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27th ICPR 2024: Kolkata, India - Part X
- Apostolos Antonacopoulos
, Subhasis Chaudhuri
Pattern Recognition - 27th International Conference, ICPR 2024, Kolkata, India, December 1-5, 2024, Proceedings, Part X. Lecture Notes in Computer Science 15310, Springer 2025, ISBN 978-3-031-78191-9 - Ethan Payne
Explaining Model Parameters Using the Product Space. 1-15 - Siladittya Manna
Correlation Weighted Prototype-Based Self-supervised One-Shot Segmentation of Medical Images. 16-33 - R. Kavitha
Quantitative Structure-Activity Relationship Modeling for the Prediction of Fish Toxicity Lethal Concentration on Fathead Minnow. 34-46 - Blaz Rolih, Matic Fucka, Danijel Skocaj:
SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection. 47-65 - Nilah Ravi Nair
Augmentation of Human Activity Data: Convert, Generate, Transform. 66-81 - Azusa Sawada, Takashi Shibata, Keiko Yokoyama, Shoji Yachida, Toshinori Hosoi:
PostAugment: Adversarial Data Augmentation with Hard Sample Suppression by Incorrect Class Likelihood. 82-97 - Vijay John, Yasutomo Kawanishi:
Generating Pseudo-Strong Labels from Weak Labels for Distributed Multi-Microphone Sound Event Detection. 98-113 - Owais Iqbal, Omprakash Chakraborty, Aftab Hussain, Rameswar Panda, Abir Das:
SITAR: Semi-supervised Image Transformer for Action Recognition. 114-130 - Xiaotian Zhuang, Yongyu Wang, Shiqi Hao, Xiaoyang Wang:
Enhancing Graph Topology and Clustering Quality: A Modularity-Guided Approach. 131-142 - Haruki Konii, Teppei Nakano, Yasumasa Miyazawa, Tetsuji Ogawa:
Leveraging Data from Vast Unexplored Seas: Positive Unlabeled Learning for Refining Prediction Area in Good Fishing Ground Prediction. 143-158 - Dominik Hirner, Friedrich Fraundorfer:
SAda-Net: A Self-supervised Adaptive Stereo Estimation CNN For Remote Sensing Image Data. 159-175 - Naihao Wang, Shushi Hong, Kexin Meng, Ruirui Li:
Enhancing the Quality of Pseudo Labels in 2D Human Pose Estimation via a Debiasing-Teacher Approach. 176-192 - Sonal Kumar
GraPix: Exploring Graph Modularity Optimization for Unsupervised Pixel Clustering. 193-209 - Rohit Venkata Sai Dulam, Chandra Kambhamettu:
SODAWideNet++: Combining Attention and Convolutions for Salient Object Detection. 210-226 - Ishu Priya, C. Krishna Mohan
FL-PSeC: Federated Learning-Pseudo Labeled Medical Image Segmentation with Personalized Class Balancing Semi-supervised Approach. 227-241 - Reshma Rastogi
Improved Hypergraph Laplacian Based Semi-supervised Support Vector Machine. 242-255 - Xiaoheng Jiang, Penghui Xiao, Feng Yan, Yang Lu, Shaohui Jin, Mingliang Xu:
Context Mutual Evolution Network for Weakly Supervised Surface Defect Detection. 256-270 - Reshma Rastogi
Binary-Tree Based Mean-Averaging Estimation for Multi-label Classification. 271-285 - Trung Hieu Vu, Eyad Elyan, Will Vorley, Joe Goodlad, Truong Dang, Tien Thanh Nguyen:
FAT: Fusion-Attention Transformer for Remaining Useful Life Prediction. 286-301 - Parashjyoti Borah, Aparajita Dutta:
Conditional Probability-Based Feature Embedding for Genomic Sequence Data. 302-314 - Avinash Chouhan
RADA: Reconstruction Assisted Domain Adaptation for Nighttime Aerial Tracking. 315-330 - Haoyu Zhai, Zexuan Fei, Yan Ma:
A Novel Outlier Detection Algorithm Based on Symmetry and Distance Ratio. 331-344 - Francesc Serratosa
Graph Regression Based on Autoencoders and Graph Autoencoders. 345-360 - Francesco Leonardi, Kaspar Riesen
Dissimilarity-Based Graph Embedding: An Efficient GAT-based Approach. 361-374 - Yutong Jiang, Xing Wu, Jianjia Wang:
Quantifying Racial Segregation Through Continuous-Time Quantum Walks. 375-389 - Nokyung Park, Daewon Chae, Jeongyong Shim, Sangpil Kim, Eun-Sol Kim, Jinkyu Kim:
Clustering-based Image-Text Graph Matching for Domain Generalization. 390-406 - Yiren Jian, Chongyang Gao, Chen Zeng, Yunjie Zhao, Soroush Vosoughi:
Knowledge from Large-Scale Protein Contact Prediction Models Can Be Transferred to the Data-Scarce RNA Contact Prediction Task. 407-423 - Samyak Rawlekar, Shubhang Bhatnagar, Vishnuvardhan Pogunulu Srinivasulu, Narendra Ahuja:
Improving Multi-label Recognition using Class Co-Occurrence Probabilities. 424-439
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