arXiv AI By Amir Mohammad Karimi Mamaghan, Ferdinand Kapl, Andrea Dittadi, Karl Henrik Johansson, Kaitlin Maile, Johannes von Oswald, Stefan Bauer

Growing to See: Efficient Pretraining of Self-Supervised Vision Transformers

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv AI
Jun 15

Learning What to Predict: Downstream-Guided Task Design for Continued Pretraining

arXiv:2601. 22108v2 Announce Type: replace-cross Abstract: Continued pretraining is optimized with fixed self-supervised tasks but selected by downstream performance, creating a coarse feedback loop in which practitioners evaluate checkpoints, change data mixtures or objectives, and restart runs, while individual updates remain blind to target capabilities.

By Shuqi Ke, Giulia Fanti
arXiv Computer Vision
Sep 7

DART: Depth-as-Target Pretraining for Surgical Vision Foundation Models

DART is a new RGB‑D pretraining method for surgical vision foundation models that incorporates pseudo‑labeled depth maps as a pixel‑space reconstruction target during training. By adding a depth reconstruction head to DINOv2’s masked iBOT framework, DART improves representation quality without affecting downstream RGB‑only fine‑tuning or inference. Across eight surgical benchmarks—including segmentation, depth estimation, and image‑level recognition—DART outperforms both natural‑image and in‑domain baselines, demonstrating that geometric pseudo‑labels can strengthen foundation model pretraining without extra labels or inference cost.

By John J. Han, Adam Schmidt, Muhammad Abdullah Jamal, Jie Ying Wu, Omid Mohareri
arXiv Computer Vision
2d ago

CODI: Improving Self-Supervised Vision Transformers with Cross Distillation

The paper introduces CODI, a method that enhances self‑supervised Vision Transformers by mixing a small portion of the teacher’s class‑token distribution into each teacher patch‑token target. This cross distillation approach gives every masked patch an attention‑independent semantic correction without altering the architecture. Experiments demonstrate that CODI improves performance on both dense prediction and image‑level tasks under aligned pretraining settings.

By Siran Dai, Qianqian Xu, Peisong Wen, Yang Liu, Xiaochun Cao, Qingming Huang