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Self-training Guided Adversarial Domain Adaptation For Thermal Imagery

Self-training Guided Adversarial Domain Adaptation For Therm...

引用

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

作者： Akkaya, Ibrahim Batuhan Altinel, Fazil Halici, Ugur Aselsan Inc Res Ctr Ankara Turkey Middle East Tech Univ Dept Elect & Elect Engn Ankara Turkey NOROM Neurosci & Neurotechnol Excellency Ctr Ankara Turkey

ISBN: (纸本)9781665448994

Deep models trained on large-scale RGB image datasets have shown tremendous success. It is important to apply such deep models to real-world problems. However, these models suffer from a performance bottleneck under illumination changes. Thermal IR cameras are more robust against such changes, and thus can be very useful for the real-world problems. In order to investigate efficacy of combining feature-rich visible spectrum and thermal image modalities, we propose an unsupervised domain adaptation method which does not require RGB-to-thermal image pairs. We employ large-scale RGB dataset MS-COCO as source domain and thermal dataset FLIR ADAS as target domain to demonstrate results of our method. Although adversarial domain adaptation methods aim to align the distributions of source and target domains, simply aligning the distributions cannot guarantee perfect generalization to the target domain. To this end, we propose a self-training guided adversarial domain adaptation method to promote generalization capabilities of adversarial domain adaptation methods. To perform self-training, pseudo labels are assigned to the samples on the target thermal domain to learn more generalized representations for the target domain. Extensive experimental analyses show that our proposed method achieves better results than the state-of-theart adversarial domain adaptation methods. The code and models are publicly available.(1)

关键词： Adaptation models computer vision conferences Lighting Cameras pattern recognition

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SPECTRE: Visual Speech-Informed Perceptual 3D Facial Expression Reconstruction from Videos

SPECTRE: Visual Speech-Informed Perceptual 3D Facial Express...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Filntisis, Panagiotis P. Retsinas, George Paraperas-Papantoniou, Foivos Katsamanis, Athanasios Roussos, Anastasios Maragos, Petros Athena Research Center Institute of Robotics Maroussi15125 Greece National Technical University of Athens School of Electrical & Computer Engineering Greece Greece University of Exeter College of Engineering Mathematics and Physical Sciences United Kingdom Imperial College London United Kingdom Institute for Language and Speech Processing Athena R.C. Greece

ISBN: (纸本)9798350302493

The recent state of the art on monocular 3D face reconstruction from image data has made some impressive advancements, thanks to the advent of Deep Learning. However, it has mostly focused on input coming from a single RGB image, overlooking the following important factors: a) Nowadays, the vast majority of facial image data of interest do not originate from single images but rather from videos, which contain rich dynamic information. b) Furthermore, these videos typically capture individuals in some form of verbal communication (public talks, teleconferences, audiovisual human-computer interactions, interviews, monologues/dialogues in movies, etc). When existing 3D face reconstruction methods are applied in such videos, the artifacts in the reconstruction of the shape and motion of the mouth area are often severe, since they do not match well with the speech *** overcome the aforementioned limitations, we present the first method for visual speech-informed perceptual reconstruction of 3D mouth expressions. We do this by proposing a "lipreading"loss, which guides the fitting process so that the elicited perception from the 3D reconstructed talking head resembles that of the original video footage. We demonstrate that, interestingly, the lipreading loss is better suited for 3D reconstruction of mouth movements compared to traditional landmark losses, and even direct 3D supervision. Furthermore, the devised method does not rely on any text transcriptions or corresponding audio, rendering it ideal for training in unlabeled datasets. We verify the efficiency of our method through objective evaluations on three large-scale datasets, as well as subjective evaluation with two web-based user studies. Project webpage: https://***/spectre/ © 2023 ieee.

关键词： Human computer interaction

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Multi-Scale Hourglass Hierarchical Fusion Network for Single Image Deraining

Multi-Scale Hourglass Hierarchical Fusion Network for Single...

引用

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

作者： Chen, Xiang Huang, Yufeng Xu, Lei Shenyang Aerosp Univ Coll Elect & Informat Engn Shenyang Liaoning Peoples R China Shenyang Fire Sci & Technol Res Inst MEM Shenyang Liaoning Peoples R China

ISBN: (纸本)9781665448994

Rain streaks bring serious blurring and visual quality degradation, which often vary in size, direction and density. Current CNN-based methods achieve encouraging performance, while are limited to depict rain characteristics and recover image details in the poor visibility environment. To address these issues, we present a Multi-scale Hourglass Hierarchical Fusion Network ((MHF)-F-2-Net) in end-to-end manner, to exactly captures rain streak features with multi-scale extraction, hierarchical distillation and information aggregation. For better extracting the features, a novel Multi-scale Hourglass Extraction Block (MHEB) is proposed to get local and global features across different scales through down- and up-sample process. Besides, a Hierarchical Attentive Distillation Block (HADB) then employs the dual attention feature responses to adaptively recalibrate the hierarchical features and eliminate the redundant ones. Further, we introduce a Residual Projected Feature Fusion (RPFF) strategy to progressively discriminate feature learning and aggregate different features instead of directly concatenating or adding. Extensive experiments on both synthetic and real rainy datasets demonstrate the effectiveness of the designed (MHF)-F-2-Net by comparing with recent state-of-the-art deraining algorithms.

关键词： Degradation Visualization computer vision Rain conferences Computational modeling Aggregates

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Phase Selective Convolution

Phase Selective Convolution

引用

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

作者： Lin, Jamie Menjay Noorzad, Parham Yang, Yang Kwak, Nojun Porikli, Fatih Qualcomm AI Res San Diego CA 92121 USA Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781665448994

This paper introduces Phase Selective Convolution (PSC), an enhanced convolution for more deliberate utilization of activations in convolutional networks. Unlike conventional use of convolutions with activation functions, PSC preserves the full space of activations while supporting desirable model nonlinearity. Similar to several other network operations, e.g., the ReLU operation, at the time of their introduction, PSC may not execute as efficiently on platforms without hardware specialization support. As a first step in addressing the need for optimization, we propose a hardware acceleration scheme to enable the intended efficiency for PSC execution. Moreover, we propose a PSC deployment strategy, with which PSC is applied only to selected layers of the networks, to avoid excessive increase in the total model size. To evaluate the results, we apply PSC as a drop-in replacement for selected convolution layers in several networks without affecting their macro network architectures. In particular, PSC-enhanced ResNets achieve higher accuracies by 1.0-2.0% and 0.7-1.0% on CIFAR-100 and ImageNet, respectively, in Pareto efficiency. PSC-enhanced MobileNets (V2 and V3 Large) and MobileNetV3 (Small) achieve 0.9-1.0% and 1.8% accuracy gains, respectively, on ImageNet at little (0.2-0.7%) total model size increase.

关键词： Measurement Tensors Convolution Estimation Network architecture Search problems pattern recognition

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Towards Accurate Visual and Natural Language-Based Vehicle Retrieval Systems

Towards Accurate Visual and Natural Language-Based Vehicle R...

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

作者： Khorramshahi, Pirazh Rambhatla, Sai Saketh Chellappa, Rama Johns Hopkins Univ Baltimore MD 21218 USA Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781665448994

In this work, we consider two tracks of the 2021 NVIDIA AI City Challenge, the City-Scale Multi-Camera Vehicle Re-identification and Natural language-based Vehicle Retrieval. For the vehicle re-identification task, we employ the state-of-art Excited Vehicle Re-Identification deep representation learning model coupled with best training practices and domain adaptation techniques to obtain robust embeddings. We further refine the re-identification results through a series of post-processing steps to remove camera and vehicle orientation bias that is inherent in the task of re-identification. We also take advantage of multiple observations of a vehicle using track-level information and finally obtain fine-grained retrieval results. For the task of Natural language-based vehicle retrieval we leverage the recently proposed Contrastive Language-Image Pre-training model and propose a simple yet effective text-based vehicle retrieval system. We compare our performance against the top submissions to the challenge and our systems are ranked 8th in the public leaderboard for both tracks.

关键词： Training Visualization computer vision Adaptation models conferences Urban areas Cameras

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City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones

City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad...

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

作者： Liu, Chong Zhang, Yuqi Luo, Hao Tang, Jiasheng Chen, Weihua Xu, Xianzhe Wang, Fan Li, Hao Shen, Yi-Dong Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Alibaba Grp Machine Intelligence Technol Lab Hangzhou Peoples R China

ISBN: (纸本)9781665448994

Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many advanced inferences and predictions. This paper describes our solution to the Track 3 multi-camera vehicle tracking task in 2021 AI City Challenge (AICITY21). This paper proposes a multi-target multi-camera vehicle tracking framework guided by the crossroad zones. The framework includes: (1) Use mature detection and vehicle re-identification models to extract targets and appearance features. (2) Use modified JDE-Tracker (without detection module) to track single-camera vehicles and generate single-camera tracklets. (3) According to the characteristics of the crossroad, the Tracklet Filter Strategy and the Direction Based Temporal Mask are proposed. (4) Propose Sub-clustering in Adjacent Cameras for multi-camera tracklets matching. Through the above techniques, our method obtained an IDF1 score of 0.8095, ranking first on the leaderboard (1). The code will be released later.

关键词： Matched filters computer vision Target tracking conferences Urban areas Feature extraction Cameras

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SBNet: Segmentation-based Network for Natural Language-based Vehicle Search

SBNet: Segmentation-based Network for Natural Language-based...

引用

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

作者： Lee, Sangrok Woo, Taekang Lee, Sang Hun MODULABS Seoul South Korea NAVER Corp Seongnam Si South Korea Kookmin Univ Seoul South Korea

ISBN: (纸本)9781665448994

Natural language-based vehicle retrieval is a task to find a target vehicle within a given image based on a natural language description as a query. This technology can be applied to various areas including police searching for a suspect vehicle. However, it is challenging due to the ambiguity of language descriptions and the difficulty of processing multi-modal data. To tackle this problem, we propose a deep neural network called SBNet that performs natural language-based segmentation for vehicle retrieval. We also propose two task-specific modules to improve performance: a substitution module that helps features from different domains to be embedded in the same space and a future prediction module that learns temporal information. SBnet has been trained using the CityFlow-NL dataset that contains 2,498 tracks of vehicles with three unique natural language descriptions each and tested 530 unique vehicle tracks and their corresponding query sets. SBNet achieved a significant improvement over the baseline in the natural language-based vehicle tracking track in the AI City Challenge 2021. Source Code: https://***/lsrock1/nlp_search

关键词： Deep learning Image segmentation computer vision Law enforcement conferences Urban areas Natural languages

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Long-term Head Pose Forecasting Conditioned on the Gaze-guiding Prior

Long-term Head Pose Forecasting Conditioned on the Gaze-guid...

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

作者： Mo, Shentong Xin, Miao Carnegie Mellon Univ Pittsburgh PA 15213 USA Chinese Acad Sci Inst Automat CASIA Beijing Peoples R China

ISBN: (纸本)9781665448994

Forecasting head pose future states is a novel task in computer vision. Since future may have many possibilities, and the logical results are much more important than the impractical ones, the forecasting results for most of the scenarios should be not only diverse but also logically realistic. These requirements pose a real challenge to the current methods, which motivates us to seek for better head pose representation and methods to restrict the forecasting reasonably. In this paper, we adopt a spatial-temporal graph to model the interdependencies between the distribution of landmarks and head pose angles. Furthermore, we propose the conditional spatial-temporal variational graph autoencoder (CST-VGAE), a deep conditional generative model for learning restricted one-to-many mappings conditioned on the spatial-temporal graph input. Specifically, we improve the proposed CST-VGAE for the long-term head pose forecasting task in terms of several aspects. First, we introduce a gaze-guiding prior based on the physiology. Then we apply a temporal self-attention and self-supervised learning mechanism to learn the long-range dependencies on the gaze prior. To better model head poses structurally, we introduce a Gaussian Mixture Model (GMM), instead of a fixed Gaussian in the encoded latent space. Experiments demonstrate the effectiveness of the proposed method for the long-term head pose forecasting task. We achieve superior forecasting performance on the benchmark datasets compared to the existing methods.

关键词： Learning systems computer vision conferences Predictive models Benchmark testing Physiology pattern recognition

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Cross Modality Knowledge Distillation for Multi-modal Aerial View Object Classification

Cross Modality Knowledge Distillation for Multi-modal Aerial...

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

作者： Yang, Lehan Xu, Kele Univ Sydney Camperdown NSW Australia Key Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9781665448994

In the case of bad weather or low lighting conditions, a single sensor may not be able to capture enough information for object identification. Compared with the traditional optical image, synthetic aperture radar (SAR) imaging has greater advantages, such as the ability to penetrate through fog and smoke. However, SAR images are of low resolution and contaminated by high-level speckle noise. As a result, it is of great difficulty to extract powerful and robust features from the SAR images. In this paper, we explored whether multiple imaging modalities can improve the object detection performance. Here, we propose a Cross Modality Knowledge Distillation (CMKD) paradigm, and explore two different network structures named CMKD-s and CMKD-m for the object classification task. Specifically, CMKD-s transfers the information captured by the two sensors using the online knowledge distillation, which can achieve cross-modal knowledge sharing and enhance the robustness of the aerial view object classification model. Moreover, leveraging the semi-supervised enhanced training, we proposed a novel method named CMKD-m, which strengthens the model for mutual knowledge transfer. Through quantitative comparison, we found that CMKD-s and CMKD-m outperform the method without knowledge transfer, on the NTIRE2021 SAR-EO challenge dataset.

关键词： Training Speckle Feature extraction Radar polarimetry Data models Robustness pattern recognition

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What Affects Learned Equivariance in Deep Image recognition Models?

What Affects Learned Equivariance in Deep Image Recognition ...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Robert-Jan Bruintjes Tomasz Motyka Jan van Gemert Computer Vision Lab Delft University of Technology Synerise

Equivariance w.r.t. geometric transformations in neural networks improves data efficiency, parameter efficiency and robustness to out-of-domain perspective shifts. When equivariance is not designed into a neural network, the network can still learn equivariant functions from the data. We quantify this learned equivariance, by proposing an improved measure for equivariance. We find evidence for a correlation between learned translation equivariance and validation accuracy on ImageNet. We therefore investigate what can increase the learned equivariance in neural networks, and find that data augmentation, reduced model capacity and inductive bias in the form of convolutions induce higher learned equivariance in neural networks.

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