pwc-archive/papers-with-abstracts huggingface dataset:
Burst Imaging for Light-Constrained Structure-From-Motion
Authors: Ahalya Ravendran, Mitch Bryson, Donald G. Dansereau
Date: 2021-08-23 00:00:00
Tasks: 3D Reconstruction, Image Registration

Images captured under extremely low light conditions are noise-limited, which can cause existing robotic vision algorithms to fail. In this paper we develop an image processing technique for aiding 3D reconstruction from images acquired in low light conditions. Our technique, based on burst photography, uses direct methods for image registration within bursts of short exposure time images to improve the robustness and accuracy of feature-based structure-from-motion (SfM). We demonstrate improved SfM performance in challenging light-constrained scenes, including quantitative evaluations that show improved feature performance and camera pose estimates. Additionally, we show that our method converges more frequently to correct reconstructions than the state-of-the-art. Our method is a significant step towards allowing robots to operate in low light conditions, with potential applications to robots operating in environments such as underground mines and night time operation.
CANet: A Context-Aware Network for Shadow Removal
Authors: Zipei Chen, Chengjiang Long, Ling Zhang, Chunxia Xiao
Date: 2021-08-23 00:00:00
Proceeding: ICCV 2021 10
Tasks: Decoder, Patch Matching, Shadow Removal

In this paper, we propose a novel two-stage context-aware network named CANet for shadow removal, in which the contextual information from non-shadow regions is transferred to shadow regions at the embedded feature spaces. At Stage-I, we propose a contextual patch matching (CPM) module to generate a set of potential matching pairs of shadow and non-shadow patches. Combined with the potential contextual relationships between shadow and non-shadow regions, our well-designed contextual feature transfer (CFT) mechanism can transfer contextual information from non-shadow to shadow regions at different scales. With the reconstructed feature maps, we remove shadows at L and A/B channels separately. At Stage-II, we use an encoder-decoder to refine current results and generate the final shadow removal results. We evaluate our proposed CANet on two benchmark datasets and some real-world shadow images with complex scenes. Extensive experimental results strongly demonstrate the efficacy of our proposed CANet and exhibit superior performance to state-of-the-arts.
Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency
Authors: Xueping Wang, Shasha Li, Min Liu, Yaonan Wang, Amit K. Roy-Chowdhury
Date: 2021-08-23 00:00:00
Proceeding: ICCV 2021 10
Tasks: Adversarial Attack, Adversarial Attack Detection, Person Re-Identification

The success of deep neural networks (DNNs) has promoted the widespread applications of person re-identification (ReID). However, ReID systems inherit the vulnerability of DNNs to malicious attacks of visually inconspicuous adversarial perturbations. Detection of adversarial attacks is, therefore, a fundamental requirement for robust ReID systems. In this work, we propose a Multi-Expert Adversarial Attack Detection (MEAAD) approach to achieve this goal by checking context inconsistency, which is suitable for any DNN-based ReID systems. Specifically, three kinds of context inconsistencies caused by adversarial attacks are employed to learn a detector for distinguishing the perturbed examples, i.e., a) the embedding distances between a perturbed query person image and its top-K retrievals are generally larger than those between a benign query image and its top-K retrievals, b) the embedding distances among the top-K retrievals of a perturbed query image are larger than those of a benign query image, c) the top-K retrievals of a benign query image obtained with multiple expert ReID models tend to be consistent, which is not preserved when attacks are present. Extensive experiments on the Market1501 and DukeMTMC-ReID datasets show that, as the first adversarial attack detection approach for ReID, MEAAD effectively detects various adversarial attacks and achieves high ROC-AUC (over 97.5%).
All-Optical Synthesis of an Arbitrary Linear Transformation Using Diffractive Surfaces
Authors: Onur Kulce, Deniz Mengu, Yair Rivenson, Aydogan Ozcan
Date: 2021-08-22 00:00:00
Tasks: All, Deep Learning

We report the design of diffractive surfaces to all-optically perform arbitrary complex-valued linear transformations between an input (N_i) and output (N_o), where N_i and N_o represent the number of pixels at the input and output fields-of-view (FOVs), respectively. First, we consider a single diffractive surface and use a matrix pseudoinverse-based method to determine the complex-valued transmission coefficients of the diffractive features/neurons to all-optically perform a desired/target linear transformation. In addition to this data-free design approach, we also consider a deep learning-based design method to optimize the transmission coefficients of diffractive surfaces by using examples of input/output fields corresponding to the target transformation. We compared the all-optical transformation errors and diffraction efficiencies achieved using data-free designs as well as data-driven (deep learning-based) diffractive designs to all-optically perform (i) arbitrarily-chosen complex-valued transformations including unitary, nonunitary and noninvertible transforms, (ii) 2D discrete Fourier transformation, (iii) arbitrary 2D permutation operations, and (iv) high-pass filtered coherent imaging. Our analyses reveal that if the total number (N) of spatially-engineered diffractive features/neurons is N_i x N_o or larger, both design methods succeed in all-optical implementation of the target transformation, achieving negligible error. However, compared to data-free designs, deep learning-based diffractive designs are found to achieve significantly larger diffraction efficiencies for a given N and their all-optical transformations are more accurate for N < N_i x N_o. These conclusions are generally applicable to various optical processors that employ spatially-engineered diffractive surfaces.
MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching
Authors: Faranak Shamsafar, Samuel Woerz, Rafia Rahim, Andreas Zell
Date: 2021-08-22 00:00:00
Tasks: Depth Estimation, Disparity Estimation, GPU, Stereo Depth Estimation, Stereo Matching

Recent methods in stereo matching have continuously improved the accuracy using deep models. This gain, however, is attained with a high increase in computation cost, such that the network may not fit even on a moderate GPU. This issue raises problems when the model needs to be deployed on resource-limited devices. For this, we propose two light models for stereo vision with reduced complexity and without sacrificing accuracy. Depending on the dimension of cost volume, we design a 2D and a 3D model with encoder-decoders built from 2D and 3D convolutions, respectively. To this end, we leverage 2D MobileNet blocks and extend them to 3D for stereo vision application. Besides, a new cost volume is proposed to boost the accuracy of the 2D model, making it performing close to 3D networks. Experiments show that the proposed 2D/3D networks effectively reduce the computational expense (27%/95% and 72%/38% fewer parameters/operations in 2D and 3D models, respectively) while upholding the accuracy. Our code is available at https://github.com/cogsys-tuebingen/mobilestereonet.
Relating CNNs with brain: Challenges and findings
Authors: Reem Abdel-Salam
Date: 2021-08-22 00:00:00

Conventional neural network models (CNN), loosely inspired by the primate visual system, have been shown to predict neural responses in the visual cortex. However, the relationship between CNNs and the visual system is incomplete due to many reasons. On one hand state of the art CNN architecture is very complex, yet can be fooled by imperceptibly small, explicitly crafted perturbations which makes it hard difficult to map layers of the network with the visual system and to understand what they are doing. On the other hand, we don't know the exact mapping between feature space of the CNNs and the space domain of the visual cortex, which makes it hard to accurately predict neural responses. In this paper we review the challenges and the methods that have been used to predict neural responses in the visual cortex and whole brain as part of The Algonauts Project 2021 Challenge: "How the Human Brain Makes Sense of a World in Motion".
Graph2Pix: A Graph-Based Image to Image Translation Framework
Authors: Dilara Gokay, Enis Simsar, Efehan Atici, Alper Ahmetoglu, Atif Emre Yuksel, Pinar Yanardag
Date: 2021-08-22 00:00:00
Tasks: Image-to-Image Translation, Translation

In this paper, we propose a graph-based image-to-image translation framework for generating images. We use rich data collected from the popular creativity platform Artbreeder (http://artbreeder.com), where users interpolate multiple GAN-generated images to create artworks. This unique approach of creating new images leads to a tree-like structure where one can track historical data about the creation of a particular image. Inspired by this structure, we propose a novel graph-to-image translation model called Graph2Pix, which takes a graph and corresponding images as input and generates a single image as output. Our experiments show that Graph2Pix is able to outperform several image-to-image translation frameworks on benchmark metrics, including LPIPS (with a 25% improvement) and human perception studies (n=60), where users preferred the images generated by our method 81.5% of the time. Our source code and dataset are publicly available at https://github.com/catlab-team/graph2pix.
EKTVQA: Generalized use of External Knowledge to empower Scene Text in Text-VQA
Authors: Arka Ujjal Dey, Ernest Valveny, Gaurav Harit
Date: 2021-08-22 00:00:00
Tasks: Open-Ended Question Answering, Optical Character Recognition (OCR), Question Answering, Visual Question Answering (VQA)

The open-ended question answering task of Text-VQA often requires reading and reasoning about rarely seen or completely unseen scene-text content of an image. We address this zero-shot nature of the problem by proposing the generalized use of external knowledge to augment our understanding of the scene text. We design a framework to extract, validate, and reason with knowledge using a standard multimodal transformer for vision language understanding tasks. Through empirical evidence and qualitative results, we demonstrate how external knowledge can highlight instance-only cues and thus help deal with training data bias, improve answer entity type correctness, and detect multiword named entities. We generate results comparable to the state-of-the-art on three publicly available datasets, under the constraints of similar upstream OCR systems and training data.
Robustness-via-Synthesis: Robust Training with Generative Adversarial Perturbations
Authors: Inci M. Baytas, Debayan Deb
Date: 2021-08-22 00:00:00
Tasks: Diversity

Upon the discovery of adversarial attacks, robust models have become obligatory for deep learning-based systems. Adversarial training with first-order attacks has been one of the most effective defenses against adversarial perturbations to this day. The majority of the adversarial training approaches focus on iteratively perturbing each pixel with the gradient of the loss function with respect to the input image. However, the adversarial training with gradient-based attacks lacks diversity and does not generalize well to natural images and various attacks. This study presents a robust training algorithm where the adversarial perturbations are automatically synthesized from a random vector using a generator network. The classifier is trained with cross-entropy loss regularized with the optimal transport distance between the representations of the natural and synthesized adversarial samples. Unlike prevailing generative defenses, the proposed one-step attack generation framework synthesizes diverse perturbations without utilizing gradient of the classifier's loss. Experimental results show that the proposed approach attains comparable robustness with various gradient-based and generative robust training techniques on CIFAR10, CIFAR100, and SVHN datasets. In addition, compared to the baselines, the proposed robust training framework generalizes well to the natural samples. Code and trained models will be made publicly available.
FEDI: Few-shot learning based on Earth Mover's Distance algorithm combined with deep residual network to identify diabetic retinopathy
Authors: Liangrui Pan, Boya Ji, Peng Xi, Xiaoqi Wang, Mitchai Chongcheawchamnan, Shaoliang Peng
Date: 2021-08-22 00:00:00
Tasks: Few-Shot Learning

Diabetic retinopathy(DR) is the main cause of blindness in diabetic patients. However, DR can easily delay the occurrence of blindness through the diagnosis of the fundus. In view of the reality, it is difficult to collect a large amount of diabetic retina data in clinical practice. This paper proposes a few-shot learning model of a deep residual network based on Earth Mover's Distance algorithm to assist in diagnosing DR. We build training and validation classification tasks for few-shot learning based on 39 categories of 1000 sample data, train deep residual networks, and obtain experience maximization pre-training models. Based on the weights of the pre-trained model, the Earth Mover's Distance algorithm calculates the distance between the images, obtains the similarity between the images, and changes the model's parameters to improve the accuracy of the training model. Finally, the experimental construction of the small sample classification task of the test set to optimize the model further, and finally, an accuracy of 93.5667% on the 3way10shot task of the diabetic retina test set. For the experimental code and results, please refer to: https://github.com/panliangrui/few-shot-learning-funds.
Self-Regulation for Semantic Segmentation
Authors: Zhang Dong, Zhang Hanwang, Tang Jinhui, Hua Xiansheng, Sun Qianru
Date: 2021-08-22 00:00:00
Proceeding: ICCV 2021 10
Tasks: Segmentation, Semantic Segmentation

In this paper, we seek reasons for the two major failure cases in Semantic Segmentation (SS): 1) missing small objects or minor object parts, and 2) mislabeling minor parts of large objects as wrong classes. We have an interesting finding that Failure-1 is due to the underuse of detailed features and Failure-2 is due to the underuse of visual contexts. To help the model learn a better trade-off, we introduce several Self-Regulation (SR) losses for training SS neural networks. By "self", we mean that the losses are from the model per se without using any additional data or supervision. By applying the SR losses, the deep layer features are regulated by the shallow ones to preserve more details; meanwhile, shallow layer classification logits are regulated by the deep ones to capture more semantics. We conduct extensive experiments on both weakly and fully supervised SS tasks, and the results show that our approach consistently surpasses the baselines. We also validate that SR losses are easy to implement in various state-of-the-art SS models, e.g., SPGNet and OCRNet, incurring little computational overhead during training and none for testing.
Spatial Transformer Networks for Curriculum Learning
Authors: Fatemeh Azimi, Jean-Francois Jacques Nicolas Nies, Sebastian Palacio, Federico Raue, Jörn Hees, Andreas Dengel
Date: 2021-08-22 00:00:00
Tasks: image-classification, Image Classification

Curriculum learning is a bio-inspired training technique that is widely adopted to machine learning for improved optimization and better training of neural networks regarding the convergence rate or obtained accuracy. The main concept in curriculum learning is to start the training with simpler tasks and gradually increase the level of difficulty. Therefore, a natural question is how to determine or generate these simpler tasks. In this work, we take inspiration from Spatial Transformer Networks (STNs) in order to form an easy-to-hard curriculum. As STNs have been proven to be capable of removing the clutter from the input images and obtaining higher accuracy in image classification tasks, we hypothesize that images processed by STNs can be seen as easier tasks and utilized in the interest of curriculum learning. To this end, we study multiple strategies developed for shaping the training curriculum, using the data generated by STNs. We perform various experiments on cluttered MNIST and Fashion-MNIST datasets, where on the former, we obtain an improvement of $3.8$pp in classification accuracy compared to the baseline.
Guiding Query Position and Performing Similar Attention for Transformer-Based Detection Heads
Authors: Xiaohu Jiang, Ze Chen, Zhicheng Wang, Erjin Zhou, ChunYuan
Date: 2021-08-22 00:00:00
Tasks: Object, Position

After DETR was proposed, this novel transformer-based detection paradigm which performs several cross-attentions between object queries and feature maps for predictions has subsequently derived a series of transformer-based detection heads. These models iterate object queries after each cross-attention. However, they don't renew the query position which indicates object queries' position information. Thus model needs extra learning to figure out the newest regions that query position should express and need more attention. To fix this issue, we propose the Guided Query Position (GQPos) method to embed the latest location information of object queries to query position iteratively. Another problem of such transformer-based detection heads is the high complexity to perform attention on multi-scale feature maps, which hinders them from improving detection performance at all scales. Therefore we propose a novel fusion scheme named Similar Attention (SiA): besides the feature maps is fused, SiA also fuse the attention weights maps to accelerate the learning of high-resolution attention weight map by well-learned low-resolution attention weight map. Our experiments show that the proposed GQPos improves the performance of a series of models, including DETR, SMCA, YoloS, and HoiTransformer and SiA consistently improve the performance of multi-scale transformer-based detection heads like DETR and HoiTransformer.
Uncertainty-aware Clustering for Unsupervised Domain Adaptive Object Re-identification
Authors: Pengfei Wang, Changxing Ding, Wentao Tan, Mingming Gong, Kui Jia, DaCheng Tao
Date: 2021-08-22 00:00:00
Tasks: Clustering, Object

Unsupervised Domain Adaptive (UDA) object re-identification (Re-ID) aims at adapting a model trained on a labeled source domain to an unlabeled target domain. State-of-the-art object Re-ID approaches adopt clustering algorithms to generate pseudo-labels for the unlabeled target domain. However, the inevitable label noise caused by the clustering procedure significantly degrades the discriminative power of Re-ID model. To address this problem, we propose an uncertainty-aware clustering framework (UCF) for UDA tasks. First, a novel hierarchical clustering scheme is proposed to promote clustering quality. Second, an uncertainty-aware collaborative instance selection method is introduced to select images with reliable labels for model training. Combining both techniques effectively reduces the impact of noisy labels. In addition, we introduce a strong baseline that features a compact contrastive loss. Our UCF method consistently achieves state-of-the-art performance in multiple UDA tasks for object Re-ID, and significantly reduces the gap between unsupervised and supervised Re-ID performance. In particular, the performance of our unsupervised UCF method in the MSMT17$\to$Market1501 task is better than that of the fully supervised setting on Market1501. The code of UCF is available at https://github.com/Wang-pengfei/UCF.
Detection and Localization of Multiple Image Splicing Using MobileNet V1
Authors: Kalyani Kadam, Dr. Swati Ahirrao, Dr. Ketan Kotecha, Sayan Sahu
Date: 2021-08-22 00:00:00

In modern society, digital images have become a prominent source of information and medium of communication. They can, however, be simply altered using freely available image editing software. Two or more images are combined to generate a new image that can transmit information across social media platforms to influence the people in the society. This information may have both positive and negative consequences. Hence there is a need to develop a technique that will detect and locates a multiple image splicing forgery in an image. This research work proposes multiple image splicing forgery detection using Mask R-CNN, with a backbone as a MobileNet V1. It also calculates the percentage score of a forged region of multiple spliced images. The comparative analysis of the proposed work with the variants of ResNet is performed. The proposed model is trained and tested using our MISD (Multiple Image Splicing Dataset), and it is observed that the proposed model outperforms the variants of ResNet models (ResNet 51,101 and 151).
Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift
Authors: Jiefeng Peng, Jiqi Zhang, Changlin Li, Guangrun Wang, Xiaodan Liang, Liang Lin
Date: 2021-08-22 00:00:00
Proceeding: ICCV 2021 10
Tasks: Attribute, Neural Architecture Search

Recently proposed neural architecture search (NAS) methods co-train billions of architectures in a supernet and estimate their potential accuracy using the network weights detached from the supernet. However, the ranking correlation between the architectures' predicted accuracy and their actual capability is incorrect, which causes the existing NAS methods' dilemma. We attribute this ranking correlation problem to the supernet training consistency shift, including feature shift and parameter shift. Feature shift is identified as dynamic input distributions of a hidden layer due to random path sampling. The input distribution dynamic affects the loss descent and finally affects architecture ranking. Parameter shift is identified as contradictory parameter updates for a shared layer lay in different paths in different training steps. The rapidly-changing parameter could not preserve architecture ranking. We address these two shifts simultaneously using a nontrivial supernet-Pi model, called Pi-NAS. Specifically, we employ a supernet-Pi model that contains cross-path learning to reduce the feature consistency shift between different paths. Meanwhile, we adopt a novel nontrivial mean teacher containing negative samples to overcome parameter shift and model collision. Furthermore, our Pi-NAS runs in an unsupervised manner, which can search for more transferable architectures. Extensive experiments on ImageNet and a wide range of downstream tasks (e.g., COCO 2017, ADE20K, and Cityscapes) demonstrate the effectiveness and universality of our Pi-NAS compared to supervised NAS. See Codes: https://github.com/Ernie1/Pi-NAS.