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Blind Face Restoration via Integrating Face Shape and Generative Priors

Blind Face Restoration via Integrating Face Shape and Genera...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhu, Feida Zhu, Junwei Chu, Wenqing Zhang, Xinyi Ji, Xiaozhong Wang, Chengjie Tai, Ying Tencent Youtu Lab Shenzhen Peoples R China

ISBN: (纸本)9781665469463

Blind face restoration, which aims to reconstruct high-quality images from low-quality inputs, can benefit many applications. Although existing generative-based methods achieve significant progress in producing high-quality images, they often fail to restore natural face shapes and high-fidelity facial details from severely-degraded inputs. In this work, we propose to integrate shape and generative priors to guide the challenging blind face restoration. Firstly, we set up a shape restoration module to recover reasonable facial geometry with 3D reconstruction. Secondly, a pretrained facial generator is adopted as decoder to generate photo-realistic high-resolution images. To ensure high-fidelity, hierarchical spatial features extracted from the low-quality inputs and rendered 3D images are inserted into the decoder with our proposed Adaptive Feature Fusion Block (AFFB). Moreover, we introduce hybrid-level losses to jointly train the shape and generative priors together with other network parts such that these two priors better adapt to our blind face restoration task. The proposed Shape and Generative Prior integrated Network (SGPN) can restore high-quality images with clear face shapes and realistic facial details. Experimental results on synthetic and real-world datasets demonstrate SGPN performs favorably against state-of-the-art blind face restoration methods.

关键词： Geometry Three-dimensional displays Shape Face recognition Feature extraction Image restoration Decoding

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Seeing What You Miss: vision-Language Pre-training with Semantic Completion Learning

Seeing What You Miss: Vision-Language Pre-training with Sema...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ji, Yatai Tu, Rongcheng Jiang, Jie Kong, Weijie Cai, Chengfei Zhao, Wenzhe Wang, Hongfa Yang, Yujiu Liu, Wei Tsinghua Univ Beijing Peoples R China Tencent Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350301298

Cross-modal alignment is essential for vision-language pre-training (VLP) models to learn the correct corresponding information across different modalities. For this purpose, inspired by the success of masked language modeling (MLM) tasks in the NLP pre-training area, numerous masked modeling tasks have been proposed for VLP to further promote cross-modal interactions. The core idea of previous masked modeling tasks is to focus on reconstructing the masked tokens based on visible context for learning local-to-local alignment. However, most of them pay little attention to the global semantic features generated for the masked data, resulting in a limited cross-modal alignment ability of global representations. Therefore, in this paper, we propose a novel Semantic Completion Learning (SCL) task, complementary to existing masked modeling tasks, to facilitate global-to-local alignment. Specifically, the SCL task complements the missing semantics of masked data by capturing the corresponding information from the other modality, promoting learning more representative global features which have a great impact on the performance of downstream tasks. Moreover, we present a flexible vision encoder, which enables our model to perform image-text and video-text multimodal tasks simultaneously. Experimental results show that our proposed method obtains state-of-the-art performance on various vision-language benchmarks, such as visual question answering, image-text retrieval, and video-text retrieval.

关键词： Multi-modal learning

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GOOD FEATURES TO TRACK

GOOD FEATURES TO TRACK

引用

1994 ieee computer-Society conference on computer vision and pattern recognition

作者： SHI, JB TOMASI, C CORNELL UNIV DEPT COMP SCIITHACANY 14853

ISBN: (纸本)0818658258

No feature-based vision system can work unless good features can be identified and tracked from frame to frame. Although tracking itself is by and large a solved problem, selecting features that can be tracked well and correspond to physical points in the world is still hard. We propose a feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world. These methods are based on a new tracking algorithm that extends previous Newton-Raphson style search methods to work under affine image transformations. We test performance with several simulations and experiments.

关键词： computer vision

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Revisiting Counterfactual Problems in Referring Expression Comprehension

Revisiting Counterfactual Problems in Referring Expression C...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yu, Zhihan Li, Ruifan Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350353006

Traditional referring expression comprehension (REC) aims to locate the target referent in an image guided by a text query. Several previous methods have studied on the Counterfactual problem in REC (C-REC) where the objects for a given query cannot be found in the image. However, these methods focus on the overall image-text or specific attribute mismatch only. In this paper, we address the C-REC problem from a deep perspective of fine-grained attributes. To this aim, we first propose a fine-grained counterfactual sample generation method to construct C-REC datasets. Specifically, we leverage pre-trained language model such as BERT to modify the attribute words in the queries, obtaining the corresponding counterfactual samples. Furthermore, we propose a C-REC framework. We first adopt three encoders to extract image, text and attribute features. Then, our dual-branch attentive fusion module fuses these crossmodal features with two branches by an attention mechanism. At last, two prediction heads generate a bounding box and a counterfactual label, respectively. In addition, we incorporate contrastive learning with the generated counterfactual samples as negatives to enhance the counterfactual perception. Extensive experiments show that our framework achieves promising performance on both public REC datasets RefCOCO/+/g and our constructed C-REC datasets C-RefCOCO/+/g. The code and data are available at https://***/Glacier0012/CREC.

关键词： Counterfactual Generation Referring Expression Comprehension vision and Language

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Attribute-Driven Feature Disentangling and Temporal Aggregation for Video Person Re-Identification 32

Attribute-Driven Feature Disentangling and Temporal Aggregat...

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32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhao, Yiru Shen, Xu Jin, Zhongming Lu, Hongtao Hua, Xian-sheng Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Dept Comp Sci & Engn Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai Peoples R China Alibaba Grp Alibaba Damo Acad Hangzhou Peoples R China

ISBN: (纸本)9781728132938

Video-based person re-identification plays an important role in surveillance video analysis, expanding image-based methods by learning features of multiple frames. Most existing methods fuse features by temporal average-pooling, without exploring the different frame weights caused by various viewpoints, poses, and occlusions. In this paper, we propose an attribute-driven method for feature disentangling and frame re-weighting. The features of single frames are disentangled into groups of sub-features, each corresponds to specific semantic attributes. The sub-features are re-weighted by the confidence of attribute recognition and then aggregated at the temporal dimension as the final representation. By means of this strategy, the most informative regions of each frame are enhanced and contributes to a more discriminative sequence representation. Extensive ablation studies demonstrate the effectiveness of feature disentangling as well as temporal re-weighting. The experimental results on the iLIDS-VID, PRID-2011 and MARS datasets demonstrate that our proposed method outperforms existing state-of-the-art approaches.

关键词： Categorization Deep Learning recognition: Detection Retrieval vision Applications and Systems

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DiffusionCLIP Text-Guided Diffusion Models for Robust Image Manipulation

DiffusionCLIP Text-Guided Diffusion Models for Robust Image ...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kim, Gwanghyun Kwon, Taesung Ye, Jong Chul Korea Adv Inst Sci & Technol KAIST Dept Bio & Brain Engn Daejeon South Korea Korea Adv Inst Sci & Technol KAIST Kim Jaechul Grad Sch AI Daejeon South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, GAN inversion methods combined with Contrastive Language-Image Pretraining (CLIP) enables zeroshot image manipulation guided by text prompts. However, their applications to diverse real images are still difficult due to the limited GAN inversion capability. Specifically, these approaches often have difficulties in reconstructing images with novel poses, views, and highly variable contents compared to the training data, altering object identity, or producing unwanted image artifacts. To mitigate these problems and enable faithful manipulation of real images, we propose a novel method, dubbed DiffusionCLIP, that performs textdriven image manipulation using diffusion models. Based on full inversion capability and high-quality image generation power of recent diffusion models, our method performs zeroshot image manipulation successfully even between unseen domains and takes another step towards general application by manipulating images from a widely varying ImageNet dataset. Furthermore, we propose a novel noise combination method that allows straightforward multi-attribute manipulation. Extensive experiments and human evaluation confirmed robust and superior manipulation performance of our methods compared to the existing baselines.

关键词： computer vision Codes Image synthesis Machine vision Computational modeling Training data pattern recognition

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Constrained Clustering and Its Application to Face Clustering in Videos

Constrained Clustering and Its Application to Face Clusterin...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wu, Baoyuan Zhang, Yifan Hu, Bao-Gang Ji, Qiang CASIA NLPR Beijing 100190 Peoples R China Rensselaer Polytech Inst Troy NY 12180 USA

ISBN: (纸本)9780769549897

In this paper, we focus on face clustering in videos. Given the detected faces from real-world videos, we partition all faces into K disjoint clusters. Different from clustering on a collection of facial images, the faces from videos are organized as face tracks and the frame index of each face is also provided. As a result, many pairwise constraints between faces can be easily obtained from the temporal and spatial knowledge of the face tracks. These constraints can be effectively incorporated into a generative clustering model based on the Hidden Markov Random Fields (HMRFs). Within the HMRF model, the pairwise constraints are augmented by label-level and constraint-level local smoothness to guide the clustering process. The parameters for both the unary and the pairwise potential functions are learned by the simulated field algorithm, and the weights of constraints can be easily adjusted. We further introduce an efficient clustering framework specially for face clustering in videos, considering that faces in adjacent frames of the same face track are very similar. The framework is applicable to other clustering algorithms to significantly reduce the computational cost. Experiments on two face data sets from real-world videos demonstrate the significantly improved performance of our algorithm over state-of-the-art algorithms.

关键词： face recognition hidden Markov models object tracking pattern clustering video signal processing

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A²-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation

<i>A</i><SUP>2</SUP>-FPN: Attention Aggregation based Featur...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Hu, Miao Li, Yali Fang, Lu Wang, Shengjin Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (纸本)9781665445092

Learning pyramidal feature representations is crucial for recognizing object instances at different scales. Feature Pyramid Network (FPN) is the classic architecture to build a feature pyramid with high-level semantics throughout. However, intrinsic defects in feature extraction and fusion inhibit FPN from further aggregating more discriminative features. In this work, we propose Attention Aggregation based Feature Pyramid Network (A(2)-FPN), to improve multi-scale feature learning through attention-guided feature aggregation. In feature extraction, it extracts discriminative features by collecting-distributing multi-level global context features, and mitigates the semantic information loss due to drastically reduced channels. In feature fusion, it aggregates complementary information from adjacent features to generate location-wise reassembly kernels for content-aware sampling, and employs channel-wise reweighting to enhance the semantic consistency before element-wise addition. A(2)-FPN shows consistent gains on different instance segmentation frameworks. By replacing FPN with A(2)-FPN in Mask R-CNN, our model boosts the performance by 2.1% and 1.6% mask AP when using ResNet-50 and ResNet-101 as backbone, respectively. Moreover, A(2)-FPN achieves an improvement of 2.0% and 1.4% mask AP when integrated into the strong baselines such as Cascade Mask R-CNN and Hybrid Task Cascade.

关键词： computer vision Fuses Aggregates Semantics computer architecture Feature extraction Data mining

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Relaxed matching kernels for robust image comparison

Relaxed matching kernels for robust image comparison

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ieee conference on computer vision and pattern recognition

作者： Vedaldi, Andrea Soatto, Stefano Univ Calif Los Angeles Los Angeles CA 90095 USA

ISBN: (纸本)9781424422425

The popular bag-of-features representation for object recognition collects signatures of local image patches and discards spatial information. Some have recently attempted to at least partially overcome this limitation, for instance by "spatial pyramids" and "proximity" kernels. We introduce the general formalism of "relaxed matching kernels" (RMKs) that includes such approaches as special cases, allow us to derive useful general properties of these kernels, and to introduce new ones. As an example, we introduce a kernel based on matching graphs of features and one based on matching information-compressed features. We show that all RMKs are competitive and outperform in several cases recently published state-of-the-art results on standard datasets. However, we also show that a proper implementation of a baseline bag-of-features algorithm can be extremely competitive, and outperform the other methods in some cases.

关键词： Object recognition

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Normalized and Geometry-Aware Self-Attention Network for Image Captioning

Normalized and Geometry-Aware Self-Attention Network for Ima...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Guo, Longteng Liu, Jing Zhu, Xinxin Yao, Peng Lu, Shichen Lu, Hanqing Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Univ Sci & Technol Beijing Beijing Peoples R China Wuhan Univ Wuhan Peoples R China

ISBN: (纸本)9781728171685

Self-attention (SA) network has shown profound value in image captioning. In this paper, we improve SA from two aspects to promote the performance of image captioning. First, we propose Normalized Self-Attention (NSA), a reparameterization of SA that brings the benefits of normalization inside SA. While normalization is previously only applied outside SA, we introduce a novel normalization method and demonstrate that it is both possible and beneficial to perform it on the hidden activations inside SA. Second, to compensate for the major limit of Transformer that it fails to model the geometry structure of the input objects, we propose a class of Geometry-aware Self-Attention (GSA) that extends SA to explicitly and efficiently consider the relative geometry relations between the objects in the image. To construct our image captioning model, we combine the two modules and apply it to the vanilla self-attention network. We extensively evaluate our proposals on MS-COCO image captioning dataset and superior results are achieved when comparing to state-of-the-art approaches. Further experiments on three challenging tasks, i.e. video captioning, machine translation, and visual question answering, show the generality of our methods.

关键词： Geometry

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