Deep learning dominates image classification tasks, yet understanding how models arrive at predictions remains a challenge. Much research focuses on local explanations of individual predictions, such as saliency maps, which visualise the influence of specific pixels on a model's prediction. However, reviewing many of these explanations to identify recurring patterns is infeasible, while global methods often oversimplify and miss important local behaviours. To address this, we propose Segment Attribution Tables (SATs), a method for summarising local saliency explanations into (semi-)global insights. SATs take image segments (such as "eyes" in Chihuahuas) and leverage saliency maps to quantify their influence. These segments highlight concepts the model relies on across instances and reveal spurious correlations, such as reliance on backgrounds or watermarks, even when out-of-distribution test performance sees little change. SATs can explain any classifier for which a form of saliency map can be produced, using segmentation maps that provide named segments. SATs bridge the gap between oversimplified global summaries and overly detailed local explanations, offering a practical tool for analysing and debugging image classifiers.
We propose a novel image retrieval framework for visual saliency detection using information about salient objects contained within bounding box annotations for similar images. For each test image, we train a customized SVM from similar example images to predict the saliency values of its object proposals and generate an external saliency map (ES) by aggregating the regional scores. To overcome limitations caused by the size of the training dataset, we also propose an internal optimization module which computes an internal saliency map (IS) by measuring the low-level contrast information of th…
UCSF（加州大学旧金山分校）；SFVAMC（旧金山退伍军人事务医疗中心）；Helen Diller Family Comprehensive Cancer Center（海伦·迪勒家庭综合癌症中心）；Institute for Neurodegenerative Diseases（神经退行性疾病研究所）；Bakar Computational Health Sciences Institute（巴卡尔计算健康科学研究所）
In high-stakes applications of machine learning models, interpretability methods provide guarantees that models are right for the right reasons. In medical imaging, saliency maps have become the standard tool for determining whether a neural model has learned relevant robust features, rather than artefactual noise. However, saliency maps are limited to local model explanation because they interpret predictions on an image-by-image basis. We propose aggregating saliency globally, using semantic segmentation masks, to provide quantitative measures of model bias across a dataset. To evaluate glob…
The aim of this work is to detect and automatically generate high-level explanations of anomalous events in video. Understanding the cause of an anomalous event is crucial as the required response is dependant on its nature and severity. Recent works typically use object or action classifier to detect and provide labels for anomalous events. However, this constrains detection systems to a finite set of known classes and prevents generalisation to unknown objects or behaviours. Here we show how to robustly detect anomalies without the use of object or action classifiers yet still recover the hi…
This paper proposes an unsupervised bottom-up saliency detection approach by aggregating complementary background template with refinement. Feature vectors are extracted from each superpixel to cover regional color, contrast and texture information. By using these features, a coarse detection for salient region is realized based on background template achieved by different combinations of boundary regions instead of only treating four boundaries as background. Then, by ranking the relevance of the image nodes with foreground cues extracted from the former saliency map, we obtain an improved re…
TASED-Net is a 3D fully-convolutional network architecture for video saliency detection. It consists of two building blocks: first, the encoder network extracts low-resolution spatiotemporal features from an input clip of several consecutive frames, and then the following prediction network decodes the encoded features spatially while aggregating all the temporal information. As a result, a single prediction map is produced from an input clip of multiple frames. Frame-wise saliency maps can be predicted by applying TASED-Net in a sliding-window fashion to a video. The proposed approach assumes…
We describe an explainable AI saliency map method for use with deep convolutional neural networks (CNN) that is much more efficient than popular fine-resolution gradient methods. It is also quantitatively similar or better in accuracy. Our technique works by measuring information at the end of each network scale which is then combined into a single saliency map. We describe how saliency measures can be made more efficient by exploiting Saliency Map Order Equivalence. We visualize individual scale/layer contributions by using a Layer Ordered Visualization of Information. This provides an intere…
Sun Yat-sen University（中山大学）；Advanced Digital Sciences Center（先进数字科学中心）；Anhui University（安徽大学）
Driven by recent vision and graphics applications such as image segmentation and object recognition, computing pixel-accurate saliency values to uniformly highlight foreground objects becomes increasingly important. In this paper, we propose a unified framework called PISA, which stands for Pixelwise Image Saliency Aggregating various bottom-up cues and priors. It generates spatially coherent yet detail-preserving, pixel-accurate and fine-grained saliency, and overcomes the limitations of previous methods which use homogeneous superpixel-based and color only treatment. PISA aggregates multiple…
IIT Kharagpur（印度理工学院卡拉格普尔分校）；University of Poitiers（普瓦捷大学）；IIT Jodhpur（印度理工学院焦特布尔分校）
Deep neural networks have shown their profound impact on achieving human level performance in visual saliency prediction. However, it is still unclear how they learn the task and what it means in terms of understanding human visual system. In this work, we develop a technique to derive explainable saliency models from their corresponding deep neural architecture based saliency models by applying human perception theories and the conventional concepts of saliency. This technique helps us understand the learning pattern of the deep network at its intermediate layers through their activation maps…
Medical image quality assessment is an important aspect of image acquisition, as poor-quality images may lead to misdiagnosis. Manual labelling of image quality is a tedious task for population studies and can lead to misleading results. While much research has been done on automated analysis of image quality to address this issue, relatively little work has been done to explain the methodologies. In this work, we propose an explainable image quality assessment system and validate our idea on two different objectives which are foreign object detection on Chest X-Rays (Object-CXR) and Left Vent…
Dalian University of Technology（大连理工大学）；Tiwaki Co.Ltd（Tiwaki株式会社）
Fully convolutional neural networks (FCNs) have shown outstanding performance in many dense labeling problems. One key pillar of these successes is mining relevant information from features in convolutional layers. However, how to better aggregate multi-level convolutional feature maps for salient object detection is underexplored. In this work, we present Amulet, a generic aggregating multi-level convolutional feature framework for salient object detection. Our framework first integrates multi-level feature maps into multiple resolutions, which simultaneously incorporate coarse semantics and …
Accurate Diagnosis of Respiratory Viruses Using an Explainable Machine Learning with Mid-Infrared Biomolecular Fingerprinting of Nasopharyngeal Secretions
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；School of Instrumentation and Optoelectronic Engineering, Beihang University（北京航空航天大学仪器科学与光电工程学院）；Department of Science and Development, Beijing Youan Hospital, Capital Medical University（首都医科大学附属北京佑安医院科学发展部）
SL-CBM: Enhancing Concept Bottleneck Models with Semantic Locality for Better Interpretability
Saarland University（萨尔大学）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Science Tokyo（东京科学大学）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；Institute of Software, Chinese Academy of Science（中国科学院软件研究所）
Improving the interpretability of geospatial artificial intelligence (GeoAI) models has become critically important to open the "black box" of complex AI models, such as deep learning. This paper compares popular saliency map generation techniques and their strengths and weaknesses in interpreting GeoAI and deep learning models' reasoning behaviors, particularly when applied to geospatial analysis and image processing tasks. We surveyed two broad classes of model explanation methods: perturbation-based and gradient-based methods. The former identifies important image areas, which help machines…
EXplainable AI (XAI) methods have been proposed to interpret how a deep neural network predicts inputs through model saliency explanations that highlight the parts of the inputs deemed important to arrive a decision at a specific target. However, it remains challenging to quantify correctness of their interpretability as current evaluation approaches either require subjective input from humans or incur high computation cost with automated evaluation. In this paper, we propose backdoor trigger patterns--hidden malicious functionalities that cause misclassification--to automate the evaluation of…
A new brand of technical artificial intelligence ( Explainable AI ) research has focused on trying to open up the 'black box' and provide some explainability. This paper presents a novel visual explanation method for deep learning networks in the form of a saliency map that can effectively localize entire object regions. In contrast to the current state-of-the art methods, the proposed method shows quite promising visual explanations that can gain greater trust of human expert. Both quantitative and qualitative evaluations are carried out on both general and clinical data sets to confirm the e…
On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective
Université Paul-Sabatier（保罗萨巴蒂尔大学）；Institut de Recherche Technologique Saint-Exupéry（圣埃克苏佩里技术研究院）；Carney Institute for Brain Science,Brown University（布朗大学卡尼脑科学研究所）；Innovation & Research Division, SNCF（法国国家铁路公司创新与研究部）
Bridging visual saliency and large language models for explainable deep learning in medical imaging
Department of Mathematics and Computer science（数学与计算机科学系）；Institute of Cognitive Science（认知科学研究所）；Rhodes University（罗德斯大学）；National Institute for Theoretical and Computational Sciences (NITheCS)（理论与计算科学国家研究所）
Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Saliency methods interpret the prediction of a neural network by showing the importance of input elements for that prediction. A popular family of saliency methods utilize gradient information. In this work, we empirically show that two approaches for handling the gradient information, namely positive aggregation, and positive propagation, break these methods. Though these methods reflect visually salient information in the input, they do not explain the model prediction anymore as the generated saliency maps are insensitive to the predicted output and are insensitive to model parameter random…
Traffic accident anticipation is a vital function of Automated Driving Systems (ADSs) for providing a safety-guaranteed driving experience. An accident anticipation model aims to predict accidents promptly and accurately before they occur. Existing Artificial Intelligence (AI) models of accident anticipation lack a human-interpretable explanation of their decision-making. Although these models perform well, they remain a black-box to the ADS users, thus difficult to get their trust. To this end, this paper presents a Gated Recurrent Unit (GRU) network that learns spatio-temporal relational fea…
The emerging field of Explainable Artificial Intelligence focuses on researching methods of explaining the decision making processes of complex machine learning models. In the field of explainability for Computer Vision, explanations are provided as saliency maps, which visualize the importance of individual pixels of the input w.r.t. the model's prediction. In this work we focus on a perturbation-based, model-agnostic explainability method called RISE, elaborate on observed shortcomings of its grid-based approach and propose two modifications: replacement of square occlusions with convex poly…
CoPA: Hierarchical Concept Prompting and Aggregating Network for Explainable Diagnosis
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China（电子信息学院，华中科技大学，武汉）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China（计算机科学与工程系，香港科技大学，香港）
High-quality saliency maps are essential in several machine learning application areas including explainable AI and weakly supervised object detection and segmentation. Many techniques have been developed to generate better saliency using neural networks. However, they are often limited to specific saliency visualisation methods or saliency issues. We propose a novel saliency enhancing approach called SESS (Saliency Enhancing with Scaling and Sliding). It is a method and model agnostic extension to existing saliency map generation methods. With SESS, existing saliency approaches become robust …
Huawei Ireland Research Center（华为爱尔兰研究中心）；Boston University（波士顿大学）；Istituto Italiano di Tecnologia（意大利理工学院）；Naver Labs Europe（欧洲Naver实验室）；Microsoft（微软）；Adobe Research（Adobe研究院）；Università di Verona（维罗纳大学）
Conventionally, AI models are thought to trade off explainability for lower accuracy. We develop a training strategy that not only leads to a more explainable AI system for object classification, but as a consequence, suffers no perceptible accuracy degradation. Explanations are defined as regions of visual evidence upon which a deep classification network makes a decision. This is represented in the form of a saliency map conveying how much each pixel contributed to the network's decision. Our training strategy enforces a periodic saliency-based feedback to encourage the model to focus on the…
Within the set of the many complex factors driving gaze placement, the properities of an image that are associated with fixations under free viewing conditions have been studied extensively. There is a general impression that the field is close to understanding this particular association. Here we frame saliency models probabilistically as point processes, allowing the calculation of log-likelihoods and bringing saliency evaluation into the domain of information. We compared the information gain of state-of-the-art models to a gold standard and find that only one third of the explainable spati…
Northwestern Polytechincal University（西北工业大学）；University of California, Merced（加州大学默塞德分校）；Google Cloud（谷歌云）
Contexts play an important role in the saliency detection task. However, given a context region, not all contextual information is helpful for the final task. In this paper, we propose a novel pixel-wise contextual attention network, i.e., the PiCANet, to learn to selectively attend to informative context locations for each pixel. Specifically, for each pixel, it can generate an attention map in which each attention weight corresponds to the contextual relevance at each context location. An attended contextual feature can then be constructed by selectively aggregating the contextual informatio…
XAI-CLIP: ROI-Guided Perturbation Framework for Explainable Medical Image Segmentation in Multimodal Vision-Language Models
King Fahd University of Petroleum and Minerals（国王法赫德石油和矿物大学）；Massachusetts Institute of Technology（麻省理工学院）；Imperial College London（伦敦帝国学院）；KFUPM-SDAIA Joint Research Centre for Artificial Intelligence（KFUPM-SDAIA联合人工智能研究中心）
LICO: Explainable Models with Language-Image Consistency
Fudan University（复旦大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；Shanghai Center for Brain Science and Brain-inspired Technology（上海脑科学与类脑研究中心）
Our overarching goal is to develop an accurate and explainable model for plant disease identification using hyperspectral data. Charcoal rot is a soil borne fungal disease that affects the yield of soybean crops worldwide. Hyperspectral images were captured at 240 different wavelengths in the range of 383 - 1032 nm. We developed a 3D Convolutional Neural Network model for soybean charcoal rot disease identification. Our model has classification accuracy of 95.73\% and an infected class F1 score of 0.87. We infer the trained model using saliency map and visualize the most sensitive pixel locati…
TU Berlin（柏林工业大学）；Fraunhofer HHI（弗劳恩霍夫海因里希·赫兹研究所）；Leibniz Institute of Agricultural Engineering and Bioeconomy e.V. (ATB)（莱布尼茨农业工程与生物经济研究所）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；University of Potsdam（波茨坦大学）
Here we present DeepGaze II, a model that predicts where people look in images. The model uses the features from the VGG-19 deep neural network trained to identify objects in images. Contrary to other saliency models that use deep features, here we use the VGG features for saliency prediction with no additional fine-tuning (rather, a few readout layers are trained on top of the VGG features to predict saliency). The model is therefore a strong test of transfer learning. After conservative cross-validation, DeepGaze II explains about 87% of the explainable information gain in the patterns of fi…
Mila, Québec Artificial Intelligence Institute（米拉魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Imagia Cybernetics；CIFAR（加拿大高等研究院）
Poor generalization is one symptom of models that learn to predict target variables using spuriously-correlated image features present only in the training distribution instead of the true image features that denote a class. It is often thought that this can be diagnosed visually using attribution (aka saliency) maps. We study if this assumption is correct. In some prediction tasks, such as for medical images, one may have some images with masks drawn by a human expert, indicating a region of the image containing relevant information to make the prediction. We study multiple methods that take …
Instance-level quantitative saliency in multiple sclerosis lesion segmentation
Translational Imaging in Neurology (ThINk)（神经学转化成像（ThINk））；Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel（医学与生物医学工程系，巴塞尔大学医院和巴塞尔大学）；Department of Neurology, University Hospital Basel（神经病学系，巴塞尔大学医院）；Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB)（巴塞尔临床神经免疫学和神经科学研究中心（RC2NB））；MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland（MedGIFT，信息学院，管理学院，瓦莱-迪-瓦尔省应用科学与艺术西方瑞士大学）；CIBM Center for Biomedical Imaging, Lausanne, Switzerland（洛桑瑞士生物医学成像中心）；Radiology Department, Lausanne University Hospital (CHUV) and University of Lausanne（放射科，洛桑大学医院（CHUV）和洛桑大学）
Fraunhofer Center for Machine Learning（弗劳恩霍夫机器学习中心）；Fraunhofer IOSB（弗劳恩霍夫光电、系统技术与图像处理研究所）
Deep neural networks, especially convolutional deep neural networks, are state-of-the-art methods to classify, segment or even generate images, movies, or sounds. However, these methods lack of a good semantic understanding of what happens internally. The question, why a COVID-19 detector has classified a stack of lung-ct images as positive, is sometimes more interesting than the overall specificity and sensitivity. Especially when human domain expert knowledge disagrees with the given output. This way, human domain experts could also be advised to reconsider their choice, regarding the inform…
A Deep Learning Approach for Automated Skin Lesion Diagnosis with Explainable AI
1 ,2,3Department of Computer Science \& Engineering, Green University of Bangladesh, Dhaka, Bangladesh；4Department of Computer Science \& Engineering, Bangladesh University of Business；5Department of Internet of Things \& Robotics Engineering, University of Frontier Technology, Bangladesh
