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arXiv AI By Samyak Rawlekar, Nikhil Chowdary Paleti, Amey Gupta, Narendra Ahuja

Complementing DINO Features with Image Structure for Part Discovery

Read the original on arXiv AI →

The paper introduces Part‑DINO, a training‑free framework that enhances self‑supervised Vision Transformer (ViT) features from DINO with low‑level image boundaries to improve part discovery. By partitioning images using boundary detectors, pooling DINO features within each region, and clustering them, Part‑DINO yields structurally coherent part assignments. It outperforms other training‑free baselines on four standard benchmarks and remains competitive with weakly‑supervised and unsupervised methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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