most citedTransferring Domain-Agnostic Knowledge in Video Question Answering

5 citations · 18 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV20221 cited

Contrastive Losses Are Natural Criteria for Unsupervised Video Summarization

Zongshang Pang, Yuta Nakashima, Mayu Otani +1

Video summarization aims to select the most informative subset of frames in a video to facilitate efficient video browsing. Unsupervised methods usually rely on heuristic training…

cs.RO2022

Deep Gesture Generation for Social Robots Using Type-Specific Libraries

Hitoshi Teshima, Naoki Wake, Diego Thomas +3

Body language such as conversational gesture is a powerful way to ease communication. Conversational gestures do not only make a speech more lively but also contain semantic meanin…

cs.CV2022

AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval

Riku Togashi, Mayu Otani, Yuta Nakashima +3

Evaluation measures have a crucial impact on the direction of research. Therefore, it is of utmost importance to develop appropriate and reliable evaluation measures for new applic…

cs.CV20222 cited

Quantifying Societal Bias Amplification in Image Captioning

Yusuke Hirota, Yuta Nakashima, Noa Garcia

We study societal bias amplification in image captioning. Image captioning models have been shown to perpetuate gender and racial biases, however, metrics to measure, quantify, and…

cs.CV2022

Optimal Correction Cost for Object Detection Evaluation

Mayu Otani, Riku Togashi, Yuta Nakashima +3

Mean Average Precision (mAP) is the primary evaluation measure for object detection. Although object detection has a broad range of applications, mAP evaluates detectors in terms o…

cs.CV20215 cited

Transferring Domain-Agnostic Knowledge in Video Question Answering

Tianran Wu, Noa Garcia, Mayu Otani +3

Video question answering (VideoQA) is designed to answer a given question based on a relevant video clip. The current available large-scale datasets have made it possible to formul…