{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":26,"n_cols":5,"rows_kept":26,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[13,2,13,3],[14,2,14,3],[16,2,16,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISA996635665503316"]
[null,null,"Autore",null,"Antonacopoulos Apostolos"]
[null,null,"Titolo",null,"Pattern Recognition : 27th International Conference, ICPR 2024, Kolkata, India, December 1–5, 2024, Proceedings, Part IX / / edited by Apostolos Antonacopoulos, Subhasis Chaudhuri, Rama Chellappa, Cheng-Lin Liu, Saumik Bhattacharya, Umapada Pal"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"3-031-78189-9"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (509 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 15309"]
[null,null,"Disciplina",null,"006.37"]
[null,null,"Soggetti",null,"Computer vision"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Computer Vision"]
[{"$nulls":4},"Machine Learning"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- President's Address -- Preface -- Organization -- Contents - Part IX -- Mask and Compress: Efficient Skeleton-Based Action Recognition in Continual Learning -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Preliminaries -- 3.2 CHARON -- 4 Experimental Analysis -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Results -- 4.4 Ablations -- 5 Conclusions -- References -- Text-Driven Prototype Learning for Few-Shot Class-Incremental Learning -- 1 Introduction -- 2 Related Works -- 3 Method -- 4 CIFAR100-Text and MiniImageNet-Text Datasets -- 5 Experiments -- 5.1 Datasets and Settings -- 5.2 Text-Driven Prototype Analysis -- 6 Discussions -- 7 Conclusion -- References -- Dual Supervised Contrastive Learning Based on Perturbation Uncertainty for Online Class Incremental Learning -- 1 Introduction -- 2 Related Work -- 2.1 Continual Learning -- 2.2 Contrastive Learning -- 3 Method -- 3.1 Problem Definition -- 3.2 Method Overview -- 3.3 Perturbation Uncertainty Based Memory Retrieval -- 3.4 Supervised Contrastive Learning -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Performance Comparison -- 4.3 Ablation Study -- 5 Conclusion -- References -- Breaking Information Silos: Global Guided Task Prediction for Class-Incremental Learning -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Problem Setting and Method Overview -- 3.2 Local De-Redundant Module -- 3.3 Global "]
[{"$nulls":4},"Information Module -- 3.4 Attention Module -- 3.5 Optimizing and Lightweight Model -- 4 Experiments -- 4.1 Experiment Setup and Implementation Details -- 4.2 Results and Discussion -- 4.3 Ablation Study and Analysis -- 5 Conclusion -- References -- Conditioned Prompt-Optimization for Continual Deepfake Detection -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Problem Formulation -- 3.2 Prompt Tuning -- 4 Prompt2Guard -- 4.1 Text-Prompt Conditioning."]
[{"$nulls":4},"4.2 Continual Read-Only Prompts -- 4.3 Predictions Ensembling -- 5 Experiments -- 5.1 Comparative Results -- 5.2 Ablations -- 6 Conclusions -- References -- Plasticity Driven Knowledge Transfer for Continual Deep Reinforcement Learning in Financial Trading -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Continual Learning Approach -- 3.2 Temporal Focused Sampling Experience Replay -- 3.3 Knowledge Transfer Methodology -- 4 Experimental Evaluation -- 4.1 Dataset and Feature Extraction -- 4.2 Model Architecture -- 4.3 Results -- 5 Conclusions -- References -- Orthogonal Latent Compression for Streaming Anomaly Detection in Industrial Vision -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Overview -- 3.2 Orthogonal Latent Compression -- 3.3 Loss Function -- 3.4 Abnormal Scores -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Evaluation on Streaming AD -- 4.3 Evaluation on Offline AD -- 4.4 Ablation Analysis -- 5 Conclusion -- References -- Out-of-Distribution Forgetting: Vulnerability of Continual Learning to Intra-class Distribution Shift -- 1 Introduction -- 2 Related Works -- 2.1 Continual Learning -- 2.2 Security Concerns of Neural Networks -- 2.3 Several Concerns of Continual Learning -- 3 Out-of-Distribution Forgetting -- 3.1 Standard CL Paradigm -- 3.2 OODF Paradigm -- 3.3 Introducing of the Distribution Shift -- 4 Experiment Settings -- 5 Properties of OODF -- 5.1 Delayed Effect -- 5.2 Targeting -- 5.3 Continual Detrimental -- 6 Analysis -- 6.1 Occlusion Strength -- 6.2 Various Conditions of Shift -- 6.3 Shift Position in the Learning Sequence -- 6.4 Different Percentage r and Strength -- 6.5 Mechanism of OODF -- 6.6 Proposal for Improving OODF -- 7 Conclusion -- References -- Generating Multi-objective Fronts from Streamed Data Using Nested List -- 1 Introduction -- 1.1 Prerequisites -- 1.2 Literature Survey."]
[{"$nulls":4},"1.3 Gap Identification and Motivation -- 1.4 Salient Points of the Proposed Approach -- 1.5 Organization of the Paper -- 2 A Nested List Structure for Non-dominated Sorting of Streamed Data Elements -- 2.1 Benchmark Data-Set Specification -- 3 Result and Discussion -- 3.1 Complexity Analysis -- 3.2 Correctness and Completeness -- 4 Conclusion -- References -- Mapping the Unknown: A New Approach to Open-World Video Recognition -- 1 Introduction -- 2 Related Work -- 3 Dynamic Ensembles for OWR -- 3.1 Ensemble Decision Module -- 3.2 Generation Ensemble Module -- 3.3 Update and Limit Module -- 4 Experiments Preliminary -- 4.1 Experiment Dataset Configuration -- 4.2 Experimental Setup -- 5 Experiment Results -- 5.1 Comparison Against State-of-the-Art Face Recognition in OWR -- 5.2 Sensitivity About Parameters -- 6 Conclusions -- References -- ESL: Explain to Improve Streaming Learning for Transformers -- 1 Introduction -- 2 Related Work -- 3 Proposed Framework: ESL -- 3.1 XAI Method: Rollout Feature Explanation Method (RFEM) -- 3.2 Input Patch Selection -- 3.3 Streaming Learner: Entropy-Based Move-To-Data (EMTD) and Retargeting (EMTDR) -- 4 Experiments and Results -- 4.1 Experimental Details -- 4.2 Results -- 4.3 Ablation Study -- 5 Conclusion and Future Work -- References -- Detection of Unknown Errors in Human-Centered Systems -- 1 Introduction -- 1.1 Contributions -- 1.2 Paper "]
[{"$nulls":4},"Organization -- 2 Preliminaries -- 2.1 Signal Temporal Logic -- 2.2 Physics-Driven Surrogate Model -- 3 Coefficient Mining from Trajectory -- 3.1 Dynamics Induced RNN -- 3.2 Forward Pass in DiH-RNN -- 3.3 Backpropagation to Learn Coefficients -- 4 Conformal Inference -- 5 Case Studies -- 5.1 Automated Insulin Delivery System Example -- 5.2 Aircraft Example -- 5.3 Autonomous Driving Example -- 6 Evaluation Method and Metrics -- 6.1 Unknown-Unknown Scenario Simulation."]
[{"$nulls":4},"6.2 Baseline Strategy -- 7 Results -- 7.1 Automated Insulin Delivery System Example -- 7.2 Aircraft Example -- 7.3 Autonomous Driving Example -- 8 Future Works -- 9 Conclusions -- References -- Source-Free Test-Time Adaptation For Online Surface-Defect Detection -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Supervisor -- 3.2 Augmented Mean Prediction -- 3.3 Dynamically-Balancing Loss -- 3.4 Model Update Pipeline -- 4 Experiments -- 4.1 Datasets and Pre-training -- 4.2 Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- Alleviating Catastrophic Forgetting in Facial Expression Recognition with Emotion-Centered Models -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Emotion-Centered Generative Replay -- 3.2 General Pipeline -- 4 Experiments -- 4.1 Results -- 5 Discussion -- 6 Conclusion -- A Appendix -- A.1 Evaluation of the MNIST Dataset -- References -- Satellite State Prediction and Maneuver Detection Analysis Using NCDEs -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Dataset -- 3.2 Data Preprocessing Step -- 3.3 SSPMDA Architecture -- 4 Empirical Evaluations -- 4.1 Experimental Results -- 4.2 Ablation Study -- 5 Visualization -- 6 Conclusion -- References -- MIXAD: Memory-Induced Explainable Time Series Anomaly Detection -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Problem Formulation -- 3.2 Spatiotemporal Recurrent Convolution Unit -- 3.3 Memory-Augmented Graph Structure Learning -- 3.4 Memory-Induced Explainable Anomaly Detection (MIXAD) -- 3.5 Anomaly Scoring -- 3.6 Anomaly Interpretation -- 4 Experiments and Analysis -- 4.1 Datasets and Baselines -- 4.2 Evaluation Metrics -- 4.3 Performance Comparisons -- 4.4 Ablation Study -- 4.5 Visualization of Node Embeddings -- 4.6 Visualization of Anomaly Scores -- 5 Case Study: Exathlon Dataset and Testbed -- 6 Conclusion -- References."]
[{"$nulls":4},"Rough Set Theoretic Approach for Solving the Multi-Armed Bandit Problems -- 1 Introduction -- 2 Applying Rough Set Concepts to Stochastic Multi-Armed Bandits -- 3 Proposed Methodology -- 4 Experimental Results -- 4.1 Bandit Problem -- 4.2 Advertising Problem -- 4.3 Election Campaign Problem -- 4.4 Ablation Study -- 4.5 Discussions -- 5 Conclusions and Future Work -- References -- Hybrid Graph Representation Learning: Integrating Euclidean and Hyperbolic Space -- 1 Introduction -- 2 Related Work -- 2.1 Representation Learning -- 2.2 Hyperbolic Representation Learning -- 3 Preliminaries -- 3.1 Poincaré Ball Model -- 3.2 Gyrovector Spaces -- 4 Method -- 4.1 Hyperbolic Encoder -- 4.2 Euclidean Loss -- 4.3 Hyperbolic Hierarchy Loss -- 4.4 Hyperbolic Uniformity Loss -- 4.5 Total Loss -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Node Classification and Clustering -- 5.3 Visualization -- 5.4 Ablation Study -- 6 Conclusion -- References -- Learning Object Focused Attention -- 1 Introduction -- 2 Object Focused Attention -- 3 Self-supervised Option with MAE -- 4 Adjacency Regularization -- 5 Related Work -- 5.1 Transformers and Self-Attention -- 5.2 Holistic Shape Representation -- 5.3 Multi-label Classification -- 6 Experimental Evaluation -- 6.1 Multi-label Classification on MS-COCO and Pascal Voc2012 -- 6.2 Out-of-Distribution Background Corruption with Stable Diffusion -- 6.3 Learning Shape Representations over Textures -- 7 Discussion and "]
[{"$nulls":4},"Future Work -- References -- Stereographic Projection for Embedding Hierarchical Structures in Hyperbolic Space -- 1 Introduction -- 2 Background -- 2.1 Hyperbolic Neural Networks -- 2.2 Topic Model -- 3 Stereographic Projection Transition Mapping -- 3.1 Limitations of Exponential Mapping for Hierarchical Embeddings -- 3.2 Method: Stereographic Projection Transition Mapping -- 3.3 Optimization Algorithm for SPTM."]
[{"$nulls":4},"4 Experiment."]
[null,null,null,"Sommario/riassunto","The multi-volume set of LNCS books with volume numbers 15301-15333 constitutes the refereed proceedings of the 27th International Conference on Pattern Recognition, ICPR 2024, held in Kolkata, India, during December 1–5, 2024. The 963 papers presented in these proceedings were carefully reviewed and selected from a total of 2106 submissions. They deal with topics such as Pattern Recognition; Artificial Intelligence; Machine Learning; Computer Vision; Robot Vision; Machine Vision; Image Processing; Speech Processing; Signal Processing; Video Processing; Biometrics; Human-Computer Interaction (HCI); Document Analysis; Document Recognition; Biomedical Imaging; Bioinformatics."]