Attention, Please! Revisiting Attentive Probing for Masked Image
  Modeling
Authors: name: Bill Psomas, name: Dionysis Christopoulos, name: Eirini Baltzi, name: Ioannis Kakogeorgiou, name: Tilemachos Aravanis, name: Nikos Komodakis, name: Konstantinos Karantzalos, name: Yannis Avrithis, name: Giorgos Tolias

As fine-tuning (FT) becomes increasingly impractical at scale, probing is
emerging as the preferred evaluation protocol for self-supervised learning
(SSL). Yet, the standard linear probing (LP) fails to adequately reflect the
potential of models trained with Masked Image Modeling (MIM), due to the
distributed nature of patch tokens. This motivates the need for attentive
probing, an alternative that uses attention to selectively aggregate
patch-level features. Despite its growing adoption, attentive probing remains
under-explored, with existing methods suffering from excessive parameterization
and poor computational efficiency.
  In this work, we revisit attentive probing through the lens of the
accuracy-efficiency trade-off. We conduct a systematic study of existing
methods, analyzing their mechanisms and benchmarking their performance. We
introduce efficient probing (EP), a multi-query cross-attention mechanism that
eliminates redundant projections, reduces the number of trainable parameters,
and achieves up to a 10times speed-up over conventional multi-head
attention. Despite its simplicity, EP outperforms LP and prior attentive
probing approaches across seven benchmarks, generalizes well beyond MIM to
diverse pre-training paradigms, produces interpretable attention maps, and
achieves strong gains in low-shot and layer-wise settings. Code available at
https://github.com/billpsomas/efficient-probing.