2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.CV2023
Concept Bottleneck with Visual Concept Filtering for Explainable Medical Image Classification
Injae Kim, Jongha Kim, Joonmyung Choi +1
Interpretability is a crucial factor in building reliable models for various medical applications. Concept Bottleneck Models (CBMs) enable interpretable image classification by uti…
cs.CV2023★ 2 cited
MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models
Dohwan Ko, Joonmyung Choi, Hyeong Kyu Choi +3
Foundation models have shown outstanding performance and generalization capabilities across domains. Since most studies on foundation models mainly focus on the pretraining phase,…
cs.CV2023
Dynamic Structure Pruning for Compressing CNNs
Jun-Hyung Park, Yeachan Kim, Junho Kim +2
Structure pruning is an effective method to compress and accelerate neural networks. While filter and channel pruning are preferable to other structure pruning methods in terms of…