Mammalian brains handle complex reasoning tasks in a gestalt manner by integrating information from regions of the brain that are specialised to individual sensory modalities. This allows for improved robustness and better generalisation ability. In contrast, deep neural networks are usually designed to process one particular information stream and susceptible to various types of adversarial perturbations. While many methods exist for detecting and defending against adversarial attacks, they do not generalise across a range of attacks and negatively affect performance on clean, unperturbed data. We developed a fusion model using a combination of background and foreground features extracted in parallel from Places-CNN and Imagenet-CNN. We tested the benefits of the fusion approach on preserving adversarial robustness for human perceivable (e.g., Gaussian blur) and network perceivable (e.g., gradient-based) attacks for CIFAR-10 and MS COCO data sets. For gradient based attacks, our results show that fusion allows for significant improvements in classification without decreasing performance on unperturbed data and without need to perform adversarial retraining. Our fused model revealed improvements for Gaussian blur type perturbations as well. The increase in performance from fusion approach depended on the variability of the image contexts; larger increases were seen for classes of images with larger differences in their contexts. We also demonstrate the effect of regularization to bias the classifier decision in the presence of a known adversary. We propose that this biologically inspired approach to integrate information across multiple modalities provides a new way to improve adversarial robustness that can be complementary to current state of the art approaches.
$\textrm{A}^{\textrm{2}}$RNet: Adversarial Attack Resilient Network for Robust Infrared and Visible Image Fusion
School of Computer and Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）；School of Software Technology, Dalian University of Technology（大连理工大学软件学院）
Face Aging with Contextual Generative Adversarial Nets
SKLOIS, IIE, CAS（中国科学院信息工程研究所信息安全国家重点实验室）；School of Cyber Security, UCAS（中国科学院大学网络空间安全学院）；University of Trento（特伦托大学）；Nanjing University of Science and Technology（南京理工大学）；Qihoo 360 AI Institute（奇虎360人工智能研究院）；National University of Singapore（新加坡国立大学）
Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer's Disease Prediction
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen University（深圳大学）；Renmin University of China（中国人民大学）；Harbin Engineering University（哈尔滨工程大学）
Single Stage Warped Cloth Learning and Semantic-Contextual Attention Feature Fusion for Virtual TryOn
Bharti School of Telecommunications, Indian Institute of Technology, Delhi, India（印度德里印度理工学院巴蒂电信学院）；Indian Institute of Information Technology, Sonepat, Haryana, India（印度哈里亚纳邦索内帕特印度信息技术学院）；Department of Electrical Engineering, Indian Institute of Technology, Delhi, India（印度德里印度理工学院电气工程系）
Evidence fusion with contextual discounting for multi-modality medical image segmentation
Heudiasyc（Heudiasyc实验室）；CNRS（法国国家科学研究中心）；Université de technologie de Compiègne（贡比涅技术大学）；Quantif（Quantif团队）；LITIS（LITIS实验室）；University of Rouen Normandy（鲁昂诺曼底大学）；Institut universitaire de France（法国大学研究院）；Henri Becquerel Cancer Center（亨利·贝克勒尔癌症中心）
V-LinkNet: Learning Contextual Inpainting Across Latent Space of Generative Adversarial Network
Manchester Metropolitan University（曼彻斯特城市大学）；Image Metrics Ltd（Image Metrics有限公司）
图像修补是图像处理任务中的一项关键技术，它能预测图像中的缺失区域，生成逼真的图像。考虑到具有特征提取、传播和重建能力的现有生成修复模型的进步，在更深层中缺乏高质量的特征提取和转移机制来解决所生成的修复区域上的持续像差。我们的方法，V-LinkNet，开发了高层次的特征转移到深层次的纹理背景的修补区域，我们的工作，提出了一种新的技术相结合的编码器学习通过递归残差过渡层（RSTL）。RSTL层通过直接通信增加唯一的语义信息，很容易适应双编码器。通过将双编码器结构与上下文特征表示损失函数相结合，我们的系统获得了使用高级特征进行修补的能力。为了减少随机mask-image配对的偏差，我们在CelebA-HQ，Paris Street View和Places 2数据集的测试集上引入了一个标准的mask-image配对协议。我们的结果表明，V-LinkNet使用此标准协议在CelebA-HQ和巴黎街景上表现更好。在接受本文后，我们将与研究界分享标准协议和我们的代码。
FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazing
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Adobe Inc.（奥多比公司）
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation
Université de technologie de Compiègne（贡比涅技术大学）；CNRS（法国国家科学研究中心）；Université de Rouen Normandie（鲁昂诺曼底大学）；Centre Henri Becquerel（亨利·贝克勒尔中心）；Institut universitaire de France（法国大学研究院）
Enhancing Visual In-Context Learning by Multi-Faceted Fusion
College of Intelligent Robotics and Advance Manufacturing, Fudan University（智能机器人与先进制造学院，复旦大学）；School of Microelectronics, Fudan University（微电子学院，复旦大学）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；School of Information Science and Engineering, East China University of Science and Technology（信息科学与工程学院，东华大学）
Human Pose Estimation with Spatial Contextual Information
Baidu Research, Baidu Inc.（百度研究部，百度公司）；Hong Kong University of Science and Technology（香港科技大学）；YouTu Lab, Tencent（优图实验室，腾讯）；The Chinese University of Hong Kong（香港中文大学）；University of Kentucky（肯塔基大学）
Context Patch Fusion With Class Token Enhancement for Weakly Supervised Semantic Segmentation
School of Cyber Science and Engineering, Wuxi University（网络安全科学与工程学院，无锡大学）；School of Informatics, Xiamen University（信息学院，厦门大学）；School of Computer Science, University of Liverpool（计算机科学学院，利物浦大学）
Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot Learning
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Engineering, Newcastle University（纽卡斯尔大学工程学院）；Department of Computer Science, Durham University（杜伦大学计算机科学系）；Harbin Institute of Technology（哈尔滨工业大学）；School of Artificial Intelligence, Nanjing University of Science and Technology（南京理工大学人工智能学院）
Disrupting Vision-Language Model-Driven Navigation Services via Adversarial Object Fusion
College of Computer Science, Chongqing University（重庆大学计算机学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）
