We propose a novel cost aggregation network, called Cost Aggregation Transformers (CATs), to find dense correspondences between semantically similar images with additional challenges posed by large intra-class appearance and geometric variations. Cost aggregation is a highly important process in matching tasks, which the matching accuracy depends on the quality of its output. Compared to hand-crafted or CNN-based methods addressing the cost aggregation, in that either lacks robustness to severe deformations or inherit the limitation of CNNs that fail to discriminate incorrect matches due to limited receptive fields, CATs explore global consensus among initial correlation map with the help of some architectural designs that allow us to fully leverage self-attention mechanism. Specifically, we include appearance affinity modeling to aid the cost aggregation process in order to disambiguate the noisy initial correlation maps and propose multi-level aggregation to efficiently capture different semantics from hierarchical feature representations. We then combine with swapping self-attention technique and residual connections not only to enforce consistent matching but also to ease the learning process, which we find that these result in an apparent performance boost. We conduct experiments to demonstrate the effectiveness of the proposed model over the latest methods and provide extensive ablation studies. Project page is available at : https://sunghwanhong.github.io/CATs/.
Tsinghua University（清华大学）；National University of Singapore（新加坡国立大学）；Advance Digital Science Center（先进数字科学中心）；University of Texas at San Antonio（得克萨斯大学圣安东尼奥分校）
GVC-Seg: Training-Free 3D Instance Segmentation via Geometric Visual Correspondence
Victoria University of Wellington（惠灵顿维多利亚大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Southern University of Science and Technology（南方科技大学）
Region Matters: Efficient and Reliable Region-Aware Visual Place Recognition
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology（教育部计算功率网络与信息安全部，山东计算机科学中心，齐鲁大学技术学院）；Spatialtemporal AI（时空AI）；School of Architecture and Urban Planning, Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen University（建筑与城市规划学院，粤港澳大湾区智慧城市联合实验室，深圳大学）
