91 citations · 250 across the 10 of their papers we have counts for
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LuMon: A Comprehensive Benchmark and Development Suite with Novel Datasets for Lunar Monocular Depth Estimation
Aytaç Sekmen, Fatih Emre Gunes, Furkan Horoz +9
Monocular Depth Estimation (MDE) is crucial for autonomous lunar rover navigation using electro-optical cameras. However, deploying terrestrial MDE networks to the Moon brings a se…
Exploring Sparsity for Parameter Efficient Fine Tuning Using Wavelets for Vision
Ahmet Bilican, M. Akın Yılmaz, A. Murat Tekalp +1
Efficiently adapting large pretrained models is critical under tight compute and memory budgets. While Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA achieve efficiency t…
Meta-LoRA: Meta-Learning LoRA Components for Domain-Aware ID Personalization
Barış Batuhan Topal, Umut Özyurt, Zafer Doğan Budak +1
Recent advancements in text-to-image generative models, particularly latent diffusion models (LDMs), have demonstrated remarkable capabilities in synthesizing high-quality images f…
HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness
Mehmet Kerim Yucel, Ramazan Gokberk Cinbis, Pinar Duygulu
Convolutional Neural Networks (CNN) are known to exhibit poor generalization performance under distribution shifts. Their generalization have been studied extensively, and one line…
VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON
Haoping Bai, Shancong Mou, Tatiana Likhomanenko +6
Despite progress in vision-based inspection algorithms, real-world industrial challenges -- specifically in data availability, quality, and complex production requirements -- often…
Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection
Berkan Demirel, Orhun Buğra Baran, Ramazan Gokberk Cinbis
Few-shot object detection, the problem of modelling novel object detection categories with few training instances, is an emerging topic in the area of few-shot learning and object…