2 citations · 5 across the 21 of their papers we have counts for
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MV2GF: Multi-view Pedestrian Detection with a Visual Geometric Foundation Model
Taiga Yamane, Satoshi Suzuki, Ryo Masumura +4
Multi-View Pedestrian Detection (MVPD) aims to detect pedestrians in the form of a bird's eye view map from multi-view images. Recent MVPD methods adopt a unified framework that pr…
Difference Vector Equalization for Robust Fine-tuning of Vision-Language Models
Satoshi Suzuki, Shin'ya Yamaguchi, Shoichiro Takeda +7
Contrastive pre-trained vision-language models, such as CLIP, demonstrate strong generalization abilities in zero-shot classification by leveraging embeddings extracted from image…
Joint Modeling of Big Five and HEXACO for Multimodal Apparent Personality-trait Recognition
Ryo Masumura, Shota Orihashi, Mana Ihori +6
This paper proposes a joint modeling method of the Big Five, which has long been studied, and HEXACO, which has recently attracted attention in psychology, for automatically recogn…
MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection
Taiga Yamane, Satoshi Suzuki, Ryo Masumura +5
Multi-View Pedestrian Detection (MVPD) aims to detect pedestrians in the form of a bird's eye view (BEV) from multi-view images. In MVPD, end-to-end trainable deep learning methods…
Utilizing Resource-Rich Language Datasets for End-to-End Scene Text Recognition in Resource-Poor Languages
Shota Orihashi, Yoshihiro Yamazaki, Naoki Makishima +4
This paper presents a novel training method for end-to-end scene text recognition. End-to-end scene text recognition offers high recognition accuracy, especially when using the enc…