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Class Incremental Learning

Papers

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Showing 201–225 of 634 papers

TitleStatusHype
Learn by Reasoning: Analogical Weight Generation for Few-Shot Class-Incremental Learning—0
Continual learning via probabilistic exchangeable sequence modelling—0
IAP: Improving Continual Learning of Vision-Language Models via Instance-Aware PromptingCode0
Adaptive Weighted Parameter Fusion with CLIP for Class-Incremental Learning—0
Knowledge Graph Enhanced Generative Multi-modal Models for Class-Incremental Learning—0
Feature Calibration enhanced Parameter Synthesis for CLIP-based Class-incremental Learning—0
Technical Report for the 5th CLVision Challenge at CVPR: Addressing the Class-Incremental with Repetition using Unlabeled Data -- 4th Place SolutionCode0
Robust3D-CIL: Robust Class-Incremental Learning for 3D Perception—0
An experimental approach on Few Shot Class Incremental Learning—0
Generative Binary Memory: Pseudo-Replay Class-Incremental Learning on Binarized Embeddings—0
A New Benchmark for Few-Shot Class-Incremental Learning: Redefining the Upper Bound—0
Singular Value Fine-tuning for Few-Shot Class-Incremental Learning—0
PTMs-TSCIL Pre-Trained Models Based Class-Incremental Learning—0
Towards Experience Replay for Class-Incremental Learning in Fully-Binary Networks—0
Teach YOLO to Remember: A Self-Distillation Approach for Continual Object Detection—0
FSCIL-SEI: Few-Shot Class-Incremental Learning Approach for Specific Emitter Identification—0
Class-Independent Increment: An Efficient Approach for Multi-label Class-Incremental Learning—0
Brain-inspired analogical mixture prototypes for few-shot class-incremental learning—0
Sculpting [CLS] Features for Pre-Trained Model-Based Class-Incremental Learning—0
ConSense: Continually Sensing Human Activity with WiFi via Growing and PickingCode0
Latest Advancements Towards Catastrophic Forgetting under Data Scarcity: A Comprehensive Survey on Few-Shot Class Incremental Learning—0
Memory Is Not the Bottleneck: Cost-Efficient Continual Learning via Weight Space Consolidation—0
Leveraging Pre-Trained Models for Multimodal Class-Incremental Learning under Adaptive Fusion—0
IPSeg: Image Posterior Mitigates Semantic Drift in Class-Incremental SegmentationCode0
Controllable Forgetting Mechanism for Few-Shot Class-Incremental Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1S&B10-stage average accuracy68.18—Unverified
2SCR10-stage average accuracy65.98—Unverified
3iCaRL10-stage average accuracy63.24—Unverified
4LUCIR10-stage average accuracy56.53—Unverified
5ABD10-stage average accuracy54.44—Unverified
6EWC10-stage average accuracy50.53—Unverified
7EMR10-stage average accuracy48.66—Unverified
8A-GEM10-stage average accuracy45.76—Unverified
#ModelMetricClaimedVerifiedStatus
1PPCA-SWSLFinal Accuracy77.07—Unverified
2PPCA-CLIPFinal Accuracy69.71—Unverified
#ModelMetricClaimedVerifiedStatus
1PPCA-SWSLFinal Accuracy77.07—Unverified
2PPCA-CLIPFinal Accuracy69.71—Unverified
#ModelMetricClaimedVerifiedStatus
1SEEDAverage Incremental Accuracy61.7—Unverified
#ModelMetricClaimedVerifiedStatus
1SEEDAverage Incremental Accuracy56.2—Unverified
#ModelMetricClaimedVerifiedStatus
1SEEDAverage Incremental Accuracy42.6—Unverified