9 papers · 1 filter
CAST: Channel-Aware Spatial Transfer Learning with Pseudo-Image Radar for Sign Language Recognition
Md. Shakhoyat Rahman Shujon, Sheikh Md. Galib Mahim, Md. Milon Islam +4
We propose CAST, a dual-stream architecture that utilizes channel-aware spatial transfer learning for isolated sign language recognition addressing the challenges of magnitude-only…
Projected Gradient Unlearning for Text-to-Image Diffusion Models: Defending Against Concept Revival Attacks
Aljalila Aladawi, Mohammed Talha Alam, Fakhri Karray
Machine unlearning for text-to-image diffusion models aims to selectively remove undesirable concepts from pre-trained models without costly retraining. Current unlearning methods…
A Signer-Invariant Conformer and Multi-Scale Fusion Transformer for Continuous Sign Language Recognition
Md Rezwanul Haque, Md. Milon Islam, S M Taslim Uddin Raju +1
Continuous Sign Language Recognition (CSLR) faces multiple challenges, including significant inter-signer variability and poor generalization to novel sentence structures. Traditio…
FusionEnsemble-Net: An Attention-Based Ensemble of Spatiotemporal Networks for Multimodal Sign Language Recognition
Md. Milon Islam, Md Rezwanul Haque, S M Taslim Uddin Raju +1
Accurate recognition of sign language in healthcare communication poses a significant challenge, requiring frameworks that can accurately interpret complex multimodal gestures. To…
MDD-Net: Multimodal Depression Detection through Mutual Transformer
Md Rezwanul Haque, Md. Milon Islam, S M Taslim Uddin Raju +3
Depression is a major mental health condition that severely impacts the emotional and physical well-being of individuals. The simple nature of data collection from social media pla…
MMFformer: Multimodal Fusion Transformer Network for Depression Detection
Md Rezwanul Haque, Md. Milon Islam, S M Taslim Uddin Raju +3
Depression is a serious mental health illness that significantly affects an individual's well-being and quality of life, making early detection crucial for adequate care and treatm…