Tootfinder

Depth Edge Alignment Loss: DEALing with Depth in Weakly Supervised Semantic Segmentation
Patrick Schmidt, Vasileios Belagiannis, Lazaros Nalpantidis
https://arxiv.org/abs/2509.17702

Depth Edge Alignment Loss: DEALing with Depth in Weakly Supervised Semantic Segmentation
Autonomous robotic systems applied to new domains require an abundance of expensive, pixel-level dense labels to train robust semantic segmentation models under full supervision. This study proposes a model-agnostic Depth Edge Alignment Loss to improve Weakly Supervised Semantic Segmentation models across different datasets. The methodology generates pixel-level semantic labels from image-level supervision, avoiding expensive annotation processes. While weak supervision is widely explored in tr…

Der Tag heute war verregnet, trotzdem erkundeten wir das Zentrum Tokios und startetem am Hie-Schrein mit einem Tunnel aus roten Torii, die dem Schrein gespendet wurden. Weiter spazierten wir am japanischen Parlament vorbei an den zugehörigen Parks, vorbei am Kaiserpalast und in den #Hibiya-Park. #Tokyocameraclub

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Mica:
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Ottorino Respighi, BBC Symphony Orchestra & Nil Venditti:
🎵 Feste Romane
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A Bivariate DAR($1$) model for ordinal time series
Anna Nalpantidi, Dimitris Karlis
https://arxiv.org/abs/2510.05680 https://arxiv.org/pdf/2510.05680

A Bivariate DAR($1$) model for ordinal time series
We present a bivariate vector valued discrete autoregressive model of order $1$ (BDAR($1$)) for discrete time series. The BDAR($1$) model assumes that each time series follows its own univariate DAR($1$) model with dependent random mechanisms that determine from which component the current status occurs and dependent innovations. The joint distribution of the random mechanisms which are expressed by Bernoulli vectors are proposed to be defined through copulas. The same holds for the joint distr…

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Giacomo Puccini, BBC Symphony Orchestra & Nil Venditti:
🎵 Preludio Sinfonico
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ClustViT: Clustering-based Token Merging for Semantic Segmentation
Fabio Montello, Ronja G\"uldenring, Lazaros Nalpantidis
https://arxiv.org/abs/2510.01948 https://

ClustViT: Clustering-based Token Merging for Semantic Segmentation
Vision Transformers can achieve high accuracy and strong generalization across various contexts, but their practical applicability on real-world robotic systems is limited due to their quadratic attention complexity. Recent works have focused on dynamically merging tokens according to the image complexity. Token merging works well for classification but is less suited to dense prediction. We propose ClustViT, where we expand upon the Vision Transformer (ViT) backbone and address semantic segmen…

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