pwc-archive/papers-with-abstracts huggingface dataset:
Deep adversarial attack on target detection systems
Authors: Uche M. Osahor, Nasser M. Nasrabadi
Date: 2021-08-12 00:00:00
Tasks: Adversarial Attack

Target detection systems identify targets by localizing their coordinates on the input image of interest. This is ideally achieved by labeling each pixel in an image as a background or a potential target pixel. Deep Convolutional Neural Network (DCNN) classifiers have proven to be successful tools for computer vision applications. However,prior research confirms that even state of the art classifier models are susceptible to adversarial attacks. In this paper, we show how to generate adversarial infrared images by adding small perturbations to the targets region to deceive a DCNN-based target detector at remarkable levels. We demonstrate significant progress in developing visually imperceptible adversarial infrared images where the targets are visually recognizable by an expert but a DCNN-based target detector cannot detect the targets in the image.
Beyond Fairness Metrics: Roadblocks and Challenges for Ethical AI in Practice
Authors: Jiahao Chen, Victor Storchan, Eren Kurshan
Date: 2021-08-11 00:00:00
Tasks: Ethics, Fairness

We review practical challenges in building and deploying ethical AI at the scale of contemporary industrial and societal uses. Apart from the purely technical concerns that are the usual focus of academic research, the operational challenges of inconsistent regulatory pressures, conflicting business goals, data quality issues, development processes, systems integration practices, and the scale of deployment all conspire to create new ethical risks. Such ethical concerns arising from these practical considerations are not adequately addressed by existing research results. We argue that a holistic consideration of ethics in the development and deployment of AI systems is necessary for building ethical AI in practice, and exhort researchers to consider the full operational contexts of AI systems when assessing ethical risks.
Dual Path Learning for Domain Adaptation of Semantic Segmentation
Authors: Yiting Cheng, Fangyun Wei, Jianmin Bao, Dong Chen, Fang Wen, Wenqiang Zhang
Date: 2021-08-13 00:00:00
Proceeding: ICCV 2021 10
Tasks: Domain Adaptation, Image-to-Image Translation, Segmentation, Self-Supervised Learning, Semantic Segmentation, Synthetic-to-Real Translation, Translation

Domain adaptation for semantic segmentation enables to alleviate the need for large-scale pixel-wise annotations. Recently, self-supervised learning (SSL) with a combination of image-to-image translation shows great effectiveness in adaptive segmentation. The most common practice is to perform SSL along with image translation to well align a single domain (the source or target). However, in this single-domain paradigm, unavoidable visual inconsistency raised by image translation may affect subsequent learning. In this paper, based on the observation that domain adaptation frameworks performed in the source and target domain are almost complementary in terms of image translation and SSL, we propose a novel dual path learning (DPL) framework to alleviate visual inconsistency. Concretely, DPL contains two complementary and interactive single-domain adaptation pipelines aligned in source and target domain respectively. The inference of DPL is extremely simple, only one segmentation model in the target domain is employed. Novel technologies such as dual path image translation and dual path adaptive segmentation are proposed to make two paths promote each other in an interactive manner. Experiments on GTA5$\rightarrow$Cityscapes and SYNTHIA$\rightarrow$Cityscapes scenarios demonstrate the superiority of our DPL model over the state-of-the-art methods. The code and models are available at: \url{https://github.com/royee182/DPL}
Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification
Authors: Shyam A. Tailor, René de Jong, Tiago Azevedo, Matthew Mattina, Partha Maji
Date: 2021-08-13 00:00:00
Tasks: Graph Neural Network, Mixed Reality

In recent years graph neural network (GNN)-based approaches have become a popular strategy for processing point cloud data, regularly achieving state-of-the-art performance on a variety of tasks. To date, the research community has primarily focused on improving model expressiveness, with secondary thought given to how to design models that can run efficiently on resource constrained mobile devices including smartphones or mixed reality headsets. In this work we make a step towards improving the efficiency of these models by making the observation that these GNN models are heavily limited by the representational power of their first, feature extracting, layer. We find that it is possible to radically simplify these models so long as the feature extraction layer is retained with minimal degradation to model performance; further, we discover that it is possible to improve performance overall on ModelNet40 and S3DIS by improving the design of the feature extractor. Our approach reduces memory consumption by 20$\times$ and latency by up to 9.9$\times$ for graph layers in models such as DGCNN; overall, we achieve speed-ups of up to 4.5$\times$ and peak memory reductions of 72.5%.
3D point cloud segmentation using GIS
Authors: Chao-Jung Liu, Vladimir Krylov, Rozenn Dahyot
Date: 2021-08-13 00:00:00
Tasks: Point Cloud Segmentation, Segmentation, Semantic Segmentation

In this paper we propose an approach to perform semantic segmentation of 3D point cloud data by importing the geographic information from a 2D GIS layer (OpenStreetMap). The proposed automatic procedure identifies meaningful units such as buildings and adjusts their locations to achieve best fit between the GIS polygonal perimeters and the point cloud. Our processing pipeline is presented and illustrated by segmenting point cloud data of Trinity College Dublin (Ireland) campus constructed from optical imagery collected by a drone.
Modal-Adaptive Gated Recoding Network for RGB-D Salient Object Detection
Authors: Jinchao Zhu, XiaoYu Zhang, Xian Fang, Feng Dong, Qiu Yu
Date: 2021-08-13 00:00:00
Tasks: Decoder, object-detection, Object Detection, RGB-D Salient Object Detection, Salient Object Detection

The multi-modal salient object detection model based on RGB-D information has better robustness in the real world. However, it remains nontrivial to better adaptively balance effective multi-modal information in the feature fusion phase. In this letter, we propose a novel gated recoding network (GRNet) to evaluate the information validity of the two modes, and balance their influence. Our framework is divided into three phases: perception phase, recoding mixing phase and feature integration phase. First, A perception encoder is adopted to extract multi-level single-modal features, which lays the foundation for multi-modal semantic comparative analysis. Then, a modal-adaptive gate unit (MGU) is proposed to suppress the invalid information and transfer the effective modal features to the recoding mixer and the hybrid branch decoder. The recoding mixer is responsible for recoding and mixing the balanced multi-modal information. Finally, the hybrid branch decoder completes the multi-level feature integration under the guidance of an optional edge guidance stream (OEGS). Experiments and analysis on eight popular benchmarks verify that our framework performs favorably against 9 state-of-art methods.
Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Clouds
Authors: Björn Michele, Alexandre Boulch, Gilles Puy, Maxime Bucher, Renaud Marlet
Date: 2021-08-13 00:00:00
Tasks: Classification, Generalized Zero-Shot Learning, Segmentation, Semantic Segmentation, Zero-Shot Learning

While there has been a number of studies on Zero-Shot Learning (ZSL) for 2D images, its application to 3D data is still recent and scarce, with just a few methods limited to classification. We present the first generative approach for both ZSL and Generalized ZSL (GZSL) on 3D data, that can handle both classification and, for the first time, semantic segmentation. We show that it reaches or outperforms the state of the art on ModelNet40 classification for both inductive ZSL and inductive GZSL. For semantic segmentation, we created three benchmarks for evaluating this new ZSL task, using S3DIS, ScanNet and SemanticKITTI. Our experiments show that our method outperforms strong baselines, which we additionally propose for this task.
CNN-based Two-Stage Parking Slot Detection Using Region-Specific Multi-Scale Feature Extraction
Authors: Quang Huy Bui, Jae Kyu Suhr
Date: 2021-08-13 00:00:00
Tasks: object-detection, Object Detection, Region Proposal

Autonomous parking systems start with the detection of available parking slots. Parking slot detection performance has been dramatically improved by deep learning techniques. Deep learning-based object detection methods can be categorized into one-stage and two-stage approaches. Although it is well-known that the two-stage approach outperforms the one-stage approach in general object detection, they have performed similarly in parking slot detection so far. We consider this is because the two-stage approach has not yet been adequately specialized for parking slot detection. Thus, this paper proposes a highly specialized two-stage parking slot detector that uses region-specific multi-scale feature extraction. In the first stage, the proposed method finds the entrance of the parking slot as a region proposal by estimating its center, length, and orientation. The second stage of this method designates specific regions that most contain the desired information and extracts features from them. That is, features for the location and orientation are separately extracted from only the specific regions that most contain the locational and orientational information. In addition, multi-resolution feature maps are utilized to increase both positioning and classification accuracies. A high-resolution feature map is used to extract detailed information (location and orientation), while another low-resolution feature map is used to extract semantic information (type and occupancy). In experiments, the proposed method was quantitatively evaluated with two large-scale public parking slot detection datasets and outperformed previous methods, including both one-stage and two-stage approaches.
Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks
Authors: Federico Nesti, Giulio Rossolini, Saasha Nair, Alessandro Biondi, Giorgio Buttazzo
Date: 2021-08-13 00:00:00
Tasks: Autonomous Driving, object-detection, Object Detection, Semantic Segmentation

Deep learning and convolutional neural networks allow achieving impressive performance in computer vision tasks, such as object detection and semantic segmentation (SS). However, recent studies have shown evident weaknesses of such models against adversarial perturbations. In a real-world scenario instead, like autonomous driving, more attention should be devoted to real-world adversarial examples (RWAEs), which are physical objects (e.g., billboards and printable patches) optimized to be adversarial to the entire perception pipeline. This paper presents an in-depth evaluation of the robustness of popular SS models by testing the effects of both digital and real-world adversarial patches. These patches are crafted with powerful attacks enriched with a novel loss function. Firstly, an investigation on the Cityscapes dataset is conducted by extending the Expectation Over Transformation (EOT) paradigm to cope with SS. Then, a novel attack optimization, called scene-specific attack, is proposed. Such an attack leverages the CARLA driving simulator to improve the transferability of the proposed EOT-based attack to a real 3D environment. Finally, a printed physical billboard containing an adversarial patch was tested in an outdoor driving scenario to assess the feasibility of the studied attacks in the real world. Exhaustive experiments revealed that the proposed attack formulations outperform previous work to craft both digital and real-world adversarial patches for SS. At the same time, the experimental results showed how these attacks are notably less effective in the real world, hence questioning the practical relevance of adversarial attacks to SS models for autonomous/assisted driving.
IFR: Iterative Fusion Based Recognizer For Low Quality Scene Text Recognition
Authors: Zhiwei Jia, Shugong Xu, Shiyi Mu, Yue Tao, Shan Cao, Zhiyong Chen
Date: 2021-08-13 00:00:00
Tasks: Image Restoration, Scene Text Recognition

Although recent works based on deep learning have made progress in improving recognition accuracy on scene text recognition, how to handle low-quality text images in end-to-end deep networks remains a research challenge. In this paper, we propose an Iterative Fusion based Recognizer (IFR) for low quality scene text recognition, taking advantage of refined text images input and robust feature representation. IFR contains two branches which focus on scene text recognition and low quality scene text image recovery respectively. We utilize an iterative collaboration between two branches, which can effectively alleviate the impact of low quality input. A feature fusion module is proposed to strengthen the feature representation of the two branches, where the features from the Recognizer are Fused with image Restoration branch, referred to as RRF. Without changing the recognition network structure, extensive quantitative and qualitative experimental results show that the proposed method significantly outperforms the baseline methods in boosting the recognition accuracy of benchmark datasets and low resolution images in TextZoom dataset.
Caption Generation on Scenes with Seen and Unseen Object Categories
Authors: Berkan Demirel, Ramazan Gokberk Cinbis
Date: 2021-08-13 00:00:00
Tasks: Caption Generation, Language Modelling

Image caption generation is one of the most challenging problems at the intersection of vision and language domains. In this work, we propose a realistic captioning task where the input scenes may incorporate visual objects with no corresponding visual or textual training examples. For this problem, we propose a detection-driven approach that consists of a single-stage generalized zero-shot detection model to recognize and localize instances of both seen and unseen classes, and a template-based captioning model that transforms detections into sentences. To improve the generalized zero-shot detection model, which provides essential information for captioning, we define effective class representations in terms of class-to-class semantic similarities, and leverage their special structure to construct an effective unseen/seen class confidence score calibration mechanism. We also propose a novel evaluation metric that provides additional insights for the captioning outputs by separately measuring the visual and non-visual contents of generated sentences. Our experiments highlight the importance of studying captioning in the proposed zero-shot setting, and verify the effectiveness of the proposed detection-driven zero-shot captioning approach.
EEEA-Net: An Early Exit Evolutionary Neural Architecture Search
Authors: Chakkrit Termritthikun, Yeshi Jamtsho, Jirarat Ieamsaard, Paisarn Muneesawang, Ivan Lee
Date: 2021-08-13 00:00:00
Tasks: GPU, Image Classification, Keypoint Detection, Neural Architecture Search, object-detection, Object Detection, Semantic Segmentation

The goals of this research were to search for Convolutional Neural Network (CNN) architectures, suitable for an on-device processor with limited computing resources, performing at substantially lower Network Architecture Search (NAS) costs. A new algorithm entitled an Early Exit Population Initialisation (EE-PI) for Evolutionary Algorithm (EA) was developed to achieve both goals. The EE-PI reduces the total number of parameters in the search process by filtering the models with fewer parameters than the maximum threshold. It will look for a new model to replace those models with parameters more than the threshold. Thereby, reducing the number of parameters, memory usage for model storage and processing time while maintaining the same performance or accuracy. The search time was reduced to 0.52 GPU day. This is a huge and significant achievement compared to the NAS of 4 GPU days achieved using NSGA-Net, 3,150 GPU days by the AmoebaNet model, and the 2,000 GPU days by the NASNet model. As well, Early Exit Evolutionary Algorithm networks (EEEA-Nets) yield network architectures with minimal error and computational cost suitable for a given dataset as a class of network algorithms. Using EEEA-Net on CIFAR-10, CIFAR-100, and ImageNet datasets, our experiments showed that EEEA-Net achieved the lowest error rate among state-of-the-art NAS models, with 2.46% for CIFAR-10, 15.02% for CIFAR-100, and 23.8% for ImageNet dataset. Further, we implemented this image recognition architecture for other tasks, such as object detection, semantic segmentation, and keypoint detection tasks, and, in our experiments, EEEA-Net-C2 outperformed MobileNet-V3 on all of these various tasks. (The algorithm code is available at https://github.com/chakkritte/EEEA-Net).
Conditional DETR for Fast Training Convergence
Authors: Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang
Date: 2021-08-13 00:00:00
Proceeding: ICCV 2021 10
Tasks: Decoder, Object, object-detection, Object Detection

The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7x faster for the backbones R50 and R101 and 10x faster for stronger backbones DC5-R50 and DC5-R101. Code is available at https://github.com/Atten4Vis/ConditionalDETR.
Full-resolution quality assessment for pansharpening
Authors: Giuseppe Scarpa, Matteo Ciotola
Date: 2021-08-13 00:00:00
Tasks: Pansharpening

A reliable quality assessment procedure for pansharpening methods is of critical importance for the development of the related solutions. Unfortunately, the lack of ground-truths to be used as guidance for an objective evaluation has pushed the community to resort to two approaches which can also be jointly applied. Hence, two kinds of indexes can be found in the literature: i) reference-based reduced-resolution indexes aimed to assess the synthesis ability; ii) no-reference subjective quality indexes for full-resolution datasets aimed to assess spectral and spatial consistency. Both reference-based and no-reference indexes present critical shortcomings which motivate the community to explore new solutions. In this work, we propose an alternative no-reference full-resolution assessment framework. On one side we introduce a protocol, namely the reprojection protocol, to take care of the spectral consistency issue. On the other side, a new index of the spatial consistency between the pansharpened image and the panchromatic band at full resolution is also proposed. Experimental results carried out on different datasets/sensors demonstrate the effectiveness of the proposed approach.
Effective semantic segmentation in Cataract Surgery: What matters most?
Authors: Theodoros Pissas, Claudio Ravasio, Lyndon Da Cruz, Christos Bergeles
Date: 2021-08-13 00:00:00
Tasks: 2D Semantic Segmentation task 1 (8 classes), 2D Semantic Segmentation task 2 (17 classes), 2D Semantic Segmentation task 3 (25 classes), Semantic Segmentation

Our work proposes neural network design choices that set the state-of-the-art on a challenging public benchmark on cataract surgery, CaDIS. Our methodology achieves strong performance across three semantic segmentation tasks with increasingly granular surgical tool class sets by effectively handling class imbalance, an inherent challenge in any surgical video. We consider and evaluate two conceptually simple data oversampling methods as well as different loss functions. We show significant performance gains across network architectures and tasks especially on the rarest tool classes, thereby presenting an approach for achieving high performance when imbalanced granular datasets are considered. Our code and trained models are available at https://github.com/RViMLab/MICCAI2021_Cataract_semantic_segmentation and qualitative results on unseen surgical video can be found at https://youtu.be/twVIPUj1WZM.
UMFA: A photorealistic style transfer method based on U-Net and multi-layer feature aggregation
Authors: D. Y. Rao, X. J. Wu, H. Li, J. Kittler, T. Y. Xu
Date: 2021-08-13 00:00:00
Tasks: Decoder, Image Reconstruction, Image Stylization, Style Transfer

In this paper, we propose a photorealistic style transfer network to emphasize the natural effect of photorealistic image stylization. In general, distortion of the image content and lacking of details are two typical issues in the style transfer field. To this end, we design a novel framework employing the U-Net structure to maintain the rich spatial clues, with a multi-layer feature aggregation (MFA) method to simultaneously provide the details obtained by the shallow layers in the stylization processing. In particular, an encoder based on the dense block and a decoder form a symmetrical structure of U-Net are jointly staked to realize an effective feature extraction and image reconstruction. Besides, a transfer module based on MFA and "adaptive instance normalization" (AdaIN) is inserted in the skip connection positions to achieve the stylization. Accordingly, the stylized image possesses the texture of a real photo and preserves rich content details without introducing any mask or post-processing steps. The experimental results on public datasets demonstrate that our method achieves a more faithful structural similarity with a lower style loss, reflecting the effectiveness and merit of our approach.