https://github.com/bic-l/burst-ann2snn
공식 코드Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks
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Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks
Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Training
Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection
Unifying Appearance Codes and Bilateral Grids for Driving Scene Gaussian Splatting
Robust Contrastive Language-Image Pre-training against Data Poisoning and Backdoor Attacks
Object Instance Mining for Weakly Supervised Object Detection
FakeFlow: Fake News Detection by Modeling the Flow of Affective Information
Multiclass Confidence and Localization Calibration for Object Detection
End-to-End $n$-ary Relation Extraction for Combination Drug Therapies
Patch-wise Contrastive Style Learning for Instagram Filter Removal
Deterministic Neural Illumination Mapping for Efficient Auto-White Balance Correction
Improving Transferability for Domain Adaptive Detection Transformers
Language-Driven Active Learning for Diverse Open-Set 3D Object Detection
Extending Neural Keyword Extraction with TF-IDF tagset matching
SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection
NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge
Bucketed Ranking-based Losses for Efficient Training of Object Detectors
Multilingual Open Text Release 1: Public Domain News in 44 Languages
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.