Eirini Baltzi

Bill Psomas, Dionysis Christopoulos, Eirini Baltzi et al.

As fine-tuning becomes increasingly impractical at scale, probing is emerging as the preferred evaluation protocol. Yet, the standard linear probing fails to adequately reflect the potential of models whose pre-training optimizes representations of patch tokens rather than an explicit global representation. 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 vs. parameter efficiency trade-off. We present the first comprehensive study of existing methods, analyzing their design choices and benchmarking their performance. Building on this, we propose efficient probing (EP), a simple yet effective multi-query cross-attention mechanism that eliminates redundant projections and reduces the number of trainable parameters. Despite its simplicity, EP outperforms linear probing and prior attentive probing approaches across seven benchmarks, generalizes well to diverse pre-training paradigms, and delivers strong low-shot and layer-wise gains. Beyond evaluation, our analysis uncovers emerging properties of EP, such as complementary attention maps, which open new directions for leveraging probing beyond protocol design. Code available at https://github.com/billpsomas/efficient-probing.

Eirini Baltzi, Dionysis Christopoulos, Sotiris Spanos et al.

Vision-language models (VLMs) have sparked growing interest in zero-shot Earth Observation (EO) downstream tasks, with further gains enabled by remote-sensing-adapted models. We examine this setting across 17 VLM variants and 12 remote sensing (RS) datasets under Meta-Prompting for Visual Recognition (MPVR), and show that zero-shot performance remains highly sensitive to textual design choices, from the meta-prompts used to guide the LLM in generating class descriptions to the descriptions themselves. We explore why semantically rich LLM-generated class descriptions do not translate into consistent gains over simple domain-adapted CLIP-style descriptions. While LLM descriptions are more semantically expressive, they can also introduce noise in the text embedding space, reducing robustness in downstream tasks. We support this observation through a text log-likelihood analysis in the whitened CLIP feature space, comparing LLM-generated and template-based descriptions. Building on this finding, we study query embedding calibration and show that lightweight calibration of the query space consistently yields strong improvements in zero-shot classification and retrieval. Overall, our results provide practical insight into the trade-off between semantic richness and robustness, and identify embedding calibration as a simple and effective tool for improving zero-shot remote sensing performance.

Dionysis Christopoulos, Sotiris Spanos, Eirini Baltzi et al.

We introduce SLIMP (Skin Lesion Image-Metadata Pre-training) for learning rich representations of skin lesions through a novel nested contrastive learning approach that captures complex relationships between images and metadata. Melanoma detection and skin lesion classification based solely on images, pose significant challenges due to large variations in imaging conditions (lighting, color, resolution, distance, etc.) and lack of clinical and phenotypical context. Clinicians typically follow a holistic approach for assessing the risk level of the patient and for deciding which lesions may be malignant and need to be excised, by considering the patient's medical history as well as the appearance of other lesions of the patient. Inspired by this, SLIMP combines the appearance and the metadata of individual skin lesions with patient-level metadata relating to their medical record and other clinically relevant information. By fully exploiting all available data modalities throughout the learning process, the proposed pre-training strategy improves performance compared to other pre-training strategies on downstream skin lesions classification tasks highlighting the learned representations quality.