3.6k citations
- Japan Science and Technology AgencyJP16 papers
- RIKENJP15 papers
- The University of TokyoJP15 papers
- VTT Technical Research Centre of FinlandFI6 papers
- Tohoku UniversityJP5 papers
- Massachusetts Institute of TechnologyUS4 papers
- MIT Lincoln LaboratoryUS4 papers
- National Institute of Advanced Industrial Science and TechnologyJP4 papers
- NEC (United States)US4 papers
- RIKEN Advanced Science InstituteJP4 papers
- RIKEN Center for Emergent Matter ScienceJP4 papers
- University of California, Santa BarbaraUS4 papers
6 papers · 1 filter
Non-iterative optimization of pseudo-labeling thresholds for training object detection models from multiple datasets
Yuki Tanaka, Shuhei M. Yoshida, Makoto Terao
We propose a non-iterative method to optimize pseudo-labeling thresholds for learning object detection from a collection of low-cost datasets, each of which is annotated for only a…
Universal Adversarial Spoofing Attacks against Face Recognition
Takuma Amada, Seng Pei Liew, Kazuya Kakizaki +1
We assess the vulnerabilities of deep face recognition systems for images that falsify/spoof multiple identities simultaneously. We demonstrate that, by manipulating the deep featu…
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning
Honglu Zhou, Asim Kadav, Farley Lai +4
This paper considers the problem of spatiotemporal object-centric reasoning in videos. Central to our approach is the notion of object permanence, i.e., the ability to reason about…
Fingerprint Feature Extraction by Combining Texture, Minutiae, and Frequency Spectrum Using Multi-Task CNN
Ai Takahashi, Yoshinori Koda, Koichi Ito +1
Although most fingerprint matching methods utilize minutia points and/or texture of fingerprint images as fingerprint features, the frequency spectrum is also a useful feature sinc…
Understanding Road Layout from Videos as a Whole
Buyu Liu, Bingbing Zhuang, Samuel Schulter +2
In this paper, we address the problem of inferring the layout of complex road scenes from video sequences. To this end, we formulate it as a top-view road attributes prediction pro…
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation
Yizhe Zhu, Martin Renqiang Min, Asim Kadav +1
We propose a sequential variational autoencoder to learn disentangled representations of sequential data (e.g., videos and audios) under self-supervision. Specifically, we exploit…