29 citations · 75 across the 10 of their papers we have counts for
6 papers · 1 filter
Learning Bottleneck Concepts in Image Classification
Bowen Wang, Liangzhi Li, Yuta Nakashima +1
Interpreting and explaining the behavior of deep neural networks is critical for many tasks. Explainable AI provides a way to address this challenge, mostly by providing per-pixel…
The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods
Vivek Singh Bawa, Gurkirt Singh, Francis KapingA +16
For an autonomous robotic system, monitoring surgeon actions and assisting the main surgeon during a procedure can be very challenging. The challenges come from the peculiar struct…
Match Them Up: Visually Explainable Few-shot Image Classification
Bowen Wang, Liangzhi Li, Manisha Verma +3
Few-shot learning (FSL) approaches are usually based on an assumption that the pre-trained knowledge can be obtained from base (seen) categories and can be well transferred to nove…
Noisy-LSTM: Improving Temporal Awareness for Video Semantic Segmentation
Bowen Wang, Liangzhi Li, Yuta Nakashima +3
Semantic video segmentation is a key challenge for various applications. This paper presents a new model named Noisy-LSTM, which is trainable in an end-to-end manner, with convolut…
SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition
Liangzhi Li, Bowen Wang, Manisha Verma +3
Explainable artificial intelligence has been gaining attention in the past few years. However, most existing methods are based on gradients or intermediate features, which are not…
Joint Learning of Vessel Segmentation and Artery/Vein Classification with Post-processing
Liangzhi Li, Manisha Verma, Yuta Nakashima +2
Retinal imaging serves as a valuable tool for diagnosis of various diseases. However, reading retinal images is a difficult and time-consuming task even for experienced specialists…