bringing a series of deployment challenges. Although various prior works have been proposed to reduce the RNN model sizes, executing RNN models in the resource-restricted environments is still a very challenging problem. In this paper, we propose to develop extremely compact RNN models with fully decomposed hierarchical Tucker (FDHT) structure. The HT decomposition does not only provide much higher storage cost reduction than the other tensor decomposition approaches, but also brings better accuracy performance improvement for the compact RNN models. Meanwhile, unlike the existing tensor decomposition-based methods that can only decompose the input-to-hidden layer of RNNs, our proposed fully decomposition approach enables the comprehensive compression for the entire RNN models with maintaining very high accuracy. Our experimental results on several popular video recognition datasets show that, our proposed fully decomposed hierarchical tucker-based LSTM (FDHT-LSTM) is extremely compact and highly efficient. To the best of our knowledge, FDHT-LSTM, for the first time, consistently achieves very high accuracy with only few thousand parameters (3,132 to 8,808) on different datasets. Compared with the state-of-the-art compressed RNN models, such as TT-LSTM, TR-LSTM and BT-LSTM, our FDHT-LSTM simultaneously enjoys both order-of-magnitude (3,985x to 10,711x) fewer parameters and significant accuracy improvement (0.6% to 12.7%).

关键词： computer vision Analytical models Tensors Recurrent neural networks Sequences Costs Computational modeling

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M3DSSD: Monocular 3D Single Stage Object Detector

M3DSSD: Monocular 3D Single Stage Object Detector

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

作者： Luo, Shujie Dai, Hang Shao, Ling Ding, Yong Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China Zhejiang Univ Sch Micronano Elect Hangzhou Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

In this paper, we propose a Monocular 3D Single Stage object Detector (M3DSSD) with feature alignment and asymmetric non-local attention. Current anchor-based monocular 3D object detection methods suffer from feature mismatching. To overcome this, we propose a two-step feature alignment approach. In the first step, the shape alignment is performed to enable the receptive field of the feature map to focus on the pre-defined anchors with high confidence scores. In the second step, the center alignment is used to align the features at 2D/3D centers. Further, it is often difficult to learn global information and capture long-range relationships, which are important for the depth prediction of objects. Therefore, we propose a novel asymmetric non-local attention block with multi-scale sampling to extract depth-wise features. The proposed M3DSSD achieves significantly better performance than the monocular 3D object detection methods on the KITH dataset, in both 3D object detection and bird's eye view tasks.

关键词： Solid modeling computer vision Three-dimensional displays Shape Computational modeling Object detection Detectors

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A Closer Look at Self-training for Zero-Label Semantic Segmentation

A Closer Look at Self-training for Zero-Label Semantic Segme...

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

作者： Pastore, Giuseppe Cermelli, Fabio Xian, Yongqin Mancini, Massimiliano Akata, Zeynep Caputo, Barbara Politecn Torino Turin Italy Italian Inst Technol Genoa Italy MPI Informat Saarbrucken Germany Univ Tubingen Tubingen Germany MPI Intelligent Syst Saarbrucken Germany

ISBN: (纸本)9781665448994

Being able to segment unseen classes not observed during training is an important technical challenge in deep learning, because of its potential to reduce the expensive annotation required for semantic segmentation. Prior zero-label semantic segmentation works approach this task by learning visual-semantic embeddings or generative models. However, they are prone to overfitting on the seen classes because there is no training signal for them. In this paper, we study the challenging generalized zero-label semantic segmentation task where the model has to segment both seen and unseen classes at test time. We assume that pixels of unseen classes could be present in the training images but without being annotated. Our idea is to capture the latent information on unseen classes by supervising the model with self-produced pseudo-labels for unlabeled pixels. We propose a consistency regularizer to filter out noisy pseudolabels by taking the intersections of the pseudo-labels generated from different augmentations of the same image. Our framework generates pseudo-labels and then retrain the model with human-annotated and pseudo-labelled data. This procedure is repeated for several iterations. As a result, our approach achieves the new state-of-the-art on PascalVOC12 and COCO-stuff datasets in the challenging generalized zero-label semantic segmentation setting, surpassing other existing methods addressing this task with more complex strategies. Code can be found at https: //***/giuseppepastore10/STRICT.

关键词： Training Image segmentation Semantics Pipelines Predictive models Information filters pattern recognition

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LAFEAT: Piercing Through Adversarial Defenses with Latent Features

LAFEAT: Piercing Through Adversarial Defenses with Latent Fe...

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

作者： Yu, Yunrui Gao, Xitong Xu, Cheng-Zhong Univ Macau Macau Sar Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Deep convolutional neural networks are susceptible to adversarial attacks. They can be easily deceived to give an incorrect output by adding a tiny perturbation to the input. This presents a great challenge in making CNNs robust against such attacks. An influx of new defense techniques have been proposed to this end. In this paper, we show that latent features in certain "robust" models are surprisingly susceptible to adversarial attacks. On top of this, we introduce a unified l(infinity)-norm white-box attack algorithm which harnesses latent features in its gradient descent steps, namely LAFEAT. We show that not only is it computationally much more efficient for successful attacks, but it is also a stronger adversary than the current state-of-the-art across a wide range of defense mechanisms. This suggests that model robustness could be contingent on the effective use of the defender's hidden components, and it should no longer be viewed from a holistic perspective.

关键词： Degradation Schedules computer vision Computational modeling Perturbation methods Lattices Robustness

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