2 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
Structuring Open-Ended NAS: Semi-Automated Design Knowledge Structuring with LLMs for Efficient Neural Architecture Search
Yuiko Sakuma, Masakazu Yoshimura, Marcel Gröpl +4
Current neural architecture search (NAS) methods are often limited by their predefined, restrictive search spaces. While recent large language model (LLM)-assisted NAS methods enab…
Online Data Curation for Object Detection via Marginal Contributions to Dataset-level Average Precision
Zitang Sun, Masakazu Yoshimura, Junji Otsuka +2
High-quality data has become a primary driver of progress under scale laws, with curated datasets often outperforming much larger unfiltered ones at lower cost. Online data curatio…
Extreme Compression of Adaptive Neural Images
Leo Hoshikawa, Marcos V. Conde, Takeshi Ohashi +1
Implicit Neural Representations (INRs) and Neural Fields are a novel paradigm for signal representation, from images and audio to 3D scenes and videos. The fundamental idea is to r…
Mixed-precision Supernet Training from Vision Foundation Models using Low Rank Adapter
Yuiko Sakuma, Masakazu Yoshimura, Junji Otsuka +2
Compression of large and performant vision foundation models (VFMs) into arbitrary bit-wise operations (BitOPs) allows their deployment on various hardware. We propose to fine-tune…
Efficient Joint Detection and Multiple Object Tracking with Spatially Aware Transformer
Siddharth Sagar Nijhawan, Leo Hoshikawa, Atsushi Irie +3
We propose a light-weight and highly efficient Joint Detection and Tracking pipeline for the task of Multi-Object Tracking using a fully-transformer architecture. It is a modified…