21 citations · 31 across the 3 of their papers we have counts for
6 papers · 1 filter
Cross-modal Representation Learning for Zero-shot Action Recognition
Chung-Ching Lin, Kevin Lin, Linjie Li +2
We present a cross-modal Transformer-based framework, which jointly encodes video data and text labels for zero-shot action recognition (ZSAR). Our model employs a conceptually new…
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition
Yue Meng, Rameswar Panda, Chung-Ching Lin +5
Temporal modelling is the key for efficient video action recognition. While understanding temporal information can improve recognition accuracy for dynamic actions, removing tempor…
VA-RED: Video Adaptive Redundancy Reduction
Bowen Pan, Rameswar Panda, Camilo Fosco +6
Performing inference on deep learning models for videos remains a challenge due to the large amount of computational resources required to achieve robust recognition. An inherent p…
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition
Yue Meng, Chung-Ching Lin, Rameswar Panda +5
Action recognition is an open and challenging problem in computer vision. While current state-of-the-art models offer excellent recognition results, their computational expense lim…
Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images
Noel C. F. Codella, Chung-Ching Lin, Allan Halpern +3
Automated dermoscopic image analysis has witnessed rapid growth in diagnostic performance. Yet adoption faces resistance, in part, because no evidence is provided to support decisi…
Distributed Bundle Adjustment
Karthikeyan Natesan Ramamurthy, Chung-Ching Lin, Aleksandr Aravkin +2
Most methods for Bundle Adjustment (BA) in computer vision are either centralized or operate incrementally. This leads to poor scaling and affects the quality of solution as the nu…