Why does LoRA work with rank 4 when weight matrices span thousands of dimensions? Practitioners set this by trial and error, yet the answer almost certainly lies in the geometry of the low-dimensional subspaces where foundation models encode their knowledge. The same structure underlies neural collapse, representation superposition, intrinsic dimensionality, and multimodal alignment.
This workshop bridges the classical, active tradition of subspace and manifold methods — long present in ACCV's main program — with the era of foundation models. It brings together the developers of subspace methods with the practitioners who build large models, and gives the Asian and international vision community a dedicated venue to connect this geometry to today's largest models.
Half-day workshop — invited talks, contributed oral presentations, a panel, and a poster session. Morning session on 14 December 2026, Grand Cube Osaka, Room 1001. Detailed clock times to be posted once finalized.
University College London. Statistical pattern recognition, machine learning, computer vision, and high-dimensional data analysis.
with Takashi Shibata and Makoto Terao
NEC Corporation. Subspace methods and Grassmann manifold learning; cross-modal retrieval and discriminant feature extraction.
Prof. Lincon Souza
Dr. Erica K. Shimomoto
Dr. Pedro H. V. Valois