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ORCID17th ECCV 2022: Tel Aviv, Israel - Volume 20
- Shai Avidan, Gabriel J. Brostow
, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner:
Computer Vision - ECCV 2022 - 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XX. Lecture Notes in Computer Science 13680, Springer 2022, ISBN 978-3-031-20043-4 - Jinxiang Lai, Siqian Yang, Wenlong Liu, Yi Zeng, Zhongyi Huang, Wenlong Wu, Jun Liu, Bin-Bin Gao, Chengjie Wang:
tSF: Transformer-Based Semantic Filter for Few-Shot Learning. 1-19 - Yanxu Hu, Andy J. Ma:
Adversarial Feature Augmentation for Cross-domain Few-Shot Classification. 20-37 - Yue Xu, Yong-Lu Li, Jiefeng Li, Cewu Lu:
Constructing Balance from Imbalance for Long-Tailed Image Recognition. 38-56 - Yuzhe Yang, Hao Wang, Dina Katabi:
On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond. 57-75 - Qi Fan, Chi-Keung Tang, Yu-Wing Tai:
Few-Shot Video Object Detection. 76-98 - Minghao Fu, Yun-Hao Cao, Jianxin Wu:
Worst Case Matters for Few-Shot Recognition. 99-115 - Kai Yi, Xiaoqian Shen, Yunhao Gou, Mohamed Elhoseiny:
Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image Classification. 116-132 - Zhitong Xiong, Haopeng Li, Xiao Xiang Zhu:
Doubly Deformable Aggregation of Covariance Matrices for Few-Shot Segmentation. 133-150 - Xinyu Shi, Dong Wei, Yu Zhang, Donghuan Lu, Munan Ning, Jiashun Chen, Kai Ma, Yefeng Zheng:
Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation. 151-168 - Xingping Dong, Jianbing Shen, Ling Shao:
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning. 169-186 - Shreyank N. Gowda, Laura Sevilla-Lara, Frank Keller, Marcus Rohrbach:
CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition. 187-203 - Townim F. Chowdhury, Ali Cheraghian, Sameera Ramasinghe, Sahar Ahmadi, Morteza Saberi, Shafin Rahman:
Few-Shot Class-Incremental Learning for 3D Point Cloud Objects. 204-220 - Zhenyi Wang, Li Shen, Le Fang, Qiuling Suo, Donglin Zhan, Tiehang Duan, Mingchen Gao:
Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions. 221-238 - Ziyu Jiang, Tianlong Chen, Xuxi Chen, Yu Cheng, Luowei Zhou, Lu Yuan, Ahmed Awadallah, Zhangyang Wang:
DnA: Improving Few-Shot Transfer Learning with Low-Rank Decomposition and Alignment. 239-256 - Rongkai Ma, Pengfei Fang, Gil Avraham, Yan Zuo, Tianyu Zhu, Tom Drummond, Mehrtash Harandi:
Learning Instance and Task-Aware Dynamic Kernels for Few-Shot Learning. 257-274 - Quande Liu, Youpeng Wen, Jianhua Han, Chunjing Xu, Hang Xu, Xiaodan Liang:
Open-World Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding. 275-292 - Zhanyuan Yang, Jinghua Wang, Yingying Zhu:
Few-Shot Classification with Contrastive Learning. 293-309 - Shan Zhang, Naila Murray, Lei Wang, Piotr Koniusz:
Time-rEversed DiffusioN tEnsor Transformer: A New TENET of Few-Shot Object Detection. 310-328 - Bowen Dong, Pan Zhou, Shuicheng Yan, Wangmeng Zuo:
Self-Promoted Supervision for Few-Shot Transformer. 329-347 - Thanh Nguyen, Chau Pham, Khoi Nguyen, Minh Hoai:
Few-Shot Object Counting and Detection. 348-365 - Kibok Lee, Hao Yang, Satyaki Chakraborty, Zhaowei Cai, Gurumurthy Swaminathan, Avinash Ravichandran, Onkar Dabeer:
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark. 366-382 - Wentao Chen, Zhang Zhang, Wei Wang, Liang Wang, Zilei Wang, Tieniu Tan:
Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations. 383-399 - TianXue Ma, Mingwei Bi, Jian Zhang, Wang Yuan, Zhizhong Zhang, Yuan Xie, Shouhong Ding, Lizhuang Ma:
Mutually Reinforcing Structure with Proposal Contrastive Consistency for Few-Shot Object Detection. 400-416 - Huisi Wu, Fangyan Xiao, Chongxin Liang:
Dual Contrastive Learning with Anatomical Auxiliary Supervision for Few-Shot Medical Image Segmentation. 417-434 - Quentin Bouniot, Ievgen Redko, Romaric Audigier, Angélique Loesch, Amaury Habrard:
Improving Few-Shot Learning Through Multi-task Representation Learning Theory. 435-452 - Min Zhang, Siteng Huang, Wenbin Li, Donglin Wang:
Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation. 453-470 - Khoi D. Nguyen, Quoc-Huy Tran, Khoi Nguyen, Binh-Son Hua, Rang Nguyen:
Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments. 471-487 - Otniel-Bogdan Mercea, Thomas Hummel, A. Sophia Koepke, Zeynep Akata:
Temporal and Cross-modal Attention for Audio-Visual Zero-Shot Learning. 488-505 - Seonghyeon Moon, Samuel S. Sohn, Honglu Zhou, Sejong Yoon, Vladimir Pavlovic, Muhammad Haris Khan, Mubbasir Kapadia:
HM: Hybrid Masking for Few-Shot Segmentation. 506-523 - Haoquan Li, Laoming Zhang, Daoan Zhang, Lang Fu, Peng Yang, Jianguo Zhang:
TransVLAD: Focusing on Locally Aggregated Descriptors for Few-Shot Learning. 524-540 - Tao Zhang, Wu Huang:
Kernel Relative-prototype Spectral Filtering for Few-Shot Learning. 541-557 - Niv Cohen, Rinon Gal, Eli A. Meirom, Gal Chechik, Yuval Atzmon:
"This Is My Unicorn, Fluffy": Personalizing Frozen Vision-Language Representations. 558-577 - Zixuan Zhou, Xuefei Ning, Yi Cai, Jiashu Han, Yiping Deng, Yuhan Dong, Huazhong Yang, Yu Wang:
CLOSE: Curriculum Learning on the Sharing Extent Towards Better One-Shot NAS. 578-594 - Junwoo Cho, Seungtae Nam, Daniel Rho, Jong Hwan Ko, Eunbyung Park:
Streamable Neural Fields. 595-612 - Julia Hornauer, Vasileios Belagiannis:
Gradient-Based Uncertainty for Monocular Depth Estimation. 613-630 - Zhen Wang, Liu Liu, Yajing Kong, Jiaxian Guo, Dacheng Tao:
Online Continual Learning with Contrastive Vision Transformer. 631-650 - Taeho Kim, Yongin Kwon, Jemin Lee, Taeho Kim, Sangtae Ha:
CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution. 651-667 - Xiaoxing Wang, Jiale Lin, Juanping Zhao, Xiaokang Yang, Junchi Yan:
EAutoDet: Efficient Architecture Search for Object Detection. 668-684 - Chao Xue, Xiaoxing Wang, Junchi Yan, Chun-Guang Li:
A Max-Flow Based Approach for Neural Architecture Search. 685-701 - Robik Shrestha, Kushal Kafle, Christopher Kanan:
OccamNets: Mitigating Dataset Bias by Favoring Simpler Hypotheses. 702-721 - Martin Trimmel, Mihai Zanfir, Richard I. Hartley, Cristian Sminchisescu:
ERA: Enhanced Rational Activations. 722-738 - Cong Wang, Hongmin Xu, Xiong Zhang, Li Wang, Zhitong Zheng, Haifeng Liu:
Convolutional Embedding Makes Hierarchical Vision Transformer Stronger. 739-756
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