pwc-archive/papers-with-abstracts huggingface dataset:
RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment
Authors: Maryam Rahnemoonfar, Tashnim Chowdhury, Robin Murphy
Date: 2022-02-24 00:00:00
Tasks: Scene Understanding, Segmentation, Semantic Segmentation

Recent advancements in computer vision and deep learning techniques have facilitated notable progress in scene understanding, thereby assisting rescue teams in achieving precise damage assessment. In this paper, we present RescueNet, a meticulously curated high-resolution post-disaster dataset that includes detailed classification and semantic segmentation annotations. This dataset aims to facilitate comprehensive scene understanding in the aftermath of natural disasters. RescueNet comprises post-disaster images collected after Hurricane Michael, obtained using Unmanned Aerial Vehicles (UAVs) from multiple impacted regions. The uniqueness of RescueNet lies in its provision of high-resolution post-disaster imagery, accompanied by comprehensive annotations for each image. Unlike existing datasets that offer annotations limited to specific scene elements such as buildings, RescueNet provides pixel-level annotations for all classes, including buildings, roads, pools, trees, and more. Furthermore, we evaluate the utility of the dataset by implementing state-of-the-art segmentation models on RescueNet, demonstrating its value in enhancing existing methodologies for natural disaster damage assessment.
StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Translation
Authors: Peter Schaldenbrand, Zhixuan Liu, Jean Oh
Date: 2022-02-24 00:00:00
Tasks: Style Transfer, Translation

Generating images that fit a given text description using machine learning has improved greatly with the release of technologies such as the CLIP image-text encoder model; however, current methods lack artistic control of the style of image to be generated. We present an approach for generating styled drawings for a given text description where a user can specify a desired drawing style using a sample image. Inspired by a theory in art that style and content are generally inseparable during the creative process, we propose a coupled approach, known here as StyleCLIPDraw, whereby the drawing is generated by optimizing for style and content simultaneously throughout the process as opposed to applying style transfer after creating content in a sequence. Based on human evaluation, the styles of images generated by StyleCLIPDraw are strongly preferred to those by the sequential approach. Although the quality of content generation degrades for certain styles, overall considering both content \textit{and} style, StyleCLIPDraw is found far more preferred, indicating the importance of style, look, and feel of machine generated images to people as well as indicating that style is coupled in the drawing process itself. Our code (https://github.com/pschaldenbrand/StyleCLIPDraw), a demonstration (https://replicate.com/pschaldenbrand/style-clip-draw), and style evaluation data (https://www.kaggle.com/pittsburghskeet/drawings-with-style-evaluation-styleclipdraw) are publicly available.
Instantaneous Physiological Estimation using Video Transformers
Authors: Ambareesh Revanur, Ananyananda Dasari, Conrad S. Tucker, Laszlo A. Jeni
Date: 2022-02-24 00:00:00
Tasks: Heart rate estimation

Video-based physiological signal estimation has been limited primarily to predicting episodic scores in windowed intervals. While these intermittent values are useful, they provide an incomplete picture of patients' physiological status and may lead to late detection of critical conditions. We propose a video Transformer for estimating instantaneous heart rate and respiration rate from face videos. Physiological signals are typically confounded by alignment errors in space and time. To overcome this, we formulated the loss in the frequency domain. We evaluated the method on the large scale Vision-for-Vitals (V4V) benchmark. It outperformed both shallow and deep learning based methods for instantaneous respiration rate estimation. In the case of heart-rate estimation, it achieved an instantaneous-MAE of 13.0 beats-per-minute.
On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression
Authors: Xuanlong Yu, Gianni Franchi, Emanuel Aldea
Date: 2022-02-24 00:00:00
Tasks: 3D Reconstruction, Autonomous Driving, Depth Estimation, Monocular Depth Estimation, regression, Uncertainty Quantification

Monocular depth is important in many tasks, such as 3D reconstruction and autonomous driving. Deep learning based models achieve state-of-the-art performance in this field. A set of novel approaches for estimating monocular depth consists of transforming the regression task into a classification one. However, there is a lack of detailed descriptions and comparisons for Classification Approaches for Regression (CAR) in the community and no in-depth exploration of their potential for uncertainty estimation. To this end, this paper will introduce a taxonomy and summary of CAR approaches, a new uncertainty estimation solution for CAR, and a set of experiments on depth accuracy and uncertainty quantification for CAR-based models on KITTI dataset. The experiments reflect the differences in the portability of various CAR methods on two backbones. Meanwhile, the newly proposed method for uncertainty estimation can outperform the ensembling method with only one forward propagation.
Highly-Efficient Binary Neural Networks for Visual Place Recognition
Authors: Bruno Ferrarini, Michael Milford, Klaus D. McDonald-Maier, Shoaib Ehsan
Date: 2022-02-24 00:00:00
Tasks: Autonomous Navigation, Binarization, Visual Place Recognition

VPR is a fundamental task for autonomous navigation as it enables a robot to localize itself in the workspace when a known location is detected. Although accuracy is an essential requirement for a VPR technique, computational and energy efficiency are not less important for real-world applications. CNN-based techniques archive state-of-the-art VPR performance but are computationally intensive and energy demanding. Binary neural networks (BNN) have been recently proposed to address VPR efficiently. Although a typical BNN is an order of magnitude more efficient than a CNN, its processing time and energy usage can be further improved. In a typical BNN, the first convolution is not completely binarized for the sake of accuracy. Consequently, the first layer is the slowest network stage, requiring a large share of the entire computational effort. This paper presents a class of BNNs for VPR that combines depthwise separable factorization and binarization to replace the first convolutional layer to improve computational and energy efficiency. Our best model achieves state-of-the-art VPR performance while spending considerably less time and energy to process an image than a BNN using a non-binary convolution as a first stage.
Learning Transferable Reward for Query Object Localization with Policy Adaptation
Authors: Tingfeng Li, Shaobo Han, Martin Renqiang Min, Dimitris N. Metaxas
Date: 2022-02-24 00:00:00
Proceeding: ICLR 2022 4
Tasks: Metric Learning, Object Localization, reinforcement-learning, Reinforcement Learning (RL)

We propose a reinforcement learning based approach to query object localization, for which an agent is trained to localize objects of interest specified by a small exemplary set. We learn a transferable reward signal formulated using the exemplary set by ordinal metric learning. Our proposed method enables test-time policy adaptation to new environments where the reward signals are not readily available, and outperforms fine-tuning approaches that are limited to annotated images. In addition, the transferable reward allows repurposing the trained agent from one specific class to another class. Experiments on corrupted MNIST, CU-Birds, and COCO datasets demonstrate the effectiveness of our approach.
Fourier-Based Augmentations for Improved Robustness and Uncertainty Calibration
Authors: Ryan Soklaski, Michael Yee, Theodoros Tsiligkaridis
Date: 2022-02-24 00:00:00
Tasks: Data Augmentation, Image Augmentation

Diverse data augmentation strategies are a natural approach to improving robustness in computer vision models against unforeseen shifts in data distribution. However, the ability to tailor such strategies to inoculate a model against specific classes of corruptions or attacks -- without incurring substantial losses in robustness against other classes of corruptions -- remains elusive. In this work, we successfully harden a model against Fourier-based attacks, while producing superior-to-AugMix accuracy and calibration results on both the CIFAR-10-C and CIFAR-100-C datasets; classification error is reduced by over ten percentage points for some high-severity noise and digital-type corruptions. We achieve this by incorporating Fourier-basis perturbations in the AugMix image-augmentation framework. Thus we demonstrate that the AugMix framework can be tailored to effectively target particular distribution shifts, while boosting overall model robustness.
Optimal channel selection with discrete QCQP
Authors: Yeonwoo Jeong, Deokjae Lee, Gaon An, Changyong Son, Hyun Oh Song
Date: 2022-02-24 00:00:00
Tasks: channel selection

Reducing the high computational cost of large convolutional neural networks is crucial when deploying the networks to resource-constrained environments. We first show the greedy approach of recent channel pruning methods ignores the inherent quadratic coupling between channels in the neighboring layers and cannot safely remove inactive weights during the pruning procedure. Furthermore, due to these inactive weights, the greedy methods cannot guarantee to satisfy the given resource constraints and deviate with the true objective. In this regard, we propose a novel channel selection method that optimally selects channels via discrete QCQP, which provably prevents any inactive weights and guarantees to meet the resource constraints tightly in terms of FLOPs, memory usage, and network size. We also propose a quadratic model that accurately estimates the actual inference time of the pruned network, which allows us to adopt inference time as a resource constraint option. Furthermore, we generalize our method to extend the selection granularity beyond channels and handle non-sequential connections. Our experiments on CIFAR-10 and ImageNet show our proposed pruning method outperforms other fixed-importance channel pruning methods on various network architectures.
Analyzing Human Observer Ability in Morphing Attack Detection -- Where Do We Stand?
Authors: Sankini Rancha Godage, Frøy Løvåsdal, Sushma Venkatesh, Kiran Raja, Raghavendra Ramachandra, Christoph Busch
Date: 2022-02-24 00:00:00
Tasks: MORPH

Few studies have focused on examining how people recognize morphing attacks, even as several publications have examined the susceptibility of automated FRS and offered morphing attack detection (MAD) approaches. MAD approaches base their decisions either on a single image with no reference to compare against (S-MAD) or using a reference image (D-MAD). One prevalent misconception is that an examiner's or observer's capacity for facial morph detection depends on their subject expertise, experience, and familiarity with the issue and that no works have reported the specific results of observers who regularly verify identity (ID) documents for their jobs. As human observers are involved in checking the ID documents having facial images, a lapse in their competence can have significant societal challenges. To assess the observers' proficiency, this work first builds a new benchmark database of realistic morphing attacks from 48 different subjects, resulting in 400 morphed images. We also capture images from Automated Border Control (ABC) gates to mimic the realistic border-crossing scenarios in the D-MAD setting with 400 probe images to study the ability of human observers to detect morphed images. A new dataset of 180 morphing images is also produced to research human capacity in the S-MAD environment. In addition to creating a new evaluation platform to conduct S-MAD and D-MAD analysis, the study employs 469 observers for D-MAD and 410 observers for S-MAD who are primarily governmental employees from more than 40 countries, along with 103 subjects who are not examiners. The analysis offers intriguing insights and highlights the lack of expertise and failure to recognize a sizable number of morphing attacks by experts. The results of this study are intended to aid in the development of training programs to prevent security failures while determining whether an image is bona fide or altered.
Efficient Video Segmentation Models with Per-frame Inference
Authors: Yifan Liu, Chunhua Shen, Changqian Yu, Jingdong Wang
Date: 2022-02-24 00:00:00
Tasks: Image Matting, Instance Segmentation, Knowledge Distillation, Optical Flow Estimation, Semantic Segmentation, Video Instance Segmentation, Video Segmentation, Video Semantic Segmentation

Most existing real-time deep models trained with each frame independently may produce inconsistent results across the temporal axis when tested on a video sequence. A few methods take the correlations in the video sequence into account,e.g., by propagating the results to the neighboring frames using optical flow or extracting frame representations using multi-frame information, which may lead to inaccurate results or unbalanced latency. In this work, we focus on improving the temporal consistency without introducing computation overhead in inference. To this end, we perform inference at each frame. Temporal consistency is achieved by learning from video frames with extra constraints during the training phase. introduced for inference. We propose several techniques to learn from the video sequence, including a temporal consistency loss and online/offline knowledge distillation methods. On the task of semantic video segmentation, weighing among accuracy, temporal smoothness, and efficiency, our proposed method outperforms keyframe-based methods and a few baseline methods that are trained with each frame independently, on datasets including Cityscapes, Camvid, and 300VW-Mask. We further apply our training method to video instance segmentation on YouTubeVISand develop an application of portrait matting in video sequences, by segmenting temporally consistent instance-level trimaps across frames. Experiments show superior qualitative and quantitative results. Code is available at: https://git.io/vidseg.
Understanding Adversarial Robustness from Feature Maps of Convolutional Layers
Authors: Cong Xu, Wei zhang, Jun Wang, Min Yang
Date: 2022-02-25 00:00:00
Tasks: Adversarial Robustness

The adversarial robustness of a neural network mainly relies on two factors: model capacity and anti-perturbation ability. In this paper, we study the anti-perturbation ability of the network from the feature maps of convolutional layers. Our theoretical analysis discovers that larger convolutional feature maps before average pooling can contribute to better resistance to perturbations, but the conclusion is not true for max pooling. It brings new inspiration to the design of robust neural networks and urges us to apply these findings to improve existing architectures. The proposed modifications are very simple and only require upsampling the inputs or slightly modifying the stride configurations of downsampling operators. We verify our approaches on several benchmark neural network architectures, including AlexNet, VGG, RestNet18, and PreActResNet18. Non-trivial improvements in terms of both natural accuracy and adversarial robustness can be achieved under various attack and defense mechanisms. The code is available at \url{https://github.com/MTandHJ/rcm}.
Learn From the Past: Experience Ensemble Knowledge Distillation
Authors: Chaofei Wang, Shaowei Zhang, Shiji Song, Gao Huang
Date: 2022-02-25 00:00:00
Tasks: Knowledge Distillation, Transfer Learning

Traditional knowledge distillation transfers "dark knowledge" of a pre-trained teacher network to a student network, and ignores the knowledge in the training process of the teacher, which we call teacher's experience. However, in realistic educational scenarios, learning experience is often more important than learning results. In this work, we propose a novel knowledge distillation method by integrating the teacher's experience for knowledge transfer, named experience ensemble knowledge distillation (EEKD). We save a moderate number of intermediate models from the training process of the teacher model uniformly, and then integrate the knowledge of these intermediate models by ensemble technique. A self-attention module is used to adaptively assign weights to different intermediate models in the process of knowledge transfer. Three principles of constructing EEKD on the quality, weights and number of intermediate models are explored. A surprising conclusion is found that strong ensemble teachers do not necessarily produce strong students. The experimental results on CIFAR-100 and ImageNet show that EEKD outperforms the mainstream knowledge distillation methods and achieves the state-of-the-art. In particular, EEKD even surpasses the standard ensemble distillation on the premise of saving training cost.
Diffeomorphic Image Registration with Neural Velocity Field
Authors: Kun Han, Shanlin Sun, Xiangyi Yan, Chenyu You, Hao Tang, Junayed Naushad, Haoyu Ma, Deying Kong, Xiaohui Xie
Date: 2022-02-25 00:00:00
Tasks: Image Registration, Medical Image Analysis

Diffeomorphic image registration, offering smooth transformation and topology preservation, is required in many medical image analysis tasks.Traditional methods impose certain modeling constraints on the space of admissible transformations and use optimization to find the optimal transformation between two images. Specifying the right space of admissible transformations is challenging: the registration quality can be poor if the space is too restrictive, while the optimization can be hard to solve if the space is too general. Recent learning-based methods, utilizing deep neural networks to learn the transformation directly, achieve fast inference, but face challenges in accuracy due to the difficulties in capturing the small local deformations and generalization ability. Here we propose a new optimization-based method named DNVF (Diffeomorphic Image Registration with Neural Velocity Field) which utilizes deep neural network to model the space of admissible transformations. A multilayer perceptron (MLP) with sinusoidal activation function is used to represent the continuous velocity field and assigns a velocity vector to every point in space, providing the flexibility of modeling complex deformations as well as the convenience of optimization. Moreover, we propose a cascaded image registration framework (Cas-DNVF) by combining the benefits of both optimization and learning based methods, where a fully convolutional neural network (FCN) is trained to predict the initial deformation, followed by DNVF for further refinement. Experiments on two large-scale 3D MR brain scan datasets demonstrate that our proposed methods significantly outperform the state-of-the-art registration methods.
RRL:Regional Rotation Layer in Convolutional Neural Networks
Authors: Zongbo Hao, Tao Zhang, Mingwang Chen, Kaixu Zhou
Date: 2022-02-25 00:00:00
Tasks: Astronomy, image-classification, Image Classification, object-detection, Object Detection

Convolutional Neural Networks (CNNs) perform very well in image classification and object detection in recent years, but even the most advanced models have limited rotation invariance. Known solutions include the enhancement of training data and the increase of rotation invariance by globally merging the rotation equivariant features. These methods either increase the workload of training or increase the number of model parameters. To address this problem, this paper proposes a module that can be inserted into the existing networks, and directly incorporates the rotation invariance into the feature extraction layers of the CNNs. This module does not have learnable parameters and will not increase the complexity of the model. At the same time, only by training the upright data, it can perform well on the rotated testing set. These advantages will be suitable for fields such as biomedicine and astronomy where it is difficult to obtain upright samples or the target has no directionality. Evaluate our module with LeNet-5, ResNet-18 and tiny-yolov3, we get impressive results.
TeachAugment: Data Augmentation Optimization Using Teacher Knowledge
Authors: Teppei Suzuki
Date: 2022-02-25 00:00:00
Proceeding: CVPR 2022 1
Tasks: Data Augmentation, image-classification, Image Classification, Representation Learning, Semantic Segmentation

Optimization of image transformation functions for the purpose of data augmentation has been intensively studied. In particular, adversarial data augmentation strategies, which search augmentation maximizing task loss, show significant improvement in the model generalization for many tasks. However, the existing methods require careful parameter tuning to avoid excessively strong deformations that take away image features critical for acquiring generalization. In this paper, we propose a data augmentation optimization method based on the adversarial strategy called TeachAugment, which can produce informative transformed images to the model without requiring careful tuning by leveraging a teacher model. Specifically, the augmentation is searched so that augmented images are adversarial for the target model and recognizable for the teacher model. We also propose data augmentation using neural networks, which simplifies the search space design and allows for updating of the data augmentation using the gradient method. We show that TeachAugment outperforms existing methods in experiments of image classification, semantic segmentation, and unsupervised representation learning tasks.
A Novel Hand Gesture Detection and Recognition system based on ensemble-based Convolutional Neural Network
Authors: Abir Sen, Tapas Kumar Mishra, Ratnakar Dash
Date: 2022-02-25 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

Nowadays, hand gesture recognition has become an alternative for human-machine interaction. It has covered a large area of applications like 3D game technology, sign language interpreting, VR (virtual reality) environment, and robotics. But detection of the hand portion has become a challenging task in computer vision and pattern recognition communities. Deep learning algorithm like convolutional neural network (CNN) architecture has become a very popular choice for classification tasks, but CNN architectures suffer from some problems like high variance during prediction, overfitting problem and also prediction errors. To overcome these problems, an ensemble of CNN-based approaches is presented in this paper. Firstly, the gesture portion is detected by using the background separation method based on binary thresholding. After that, the contour portion is extracted, and the hand region is segmented. Then, the images have been resized and fed into three individual CNN models to train them in parallel. In the last part, the output scores of CNN models are averaged to construct an optimal ensemble model for the final prediction. Two publicly available datasets (labeled as Dataset-1 and Dataset-2) containing infrared images and one self-constructed dataset have been used to validate the proposed system. Experimental results are compared with the existing state-of-the-art approaches, and it is observed that our proposed ensemble model outperforms other existing proposed methods.