Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification
Australian Centre for Robotic Vision（澳大利亚机器人视觉中心）；Queensland University of Technology（昆士兰科技大学）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织Data61）；University of Queensland（昆士兰大学）；University of Adelaide（阿德莱德大学）
ZongYuan Ge, Chris McCool, Conrad Sanderson, Peng Wang, Lingqiao Liu, Ian Reid, Peter Corke
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International Conference on Digital Image Computing: Techniques and Applications, 2016
Fine-grained classification is a relatively new field that has concentrated on using information from a single image, while ignoring the enormous potential of using video data to improve classification. In this work we present the novel task of video-based fine-grained object classification, propose a corresponding new video dataset, and perform a systematic study of several recent deep convolutional neural network (DCNN) based approaches, which we specifically adapt to the task. We evaluate three-dimensional DCNNs, two-stream DCNNs, and bilinear DCNNs. Two forms of the two-stream approach are used, where spatial and temporal data from two independent DCNNs are fused either via early fusion (combination of the fully-connected layers) and late fusion (concatenation of the softmax outputs of the DCNNs). For bilinear DCNNs, information from the convolutional layers of the spatial and temporal DCNNs is combined via local co-occurrences. We then fuse the bilinear DCNN and early fusion of the two-stream approach to combine the spatial and temporal information at the local and global level (Spatio-Temporal Co-occurrence). Using the new and challenging video dataset of birds, classification performance is improved from 23.1% (using single images) to 41.1% when using the Spatio-Temporal Co-occurrence system. Incorporating automatically detected bounding box location further improves the classification accuracy to 53.6%.
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks
Australian Centre for Robotic Vision（澳大利亚机器人视觉中心）；Queensland University of Technology (QUT)（昆士兰科技大学）；University of Queensland（昆士兰大学）；NICTA（澳大利亚国家信息与通信技术研究院）
Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones
Nanjing University of Science and Technology（南京理工大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Technology Sydney（悉尼科技大学）
CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels
Shanghai Key Laboratory of Multidimensional Information Processing（上海市多维度信息处理重点实验室）；Key Laboratory of Advanced Theory and Application in Statistics and Data Science（统计与数据科学前沿理论及应用重点实验室）；East China Normal University（华东师范大学）
Exploiting Feature Diversity for Make-up Temporal Video Grounding
Tencent Youtu Lab（腾讯优图实验室）
本技术报告介绍了MTVG的第三个获奖解决方案，这是在ACM MM 2022的第四届情景中人（PIC）挑战赛中引入的一项新任务。MTVG的目的是在一个未修剪的视频的基础上的文本描述的时间边界的步骤。这项任务的最大挑战是化妆步骤的细粒度视频-文本语义。然而，目前的方法主要是使用基于动作的预训练模型提取视频特征。由于动作比化妆步骤更粗粒度，因此基于动作的特征不足以提供细粒度的线索。为了解决这个问题，我们建议通过利用功能diffusion实现细粒度表示。具体来说，我们提出了一系列的方法，从特征提取，网络优化，模型集成。因此，我们在MTVG比赛中获得了第三名。
Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding
Sun Yat-sen University（中山大学）；South China Agricultural University（华南农业大学）；University of Electronic Science and Technology of China（电子科技大学）；Guilin University of Electronic Technology（桂林电子科技大学）
CILP-FGDI: Exploiting Vision-Language Model for Generalizable Person Re-Identification
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education（教育部新一代人工智能技术及其交叉应用重点实验室（东南大学））
Channel Interaction Networks for Fine-Grained Image Categorization
Malong Technologies（码隆科技）；Shenzhen Malong Artificial Intelligence Research Center（深圳码隆人工智能研究中心）
细粒度图像分类由于类间的细微差异而具有挑战性，我们认为利用通道之间丰富的关系可以帮助捕捉这些差异，因为不同的通道对应不同的语义。在本文中，我们提出了一个通道交互网络（CIN），它模拟了图像内和图像之间的通道相互作用。对于单个图像，提出了自通道交互（SCI）模块来探索图像内的通道相关性。这允许模型从相关通道中学习互补特征，从而产生更强的细粒度特征。此外，给定一个图像对，我们引入了一个对比通道交互（CCI）模块，用一个度量学习框架来模拟跨样本通道交互，使CIN能够区分图像之间的细微视觉差异。我们的模型可以以端到端的方式进行有效的训练，而不需要多阶段的训练和测试。最后，在三个公开可用的基准上进行了全面的实验，其中所提出的方法始终优于最先进的方法，如DFL-CNN（Wang，Morariu和Davis 2018）和NTS（Yang et al. 2018）。
Data-free Knowledge Distillation for Fine-grained Visual Categorization
School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；School of Computer Science and Technology, Shandong University（山东大学计算机科学与技术学院）
FineParser: A Fine-grained Spatio-temporal Action Parser for Human-centric Action Quality Assessment
School of Intelligence Science and Technology, University of Science and Technology Beijing（北京科技大学智能科学与技术学院）；Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）
Fine-grained text-driven dual-human motion generation via dynamic hierarchical interaction
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China（虚拟现实技术与系统国家重点实验室，北京航空航天大学，北京，中国）；Department of Computer Science, University of Durham, UK（计算机科学系，达勒姆大学，英国）；Zhongguancun Laboratory, Beijing, China（中关村实验室，北京，中国）
CoMLP: Cooperatively-Gated MLPs for Fine-Grained Cross-Modal Information Fusion in Medical Image Segmentation
Institute of Translational Medicine, Shanghai Jiao Tong University（上海交通大学转化医学研究院）；Zhongguancun Academy（中关村学院）；Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究院）；School of Computer Science, the University of Sydney（悉尼大学计算机学院）；China United Network Communications Group Co., Ltd.（中国联合网络通信集团有限公司）
Foundation Model Insights and a Multi-Model Approach for Superior Fine-Grained One-shot Subset Selection
National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, School of Computer Science, Wuhan University, China（国家多媒体软件工程研究中心、人工智能研究院、计算机科学学院、武汉大学，中国）；Hubei Key Laboratory of Multimedia（湖北省多媒体关键实验室）；The University of Tokyo, Japan（东京大学，日本）；National Institute of Informatics, Japan（日本信息处理学会）；School of Computer Science（计算机科学学院）；Technology, Wuhan University of Science（科技，武汉科技大学）
Exploiting the Semantic Knowledge of Pre-trained Text-Encoders for Continual Learning
School of Computer Science and Engineering, Tianjin University of Technology（天津理工大学计算机科学与工程学院）；Computer Vision Center, Universitat Autonoma de Barcelona（巴塞罗那自治大学计算机视觉中心）；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学与技术学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院多模态人工智能系统国家重点实验室）；School of Artificial Intelligence, University of the Chinese Academy of Sciences（中国科学院大学人工智能学院）
