Grouping has been commonly used in deep metric learning for computing diverse features. However, current methods are prone to overfitting and lack interpretability. In this work, we propose an improved and interpretable grouping method to be integrated flexibly with any metric learning framework. Our method is based on the attention mechanism with a learnable query for each group. The query is fully trainable and can capture group-specific information when combined with the diversity loss. An appealing property of our method is that it naturally lends itself interpretability. The attention scores between the learnable query and each spatial position can be interpreted as the importance of that position. We formally show that our proposed grouping method is invariant to spatial permutations of features. When used as a module in convolutional neural networks, our method leads to translational invariance. We conduct comprehensive experiments to evaluate our method. Our quantitative results indicate that the proposed method outperforms prior methods consistently and significantly across different datasets, evaluation metrics, base models, and loss functions. For the first time to the best of our knowledge, our interpretation results clearly demonstrate that the proposed method enables the learning of distinct and diverse features across groups. The code is available on https://github.com/XinyiXuXD/DGML-master.
Pain is a serious and costly issue globally, but to be treated, it must first be detected. Vision transformers are a top-performing architecture in computer vision, with little research on their use for pain detection. In this paper, we propose the first fully-attentive automated pain detection pipeline that achieves state-of-the-art performance on binary pain detection from facial expressions. The model is trained on the UNBC-McMaster dataset, after faces are 3D-registered and rotated to the canonical frontal view. In our experiments we identify important areas of the hyperparameter space and…
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification
Tokyo University of Agriculture and Technology (TUAT)（东京农工大学）；RIKEN Center for Advanced Intelligence Project (RIKEN AIP)（理化学研究所先进智能研究中心）；Graduate School of Medicine, Juntendo University（顺天堂大学医学院）；Faculty of Health Data Science, Juntendo University（顺天堂大学健康数据科学学部）；RIKEN（理化学研究所）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
In group activity recognition, hierarchical framework is widely adopted to represent the relationships between individuals and their corresponding group, and has achieved promising performance. However, the existing methods simply employed max/average pooling in this framework, which ignored the distinct contributions of different individuals to the group activity recognition. In this paper, we propose a new contextual pooling scheme, named attentive pooling, which enables the weighted information transition from individual actions to group activity. By utilizing the attention mechanism, the a…
Peter Munk Cardiac Center（彼得·芒克心脏中心）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；University of Waterloo（滑铁卢大学）
Medical image segmentation is a difficult but important task for many clinical operations such as cardiac bi-ventricular volume estimation. More recently, there has been a shift to utilizing deep learning and fully convolutional neural networks (CNNs) to perform image segmentation that has yielded state-of-the-art results in many public benchmark datasets. Despite the progress of deep learning in medical image segmentation, standard CNNs are still not fully adopted in clinical settings as they lack robustness and interpretability. Shapes are generally more meaningful features than solely textu…
An Interpretable Cross-Attentive Multi-modal MRI Fusion Framework for Schizophrenia Diagnosis
Tulane University（杜兰大学）；Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)（跨机构神经成像与数据科学转化研究中心（TReNDS））；Georgia State University（佐治亚州立大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
We present an interpretable deep model for fine-grained visual recognition. At the core of our method lies the integration of region-based part discovery and attribution within a deep neural network. Our model is trained using image-level object labels, and provides an interpretation of its results via the segmentation of object parts and the identification of their contributions towards classification. To facilitate the learning of object parts without direct supervision, we explore a simple prior of the occurrence of object parts. We demonstrate that this prior, when combined with our region…
HKUST（香港科技大学）；University of Maryland, College Park（马里兰大学帕克分校）；Zhejiang University（浙江大学）；Alibaba A.I. Labs（阿里巴巴人工智能实验室）；City University of Hong Kong（香港城市大学）
Freehand sketching is a dynamic process where points are sequentially sampled and grouped as strokes for sketch acquisition on electronic devices. To recognize a sketched object, most existing methods discard such important temporal ordering and grouping information from human and simply rasterize sketches into binary images for classification. In this paper, we propose a novel single-branch attentive network architecture RNN-Rasterization-CNN (Sketch-R2CNN for short) to fully leverage the dynamics in sketches for recognition. Sketch-R2CNN takes as input only a vector sketch with grouped seque…
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）；Sun Yat-sen University（中山大学）；Southwest Jiaotong University（西南交通大学）；Nanyang Technological University（南洋理工大学）
Deep networks achieve excellent results on large-scale clean data but degrade significantly when learning from noisy labels. To suppressing the impact of mislabeled data, this paper proposes a conceptually simple yet efficient training block, termed as Attentive Feature Mixup (AFM), which allows paying more attention to clean samples and less to mislabeled ones via sample interactions in small groups. Specifically, this plug-and-play AFM first leverages a \textit{group-to-attend} module to construct groups and assign attention weights for group-wise samples, and then uses a \textit{mixup} modu…
Stanford University（斯坦福大学）；University of Florida（佛罗里达大学）；University of Adelaide（阿德莱德大学）
This paper addresses the problem of path prediction for multiple interacting agents in a scene, which is a crucial step for many autonomous platforms such as self-driving cars and social robots. We present \textit{SoPhie}; an interpretable framework based on Generative Adversarial Network (GAN), which leverages two sources of information, the path history of all the agents in a scene, and the scene context information, using images of the scene. To predict a future path for an agent, both physical and social information must be leveraged. Previous work has not been successful to jointly model …
Channel-Level Relation to Attentive Aggregation with Neighborhood-Homogeneity Constraint for Point Cloud Analysis
1School of Automation Science；Electrical Engineering, Beihang University, Beijing, China 2Wuhan Leaddo Measuring \& Control Technology, Wuhan, China 3School of Artificial Intelligence, Beihang University, Beijing, China Emails
University of Guelph（圭尔夫大学）；CIFAR（加拿大高等研究院）；Vector Institute（向量研究所）；University of Waterloo（滑铁卢大学）；Waterloo AI Institute（滑铁卢人工智能研究所）
Deep learning has been shown to outperform traditional machine learning algorithms across a wide range of problem domains. However, current deep learning algorithms have been criticized as uninterpretable "black-boxes" which cannot explain their decision making processes. This is a major shortcoming that prevents the widespread application of deep learning to domains with regulatory processes such as finance. As such, industries such as finance have to rely on traditional models like decision trees that are much more interpretable but less effective than deep learning for complex problems. In …
Dissecting Query-Key Interaction in Vision Transformers
University of Miami（迈阿密大学）；Harvard University（哈佛大学）；Michigan State University（密歇根州立大学）；University of Texas Health Science Center at Houston（休斯顿德克萨斯大学健康科学中心）
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；TU Kaiserslautern（凯泽斯劳滕工业大学）
Classification problems solved with deep neural networks (DNNs) typically rely on a closed world paradigm, and optimize over a single objective (e.g., minimization of the cross-entropy loss). This setup dismisses all kinds of supporting signals that can be used to reinforce the existence or absence of a particular pattern. The increasing need for models that are interpretable by design makes the inclusion of said contextual signals a crucial necessity. To this end, we introduce the notion of Self-Supervised Autogenous Learning (SSAL) models. A SSAL objective is realized through one or more add…
Bodhi VLM: Privacy-Alignment Modeling for Hierarchical Visual Representations in Vision Backbones and VLM Encoders via Bottom-Up and Top-Down Feature Search
Auckland University of Technology（奥克兰技术大学）；Resideo Technologies, Inc.（Resideo技术公司）；Guilin University of Electronic Technology（桂林电子科技大学）；Universidad de Chile（智利大学）
The ability to look multiple times through a series of pose-adjusted glimpses is fundamental to human vision. This critical faculty allows us to understand highly complex visual scenes. Short term memory plays an integral role in aggregating the information obtained from these glimpses and informing our interpretation of the scene. Computational models have attempted to address glimpsing and visual attention but have failed to incorporate the notion of memory. We introduce a novel, biologically inspired visual working memory architecture that we term the Hebb-Rosenblatt memory. We subsequently…
