Zobrazeno 1 - 10
of 285
pro vyhledávání: '"KALKAN, SİNAN"'
Ranking-based loss functions, such as Average Precision Loss and Rank&Sort Loss, outperform widely used score-based losses in object detection. These loss functions better align with the evaluation criteria, have fewer hyperparameters, and offer robu
Externí odkaz:
http://arxiv.org/abs/2407.14204
In many applications, a mobile manipulator robot is required to grasp a set of objects distributed in space. This may not be feasible from a single base pose and the robot must plan the sequence of base poses for grasping all objects, minimizing the
Externí odkaz:
http://arxiv.org/abs/2406.08653
Heatmaps have been instrumental in helping understand deep network decisions, and are a common approach for Explainable AI (XAI). While significant progress has been made in enhancing the informativeness and accessibility of heatmaps, heatmap analysi
Externí odkaz:
http://arxiv.org/abs/2405.13264
We introduce, XoFTR, a cross-modal cross-view method for local feature matching between thermal infrared (TIR) and visible images. Unlike visible images, TIR images are less susceptible to adverse lighting and weather conditions but present difficult
Externí odkaz:
http://arxiv.org/abs/2404.09692
Detecting edges in images suffers from the problems of (P1) heavy imbalance between positive and negative classes as well as (P2) label uncertainty owing to disagreement between different annotators. Existing solutions address P1 using class-balanced
Externí odkaz:
http://arxiv.org/abs/2403.01795
Autor:
Çam, Barış Can, Öksüz, Kemal, Kahraman, Fehmi, Baltacı, Zeynep Sonat, Kalkan, Sinan, Akbaş, Emre
This paper introduces Generalized Mask-aware Intersection-over-Union (GmaIoU) as a new measure for positive-negative assignment of anchor boxes during training of instance segmentation methods. Unlike conventional IoU measure or its variants, which o
Externí odkaz:
http://arxiv.org/abs/2312.17031
Unfair predictions of machine learning (ML) models impede their broad acceptance in real-world settings. Tackling this arduous challenge first necessitates defining what it means for an ML model to be fair. This has been addressed by the ML community
Externí odkaz:
http://arxiv.org/abs/2312.11299
Object detectors are conventionally trained by a weighted sum of classification and localization losses. Recent studies (e.g., predicting IoU with an auxiliary head, Generalized Focal Loss, Rank & Sort Loss) have shown that forcing these two loss ter
Externí odkaz:
http://arxiv.org/abs/2301.01019
The rise of simulation environments has enabled learning-based approaches for assembly planning, which is otherwise a labor-intensive and daunting task. Assembling furniture is especially interesting since furniture are intricate and pose challenges
Externí odkaz:
http://arxiv.org/abs/2209.07268
Autor:
Yavuz, Feyza, Kalkan, Sinan
Logo retrieval is a challenging problem since the definition of similarity is more subjective compared to image retrieval tasks and the set of known similarities is very scarce. To tackle this challenge, in this paper, we propose a simple but effecti
Externí odkaz:
http://arxiv.org/abs/2209.02482