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Konstantinos Karantzalos

6 accepted papers

2026

Attention, Please! Revisiting Attentive Probing Through the Lens of Efficiency

Bill Psomas, Dionysis Christopoulos, Eirini Baltzi, Ioannis Kakogeorgiou, Tilemachos Aravanis, Nikos Komodakis +3
ICLR 2026poster

As fine-tuning becomes impractical at scale, probing is emerging as the preferred evaluation protocol. However, standard linear probing can understate the capability of models whose pre-training optimizes local representations rather than an explicit global representation. This motivates attentive p…

Cited by 0Source ↗code ↗Scholar ↗
2024

SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers

Ioannis Kakogeorgiou, Spyros Gidaris, Konstantinos Karantzalos, Nikos Komodakis
CVPR 2024highlight

Unsupervised object-centric learning aims to decompose scenes into interpretable object entities termed slots. Slot-based auto-encoders stand out as a prominent method for this task. Within them crucial aspects include guiding the encoder to generate object-specific slots and ensuring the decoder ut…

Cited by 14Source ↗PDF ↗code ↗Scholar ↗
2023

Keep It SimPool: Who Said Supervised Transformers Suffer from Attention Deficit?

Bill Psomas, Ioannis Kakogeorgiou, Konstantinos Karantzalos, Yannis Avrithis
ICCV 2023poster

Convolutional networks and vision transformers have different forms of pairwise interactions, pooling across layers and pooling at the end of the network. Does the latter really need to be different? As a by-product of pooling, vision transformers provide spatial attention for free, but this is mos…

Cited by 12PDF ↗code ↗Scholar ↗
2022

It Takes Two to Tango: Mixup for Deep Metric Learning

Shashanka Venkataramanan, Bill Psomas, Ewa Kijak, laurent amsaleg, Konstantinos Karantzalos, Yannis Avrithis
ICLR 2022poster

Metric learning involves learning a discriminative representation such that embeddings of similar classes are encouraged to be close, while embeddings of dissimilar classes are pushed far apart. State-of-the-art methods focus mostly on sophisticated loss functions or mining strategies. On the one ha…

Cited by 38Source ↗PDF ↗code ↗Scholar ↗
2022

Objects Can Move: 3D Change Detection by Geometric Transformation Consistency

Aikaterini Adam, Torsten Sattler, Konstantinos Karantzalos, Tomas Pajdla
ECCV 2022poster

"AR/VR applications and robots need to know when the scene has changed. An example is when objects are moved, added, or removed from the scene. We propose a 3D object discovery method that is based only on scene changes. Our method does not need to encode any assumptions about what is an object, but…

Cited by 11Source ↗PDF ↗code ↗Scholar ↗
2022

What to Hide from Your Students: Attention-Guided Masked Image Modeling

Ioannis Kakogeorgiou, Spyros Gidaris, Bill Psomas, Yannis Avrithis, Andrei Bursuc, Konstantinos Karantzalos +1
ECCV 2022poster

"Transformers and masked language modeling are quickly being adopted and explored in computer vision as vision transformers and masked image modeling (MIM). In this work, we argue that image token masking differs from token masking in text, due to the amount and correlation of tokens in an image. In…

Cited by 146Source ↗PDF ↗code ↗Scholar ↗