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

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HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening

HyperTransformer: A Textural and Spectral Feature Fusion Tra...

引用

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

作者： Bandara, Wele Gedara Chaminda Patel, Vishal M. Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Pansharpening aims to fuse a registered high-resolution panchromatic image (PAN) with a low-resolution hyperspectral image (LR-HSI) to generate an enhanced HSI with high spectral and spatial resolution. Existing pansharpening approaches neglect using an attention mechanism to transfer HR texture features from PAN to LR-HSI features, resulting in spatial and spectral distortions. In this paper, we present a novel attention mechanism for pansharpening called HyperTransformer, in which features of LR-HSI and PAN are formulated as queries and keys in a transformer, respectively. HyperTransformer consists of three main modules, namely two separate feature extractors for PAN and HSI, a multi-head feature soft-attention module, and a spatial-spectral feature fusion module. Such a network improves both spatial and spectral quality measures of the pansharpened HSI by learning cross feature space dependencies and long-range details of PAN and LR-HSI. Furthermore, HyperTransformer can be utilized across multiple spatial scales at the backbone for obtaining improved performance. Extensive experiments conducted on three widely used datasets demonstrate that HyperTransformer achieves significant improvement over the state-of-the-art methods on both spatial and spectral quality measures. Implementation code and pre-trained weights can be accessed at https://***/wgcban/Hypertranfeormer.

关键词： computer vision Codes Fuses Pansharpening Feature extraction Transformers Extraterrestrial measurements

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An Empirical Study of End-to-End Temporal Action Detection

An Empirical Study of End-to-End Temporal Action Detection

引用

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

作者： Liu, Xiaolong Bai, Song Bai, Xiang Huazhong Univ Sci & Technol Wuhan Peoples R China ByteDance Inc Wuhan Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Temporal action detection ('TAD) is an important yet challenging task in video understanding. It aims to simultaneously predict the semantic label and the temporal interval of every action instance in an untrimmed video. Rather than end-to-end learning, most existing methods adopt a head-only learning paradigm, where the video encoder is pre-trained for action classification, and only the detection head upon the encoder is optimized for TAD. The effect of end-to-end learning is not systematically evaluated. Besides, there lacks an in-depth study on the efficiency-accuracy trade-off in end-to-end TAD. In this paper, we present an empirical study of end-to-end temporal action detection. We validate the advantage of end-to-end learning over head-only learning and observe up to 11% performance improvement. Besides, we study the effects of multiple design choices that affect the TAD performance and speed, including detection head, video encoder, and resolution of input videos. Based on the findings, we build a mid-resolution baseline detector, which achieves the state-of-the-art performance of end-to-end methods while running more than 4x faster. We hope that this paper can serve as a guide for end-to-end learning and inspire future research in this field.

关键词： Training computer vision Codes Computational modeling Semantics Buildings Detectors

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Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification

Contrastive Learning based Hybrid Networks for Long-Tailed I...

引用

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

作者： Wang, Peng Han, Kai Wei, Xiu-Shen Zhang, Lei Wang, Lei Univ Wollongong Wollongong NSW Australia Univ Bristol Bristol Avon England Nanjing Univ Sci & Technol Nanjing Peoples R China Northwestern Polytech Univ Xian Peoples R China

ISBN: (纸本)9781665445092

Learning discriminative image representations plays a vital role in long-tailed image classification because it can ease the classifier learning in imbalanced cases. Given the promising performance contrastive learning has shown recently in representation learning, in this work, we explore effective supervised contrastive learning strategies and tailor them to learn better image representations from imbalanced data in order to boost the classification accuracy thereon. Specifically, we propose a novel hybrid network structure being composed of a supervised contrastive loss to learn image representations and a cross-entropy loss to learn classifiers, where the learning is progressively transited from feature learning to the classifier learning to embody the idea that better features make better classifiers. We explore two variants of contrastive loss for feature learning, which vary in the forms but share a common idea of pulling the samples from the same class together in the normalized embedding space and pushing the samples from different classes apart. One of them is the recently proposed supervised contrastive (SC) loss, which is designed on top of the state-of-the-art unsupervised contrastive loss by incorporating positive samples from the same class. The other is a prototypical supervised contrastive (PSC) learning strategy which addresses the intensive memory consumption in standard SC loss and thus shows more promise under limited memory budget. Extensive experiments on three long-tailed classification datasets demonstrate the advantage of the proposed contrastive learning based hybrid networks in long-tailed classification.

关键词： Training computer vision Memory management Graphics processing units Image representation pattern recognition Proposals

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Discovering Interpretable Latent Space Directions of GANs Beyond Binary Attributes

Discovering Interpretable Latent Space Directions of GANs Be...

引用

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

作者： Yang, Huiting Chai, Liangyu Wen, Qiang Zhao, Shuang Sun, Zixun He, Shengfeng South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Tencent Inc Interact Entertainment Grp Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Generative adversarial networks (GANs) learn to map noise latent vectors to high-fidelity image outputs. It is found that the input latent space shows semantic correlations with the output image space. Recent works aim to interpret the latent space and discover meaningful directions that correspond to human interpretable image transformations. However, these methods either rely on explicit scores of attributes (e.g., memorability) or are restricted to binary ones (e.g., gender), which largely limits the applicability of editing tasks, especially for free-form artistic tasks like style/anime editing. In this paper, we propose an adversarial method, AdvStyle, for discovering interpretable directions in the absence of well-labeled scores or binary attributes. In particular, the proposed adversarial method simultaneously optimizes the discovered directions and the attribute assessor using the target attribute data as positive samples, while the generated ones being negative. In this way, arbitrary attributes can be edited by collecting positive data only, and the proposed method learns a controllable representation enabling manipulation of non-binary attributes like anime styles and facial characteristics. Moreover, the proposed learning strategy attenuates the entanglement between attributes, such that multiattribute manipulation can be easily achieved without any additional constraint. Furthermore, we reveal several interesting semantics with the involuntarily learned negative directions. Extensive experiments on 9 anime attributes and 7 human attributes demonstrate the effectiveness of our adversarial approach qualitatively and quantitatively. Code is available at https://***/BERYLSHEEP/AdvStyle.

关键词： computer vision Correlation Codes Semantics Generative adversarial networks pattern recognition Task analysis

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BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification

BiCnet-TKS: Learning Efficient Spatial-Temporal Representati...

引用

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

作者： Hou, Ruibing Chang, Hong Ma, Bingpeng Huang, Rui Shan, Shiguang Chinese Acad Sci Inst Comp Technol CAS Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Univ Hong Kong Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Guangdong Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai 200031 Peoples R China

ISBN: (纸本)9781665445092

In this paper, we present an efficient spatial-temporal representation for video person re-identification (reID). Firstly, we propose a Bilateral Complementary Network (BiCnet) for spatial complementarity modeling. Specifically, BiCnet contains two branches. Detail Branch processes frames at original resolution to preserve the detailed visual clues, and Context Branch with a down-sampling strategy is employed to capture long-range contexts. On each branch, BiCnet appends multiple parallel and diverse attention modules to discover divergent body parts for consecutive frames, so as to obtain an integral characteristic of target identity. Furthermore, a Temporal Kernel Selection (TKS) block is designed to capture short-term as well as long-term temporal relations by an adaptive mode. TKS can be inserted into BiCnet at any depth to construct BiCnet-TKS for spatial-temporal modeling. Experimental results on multiple benchmarks show that BiCnet-TKS outperforms state-of-the-arts with about 50% less computations.

关键词： Visualization computer vision Codes Computational modeling Benchmark testing pattern recognition Computational efficiency

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Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization

Actor-Context-Actor Relation Network for Spatio-Temporal Act...

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

作者： Pan, Junting Chen, Siyu Shou, Mike Zheng Liu, Yu Shao, Jing Li, Hongsheng Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Columbia Univ New York NY 10027 USA Xidian Univ Sch CST Xian Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Localizing persons and recognizing their actions from videos is a challenging task towards high-level video understanding. Recent advances have been achieved by modeling direct pairwise relations between entities. In this paper, we take one step further, not only model direct relations between pairs but also take into account indirect higher-order relations established upon multiple elements. We propose to explicitly model the Actor-Context-Actor Relation, which is the relation between two actors based on their interactions with the context. To this end, we design an Actor-Context-Actor Relation Network (ACAR-Net) which builds upon a novel High-order Relation Reasoning Operator and an Actor-Context Feature Bank to enable indirect relation reasoning for spatio-temporal action localization. Experiments on AVA and UCF101-24 datasets show the advantages of modeling actor-context-actor relations, and visualization of attention maps further verifies that our model is capable of finding relevant higher-order relations to support action detection. Notably, our method ranks first in the AVA-Kinetics action localization task of ActivityNet Challenge 2020, outperforming other entries by a significant margin (+6.71 mAP). The code is available online.(1)

关键词： Location awareness Visualization computer vision Cognition pattern recognition Complexity theory Task analysis

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Efficient Regional Memory Network for Video Object Segmentation

Efficient Regional Memory Network for Video Object Segmentat...

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

作者： Xie, Haozhe Yao, Hongxun Zhou, Shangchen Zhang, Shengping Sun, Wenxiu Harbin Inst Technol Harbin Peoples R China SenseTime Res & Tetras AI Hong Kong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore Peng Cheng Lab Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9781665445092

Recently, several Space-Time Memory based networks have shown that the object cues (e.g. video frames as well as the segmented object masks) from the past frames are useful for segmenting objects in the current frame. However, these methods exploit the information from the memory by global-to-global matching between the current and past frames, which lead to mismatching to similar objects and high computational complexity. To address these problems, we propose a novel local-to-local matching solution for semi-supervised VOS, namely Regional Memory Network (RMNet). In RMNet, the precise regional memory is constructed by memorizing local regions where the target objects appear in the past frames. For the current query frame, the query regions are tracked and predicted based on the optical flow estimated from the previous frame. The proposed local-to-local matching effectively alleviates the ambiguity of similar objects in both memory and query frames, which allows the information to be passed from the regional memory to the query region efficiently and effectively. Experimental results indicate that the proposed RMNet performs favorably against state-of-the-art methods on the DAVIS and YouTube-VOS datasets.

关键词： computer vision Target tracking Memory management Object segmentation pattern recognition Computational complexity Optical flow

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Identity Preserving Loss for Learned Image Compression

Identity Preserving Loss for Learned Image Compression

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

作者： Xiao, Jiuhong Aggarwal, Lavisha Banerjee, Prithviraj Aggarwal, Manoj Medioni, Gerard NYU 550 1St Ave New York NY 10012 USA Amazon Seattle WA 98109 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep learning model inference on embedded devices is challenging due to the limited availability of computation resources. A popular alternative is to perform model inference on the cloud, which requires transmitting images from the embedded device to the cloud. Image compression techniques are commonly employed in such cloud-based architectures to reduce transmission latency over low bandwidth networks. This work proposes an end-to-end image compression framework that learns domain-specific features to achieve higher compression ratios than standard HEVC/JPEG compression techniques while maintaining accuracy on downstream tasks (e.g., recognition). Our framework does not require fine-tuning of the downstream task, which allows us to drop-in any off-the-shelf downstream task model without retraining. We choose faces as an application domain due to the ready availability of datasets and off-the-shelf recognition models as representative downstream tasks. We present a novel Identity Preserving Reconstruction (IPR) loss function which achieves Bits-Per-Pixel (BPP) values that are similar to 38% and similar to 42% of CRF-23 HEVC compression for LFW (low-resolution) and CelebA-HQ (high-resolution) datasets, respectively, while maintaining parity in recognition accuracy. The superior compression ratio is achieved as the model learns to retain the domain-specific features (e.g., facial features) while sacrificing details in the background. Furthermore, images reconstructed by our proposed compression model are robust to changes in downstream model architectures. We show at-par recognition performance on the LFW dataset with an unseen recognition model while retaining a lower BPP value of similar to 38% of CRF-23 HEVC compression.

关键词： Image coding Image recognition Face recognition Computational modeling Intellectual property computer architecture Robustness

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Rethinking Deep Face Restoration

Rethinking Deep Face Restoration

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

作者： Zhao, Yang Su, Yu-Chuan Chu, Chun-Te Li, Yandong Renn, Marius Zhu, Yukun Chen, Changyou Jia, Xuhui Univ Buffalo Buffalo NY 14260 USA Google Res Mountain View CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

A model that can authentically restore a low-quality face image to a high-quality one can benefit many applications. While existing approaches for face restoration make significant progress in generating high-quality faces, they often fail to preserve facial features that compromise the authenticity of reconstructed faces. Because the human visual system is very sensitive to faces, even minor changes may significantly degrade the perceptual quality. In this work, we argue that the problems of existing models can be traced down to the two sub-tasks of the face restoration problem, i.e. face generation and face reconstruction, and the fragile balance between them. Based on the observation, we propose a new face restoration model that improves both generation and reconstruction. Besides the model improvement, we also introduce a new evaluation metric for measuring models' ability to preserve the identity in the restored faces. Extensive experiments demonstrate that our model achieves state-of-the-art performance on multiple face restoration benchmarks, and the proposed metric has a higher correlation with user preference. The user study shows that our model produces higher quality faces while better preserving the identity 86.4% of the time compared with state-of-the-art methods.

关键词： Measurement computer vision Correlation Face recognition Computational modeling Visual systems Image restoration

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AdderSR: Towards Energy Efficient Image Super-Resolution

AdderSR: Towards Energy Efficient Image Super-Resolution

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

作者： Song, Dehua Wang, Yunhe Chen, Hanting Xu, Chang Xu, Chunjing Tao, Dacheng Huawei Technol Noahs Ark Lab Shenzhen Peoples R China Peking Univ Beijing Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

This paper studies the single image super-resolution problem using adder neural networks (AdderNets). Compared with convolutional neural networks, AdderNets utilize additions to calculate the output features thus avoid massive energy consumptions of conventional multiplications. However, it is very hard to directly inherit the existing success of AdderNets on large-scale image classification to the image super-resolution task due to the different calculation paradigm. Specifically, the adder operation cannot easily learn the identity mapping, which is essential for image processing tasks. In addition, the functionality of high-pass filters cannot be ensured by AdderNets. To this end, we thoroughly analyze the relationship between an adder operation and the identity mapping and insert shortcuts to enhance the performance of SR models using adder networks. Then, we develop a learnable power activation for adjusting the feature distribution and refining details. Experiments conducted on several benchmark models and datasets demonstrate that, our image super-resolution models using AdderNets can achieve comparable performance and visual quality to that of their CNN baselines with an about 2.5x reduction on the energy consumption. The codes are available at: https://***/huawei-noah/AdderNet.

关键词： Energy consumption Visualization Computational modeling Superresolution Refining Neural networks pattern recognition

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