Per-sample random augmentation plus unimodal fine-tuning of pretrained layers improves late-fusion multimodal classification on FPU23 fetal ultrasound (up to 96.9% head detection) and reaches 92.63% on UPMC Food-101, but the two recipe components are not ablated separately.
Dl-ids: Extracting features using cnn-lstm hybrid network for intrusion detection system,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Batch Augmentation with Unimodal Fine-tuning for Multimodal Learning
Per-sample random augmentation plus unimodal fine-tuning of pretrained layers improves late-fusion multimodal classification on FPU23 fetal ultrasound (up to 96.9% head detection) and reaches 92.63% on UPMC Food-101, but the two recipe components are not ablated separately.