Biao Li

Bio: Biao Li is an academic researcher from National University of Defense Technology. The author has contributed to research in topics: Visualization & Filter (video). The author has an hindex of 3, co-authored 4 publications receiving 39 citations.

Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs

Abstract: To have a better understanding and usage of Convolution Neural Networks (CNNs), the visualization and interpretation of CNNs has attracted increasing attention in recent years. In particular, several Class Activation Mapping (CAM) methods have been proposed to discover the connection between CNN's decision and image regions. In spite of the reasonable visualization, lack of clear and sufficient theoretical support is the main limitation of these methods. In this paper, we introduce two axioms -- Conservation and Sensitivity -- to the visualization paradigm of the CAM methods. Meanwhile, a dedicated Axiom-based Grad-CAM (XGrad-CAM) is proposed to satisfy these axioms as much as possible. Experiments demonstrate that XGrad-CAM is an enhanced version of Grad-CAM in terms of conservation and sensitivity. It is able to achieve better visualization performance than Grad-CAM, while also be class-discriminative and easy-to-implement compared with Grad-CAM++ and Ablation-CAM. The code is available at this https URL.

Axiom−based Grad−CAM: Towards Accurate Visualization and Explanation of CNNs

Abstract: To have a better understanding and usage of Convolution Neural Networks (CNNs), the visualization and interpretation of CNNs has attracted increasing attention in recent years. In particular, several Class Activation Mapping (CAM) methods have been proposed to discover the connection between CNN's decision and image regions. In spite of the reasonable visualization, lack of clear and sufficient theoretical support is the main limitation of these methods. In this paper, we introduce two axioms -- Conservation and Sensitivity -- to the visualization paradigm of the CAM methods. Meanwhile, a dedicated Axiom-based Grad-CAM (XGrad-CAM) is proposed to satisfy these axioms as much as possible. Experiments demonstrate that XGrad-CAM is an enhanced version of Grad-CAM in terms of conservation and sensitivity. It is able to achieve better visualization performance than Grad-CAM, while also be class-discriminative and easy-to-implement compared with Grad-CAM++ and Ablation-CAM. The code is available at this https URL.

Robust Long-Term Tracking via Instance-Specific Proposals

Abstract: Correlation filter-based trackers are able to achieve long-term tracking when an additional detector is available. However, it is still challenging to achieve robust and accurate tracking due to several complicated situations, including occlusion and severe deformation. This is because a simple model is difficult to adapt to dramatic appearance changes of the object. Furthermore, redetection results are sometimes unreliable as the detector is trained on only a limited number of samples. In this paper, we propose a tracker named Complementary Learners with Instance-specific Proposals (CLIP). This tracker consists of three major components, including a translation filter, a scale filter, and an error correction (EC) module. The translation filter incorporates complementary features to handle severe target appearance variations. It is further combined with the scale filter to predict the state of the target. Finally, an adaptive updating mechanism is proposed to balance the stability and flexibility of the tracking model. Moreover, an instance-specific proposal generator is embedded into the EC module to recover the lost target from tracking failures. The experimental results on OTB2015, VOT2016, Temple-Color 128, and UAV20L demonstrate that the proposed CLIP tracker achieves comparable performance with the state-of-the-art trackers on several scenarios, including occlusion, deformation, and out-of-plane rotation. Moreover, the CLIP tracker is able to run at a speed of 35 frames/s on OTB2015, which makes it highly suitable for many real-time applications.

Long-term Object Tracking with Instance Specific Proposals

Abstract: Correlation filter based trackers have been extensively investigated for their superior efficiency and fairly good robustness. However, it remains challenging to achieve longterm tracking when the object is under occlusion and severe deformation. In this paper, we propose a tracker named Complementary Learners with Instance-specific Proposals (CLIP). The CLIP tracker consists of three main components, including a translation filter, a scale filter, and an error correction module. Complementary features are incorporated into the translation filter to cope with illumination changes and deformation, and an adaptive updating mechanism is proposed to prevent model corruption. The translation filter aims to provide an excellent real-time inference. Furthermore, the error correction module is activated to correct the localization error by an instance-specific proposal generator, especially when the target suffers from dramatic appearance changes. Experimental results on the OTB, Temple-Color 128 and UAV20L datasets demonstrate that the CLIP tracker performs favorably against existing competitive trackers in term of accuracy and robustness. Moreover, our proposed CLIP tracker runs at the speed of 33 fps on the OTB. It is highly suitable for real-time applications.

Deep Learning for 3D Point Clouds: A Survey

Abstract: Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions

Deep weakly-supervised learning methods for classification and localization in histology images: a survey.

Abstract: Using state-of-the-art deep learning models for cancer diagnosis presents several challenges related to the nature and availability of labeled histology images. In particular, cancer grading and localization in these images normally relies on both image- and pixel-level labels, the latter requiring a costly annotation process. In this survey, deep weakly-supervised learning (WSL) models are investigated to identify and locate diseases in histology images, without the need for pixel-level annotations. Given training data with global image-level labels, these models allow to simultaneously classify histology images and yield pixel-wise localization scores, thereby identifying the corresponding regions of interest (ROI). Since relevant WSL models have mainly been investigated within the computer vision community, and validated on natural scene images, we assess the extent to which they apply to histology images which have challenging properties, e.g. very large size, similarity between foreground/background, highly unstructured regions, stain heterogeneity, and noisy/ambiguous labels. The most relevant models for deep WSL are compared experimentally in terms of accuracy (classification and pixel-wise localization) on several public benchmark histology datasets for breast and colon cancer -- BACH ICIAR 2018, BreaKHis, CAMELYON16, and GlaS. Furthermore, for large-scale evaluation of WSL models on histology images, we propose a protocol to construct WSL datasets from Whole Slide Imaging. Results indicate that several deep learning models can provide a high level of classification accuracy, although accurate pixel-wise localization of cancer regions remains an issue for such images. Code is publicly available.

Deep Learning for 3D Point Clouds: A Survey

Abstract: Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.

Integrated Grad-Cam: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks Via Integrated Gradient-Based Scoring

Abstract: Visualizing the features captured by Convolutional Neural Networks (CNNs) is one of the conventional approaches to interpret the predictions made by these models in numerous image recognition applications. Grad-CAM is a popular solution that provides such a visualization by combining the activation maps obtained from the model. However, the average gradient-based terms deployed in this method underestimates the contribution of the representations discovered by the model to its predictions. Addressing this problem, we introduce a solution to tackle this issue by computing the path integral of the gradient-based terms in Grad-CAM. We conduct a thorough analysis to demonstrate the improvement achieved by our method in measuring the importance of the extracted representations for the CNN’s predictions, which yields to our method’s administration in object localization and model interpretation.

Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation

Abstract: As an emerging field in Machine Learning, Explainable AI (XAI) has been offering remarkable performance in interpreting the decisions made by Convolutional Neural Networks (CNNs). To achieve visual explanations for CNNs, methods based on class activation mapping and randomized input sampling have gained great popularity. However, the attribution methods based on these techniques provide lower resolution and blurry explanation maps that limit their explanation power. To circumvent this issue, visualization based on various layers is sought. In this work, we collect visualization maps from multiple layers of the model based on an attribution-based input sampling technique and aggregate them to reach a fine-grained and complete explanation. We also propose a layer selection strategy that applies to the whole family of CNN-based models, based on which our extraction framework is applied to visualize the last layers of each convolutional block of the model. Moreover, we perform an empirical analysis of the efficacy of derived lower-level information to enhance the represented attributions. Comprehensive experiments conducted on shallow and deep models trained on natural and industrial datasets, using both ground-truth and model-truth based evaluation metrics validate our proposed algorithm by meeting or outperforming the state-of-the-art methods in terms of explanation ability and visual quality, demonstrating that our method shows stability regardless of the size of objects or instances to be explained.