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Multi-Target Domain Adaptation with Collaborative Consistency Learning

Multi-Target Domain Adaptation with Collaborative Consistenc...

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

作者： Isobe, Takashi Jia, Xu Chen, Shuaijun He, Jianzhong Shi, Yongjie Liu, Jianzhuang Lu, Huchuan Wang, Shengjin Tsinghua Univ Dept Elect Engn Beijing Peoples R China Dalian Univ Technol Dalian Peoples R China Huawei Technol Noahs Ark Lab Shenzhen Peoples R China Peking Univ Key Lab Machine Percept MOE Beijing Peoples R China

ISBN: (纸本)9781665445092

Recently unsupervised domain adaptation for the semantic segmentation task has become more and more popular due to high-cost of pixel-level annotation on real-world images. However, most domain adaptation methods are only restricted to single-source-single-target pair, and can not be directly extended to multiple target domains. In this work, we propose a collaborative learning framework to achieve unsupervised multi-target domain adaptation. An unsupervised domain adaptation expert model is first trained for each source-target pair and is further encouraged to collaborate with each other through a bridge built between different target domains. These expert models are further improved by adding the regularization of making the consistent pixel-wise prediction for each sample with the same structured context. To obtain a single model that works across multiple target domains, we propose to simultaneously learn a student model which is trained to not only imitate the output of each expert on the corresponding target domain, but also to pull different expert close to each other with regularization on their weights. Extensive experiments demonstrate that the proposed method can effectively exploit rich structured information contained in both labeled source domain and multiple unlabeled target domains. Not only does it perform well across multiple target domains but also performs favorably against state-of-the-art unsupervised domain adaptation methods specially trained on a single source-target pair. Code is available at https://***/junpan19/MTDA.

关键词： Adaptation models Image segmentation computer vision Computational modeling Semantics Collaboration Predictive models

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Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection

Beyond Max-Margin: Class Margin Equilibrium for Few-shot Obj...

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

作者： Li, Bohao Yang, Boyu Liu, Chang Liu, Feng Ji, Rongrong Ye, Qixiang Univ Chinese Acad Sci EECE PriSDL Beijing 100049 Peoples R China Xiamen Univ MAC Dept Artificial Intelligence Sch Informat Xiamen 361005 Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen 361005 Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Few-shot object detection has made substantial progress by representing novel class objects using the feature representation learned upon a set of base class objects. However, an implicit contradiction between novel class classification and representation is unfortunately ignored. On the one hand, to achieve accurate novel class classification, the distributions of either two base classes must be far away from each other (max-margin). On the other hand, to precisely represent novel classes, the distributions of base classes should be close to each other to reduce the intra-class distance of novel classes (min-margin). In this paper, we propose a class margin equilibrium (CME) approach, with the aim to optimize both feature space partition and novel class reconstruction in a systematic way. CME first converts the few-shot detection problem to the few-shot classification problem by using a fully connected layer to decouple localization features. CME then reserves adequate margin space for novel classes by introducing simple-yet-effective class margin loss during feature learning. Finally, CME pursues margin equilibrium by disturbing the features of novel class instances in an adversarial min-max fashion. Experiments on Pascal VOC and MS-COCO datasets show that CME significantly improves upon two baseline detectors (up to 3 similar to 5% in average), achieving state-of-the-art performance.

关键词： Training Location awareness computer vision Systematics Codes Object detection Detectors

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Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation

Semi-supervised Domain Adaptation based on Dual-level Domain...

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

作者： Chen, Shuaijun Jia, Xu He, Jianzhong Shi, Yongjie Liu, Jianzhuang Huawei Technol Noahs Ark Lab Shenzhen Guangdong Peoples R China Dalian Univ Technol Dalian Liaoning Peoples R China Huawei Cloud Data Storage & Intelligent Vis Tech Res Dept Shenzhen Guangdong Peoples R China Peking Univ Key Lab Machine Percept Beijing Peoples R China Noahs Ark Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781665445092

Data-driven based approaches, in spite of great success in many tasks, have poor generalization when applied to unseen image domains, and require expensive cost of annotation especially for dense pixel prediction tasks such as semantic segmentation. Recently, both unsupervised domain adaptation (UDA) from large amounts of synthetic data and semi-supervised learning (SSL) with small set of labeled data have been studied to alleviate this issue. However, there is still a large gap on performance compared to their supervised counterparts. We focus on a more practical setting of semi-supervised domain adaptation (SSDA) where both a small set of labeled target data and large amounts of labeled source data are available. To address the task of SSDA, a novel framework based on dual-level domain mixing is proposed. The proposed framework consists of three stages. First, two kinds of data mixing methods are proposed to reduce domain gap in both region-level and sample-level respectively. We can obtain two complementary domain-mixed teachers based on dual-level mixed data from holistic and partial views respectively. Then, a student model is learned by distilling knowledge from these two teachers. Finally, pseudo labels of unlabeled data are generated in a self-training manner for another few rounds of teachers training. Extensive experimental results have demonstrated the effectiveness of our proposed framework on synthetic-to-real semantic segmentation benchmarks.

关键词： Training Adaptation models Image segmentation computer vision Costs Semantics Benchmark testing

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IDGI: A Framework to Eliminate Explanation Noise from Integrated Gradients

IDGI: A Framework to Eliminate Explanation Noise from Integr...

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conference on computer vision and pattern recognition (cvpr)

作者： Ruo Yang Binghui Wang Mustafa Bilgic Department of Computer Science Illinois Institute of Technology Chicago IL USA

Integrated Gradients (IG) as well as its variants are well-known techniques for interpreting the decisions of deep neural networks. While IG-based approaches attain state-of-the-art performance, they often integrate noise into their explanation saliency maps, which reduce their interpretability. To minimize the noise, we examine the source of the noise analytically and propose a new approach to reduce the explanation noise based on our analytical findings. We propose the Important Direction Gradient Integration (IDGI) framework, which can be easily incorporated into any IG-based method that uses the Reimann Integration for integrated gradient computation. Extensive experiments with three IG-based methods show that IDGI improves them drastically on numerous interpretability metrics. The source code for IDGI is available at https://***/yangruo1226/IDGI.

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VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization

VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aw...

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

作者： Choi, Seunghwan Park, Sunghyun Lee, Minsoo Choo, Jaegul Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

The task of image-based virtual try-on aims to transfer a target clothing item onto the corresponding region of a person, which is commonly tackled by fitting the item to the desired body part and fusing the warped item with the person. While an increasing number of studies have been conducted, the resolution of synthesized images is still limited to low (e.g., 256x192), which acts as the critical limitation against satisfying online consumers. We argue that the limitation stems from several challenges: as the resolution increases, the artifacts in the misaligned areas between the warped clothes and the desired clothing regions become noticeable in the final results;the architectures used in existing methods have low performance in generating high-quality body parts and maintaining the texture sharpness of the clothes. To address the challenges, we propose a novel virtual try-on method called VITON-HD that successfully synthesizes 1024x768 virtual try-on images. Specifically, we first prepare the segmentation map to guide our virtual try-on synthesis, and then roughly fit the target clothing item to a given person's body. Next, we propose ALIgnment-Aware Segment (ALIAS) normalization and ALIAS generator to handle the misaligned areas and preserve the details of 1024x768 inputs. Through rigorous comparison with existing methods, we demonstrate that VITON-HD highly surpasses the baselines in terms of synthesized image quality both qualitatively and quantitatively.

关键词： Image quality Image segmentation computer vision Image resolution Clothing Semantics Fitting

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Monocular Reconstruction of Neural Face Reflectance Fields

Monocular Reconstruction of Neural Face Reflectance Fields

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

作者： Mallikarjun, B. R. Tewari, Ayush Oh, Tae-Hyun Weyrich, Tim Bickel, Bernd Seidel, Hans-Peter Pfister, Hanspeter Matusik, Wojciech Elgharib, Mohamed Theobalt, Christian Max Planck Inst Informat Saarland Informat Campus Saarbrucken Germany POSTECH Pohang South Korea UCL London England IST Austria Klosterneuburg Austria Harvard Univ Cambridge MA 02138 USA MIT CSAIL Cambridge MA USA

ISBN: (纸本)9781665445092

The reflectance field of a face describes the reflectance properties responsible for complex lighting effects including diffuse, specular, inter-reflection and self shadowing. Most existing methods for estimating the face reflectance from a monocular image assume faces to be diffuse with very few approaches adding a specular component. This still leaves out important perceptual aspects of reflectance such as higher-order global illumination effects and self-shadowing. We present a new neural representation for face reflectance where we can estimate all components of the reflectance responsible for the final appearance from a monocular image. Instead of modeling each component of the reflectance separately using parametric models, our neural representation allows us to generate a basis set of faces in a geometric deformation-invariant space, parameterized by the input light direction, viewpoint and face geometry. We learn to reconstruct this reflectance field of a face just from a monocular image, which can be used to render the face from any viewpoint in any light condition. Our method is trained on a light-stage dataset, which captures 300 people illuminated with 150 light conditions from 8 viewpoints. We show that our method outperforms existing monocular reflectance reconstruction methods due to better capturing of physical effects, such as sub-surface scattering, specularities, self-shadows and other higher-order effects.

关键词： Reflectivity Geometry Deformable models Face recognition Lighting Scattering Reconstruction algorithms

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A Simple Baseline for Fast and Accurate Depth Estimation on Mobile Devices

A Simple Baseline for Fast and Accurate Depth Estimation on ...

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

作者： Zhang, Ziyu Wang, Yicheng Huang, Zilong Luo, Guozhong Yu, Gang Fu, Bin Tencent GY Lab Shenzhen Peoples R China

ISBN: (纸本)9781665448994

In this paper, we propose a simple but effective encoder-decoder based network for fast and accurate depth estimation on mobile devices. Unlike other depth estimation methods using heavy context modeling modules, the encoder with a fast downsampling strategy is employed to obtain sufficient receptive field and contexts at a faster rate. To obtain dense prediction, a light decoder is adopted to recover back to the original resolution. Additionally, to improve the representative ability of the light network, we introduce a teacher-student strategy. It relies on a distillation process ensuring that the student (the proposed light network) learns from the teacher. The proposed method achieves a good trade-off between latency and accuracy. We evaluated the proposed algorithm on the MAI 2021 Monocular Depth Estimation Challenge and achieved a score of 129.41, ranked the first place, which wins the second by a large margin (129.41 v.s. 14.51). More specifically, the proposed method achieves a si-RMSE score of 0.28 with 97 ms on the Raspberry Pi 4.

关键词： Performance evaluation computer vision conferences Estimation Feature extraction Mobile handsets pattern recognition

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NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior

NoPe-NeRF: Optimising Neural Radiance Field with No Pose Pri...

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conference on computer vision and pattern recognition (cvpr)

作者： Wenjing Bian Zirui Wang Kejie Li Jia-Wang Bian Victor Adrian Prisacariu Active Vision Lab University of Oxford

Training a Neural Radiance Field (NeRF) without precomputed camera poses is challenging. Recent advances in this direction demonstrate the possibility of jointly optimising a NeRF and camera poses in forward-facing scenes. However, these methods still face difficulties during dramatic camera movement. We tackle this challenging problem by incorporating undistorted monocular depth priors. These priors are generated by correcting scale and shift parameters during training, with which we are then able to constrain the relative poses between consecutive frames. This constraint is achieved using our proposed novel loss functions. Experiments on real-world indoor and outdoor scenes show that our method can handle challenging camera trajectories and outperforms existing methods in terms of novel view rendering quality and pose estimation accuracy. Our project page is https://***.

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Multi-attentional Deepfake Detection

Multi-attentional Deepfake Detection

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

作者： Zhao, Hanqing Wei, Tianyi Zhou, Wenbo Zhang, Weiming Chen, Dongdong Yu, Nenghai Univ Sci & Technol China Hefei Peoples R China Microsoft Cloud AI Redmond WA USA

ISBN: (纸本)9781665445092

Face forgery by deepfake is widely spread over the internet and has raised severe societal concerns. Recently, how to detect such forgery contents has become a hot research topic and many deepfake detection methods have been proposed. Most of them model deepfake detection as a vanilla binary classification problem, i.e, first use a backbone network to extract a global feature and then feed it into a binary classifier (real/fake). But since the difference between the real and fake images in this task is often subtle and local, we argue this vanilla solution is not optimal. In this paper, we instead formulate deepfake detection as a fine-grained classification problem and propose a new multi-attentional deepfake detection network. Specifically, it consists of three key components: 1) multiple spatial attention heads to make the network attend to different local parts;2) textural feature enhancement block to zoom in the subtle artifacts in shallow features;3) aggregate the low-level textural feature and high-level semantic features guided by the attention maps. Moreover, to address the learning difficulty of this network, we further introduce a new regional independence loss and an attention guided data augmentation strategy. Through extensive experiments on different datasets, we demonstrate the superiority of our method over the vanilla binary classifier counterparts, and achieve state-of-the-art performance.

关键词： Measurement Semantics Feature extraction Forgery pattern recognition Feeds Task analysis

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Occlusion Guided Scene Flow Estimation on 3D Point Clouds

Occlusion Guided Scene Flow Estimation on 3D Point Clouds

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

作者： Ouyang, Bojun Raviv, Dan Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9781665448994

3D scene flow estimation is a vital tool in perceiving our environment given depth or range sensors. Unlike optical flow, the data is usually sparse and in most cases partially occluded in between two temporal samplings. Here we propose a new scene flow architecture called OGSF-Net which tightly couples the learning for both flow and occlusions between frames. Their coupled symbiosis results in a more accurate prediction of flow in space. Unlike a traditional multi-action network, our unified approach is fused throughout the network, boosting performances for both occlusion detection and flow estimation. Our architecture is the first to gauge the occlusion in 3D scene flow estimation on point clouds. In key datasets such as Flyingthings3D and KITTI, we achieve the state-of-the-art results.(1,2)

关键词： Symbiosis Measurement Deep learning Three-dimensional displays Estimation computer architecture Tools

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