activity
20222026
most citedFocus-and-Detect: A Small Object Detection Framework for Aerial Images

107 citations · 107 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2026

Pathway-Structured Privileged Distillation for Deployable Computational Pathology

Yongxin Guo, Hao Lu, Onur Koyun +2

Integrating transcriptomics and histopathology can improve cancer risk modelling, yet practical use is constrained by the limited availability of RNA profiling in routine settings.…

cs.CV2026

NSVQ: Mitigating Codebook Collapse by Stabilizing Encoder Drift in Vector Quantization

Hao Lu, Yongxin Guo, Onur Koyun +3

Vector quantization is central to modern generative modeling pipelines, but large-codebook VQ models often suffer from codebook collapse. We identify encoder drift as a key driver…

cs.CV2026

Momentum Memory for Knowledge Distillation in Computational Pathology

Yongxin Guo, Hao Lu, Onur C. Koyun +3

Multimodal learning that integrates genomics and histopathology has shown strong potential in cancer diagnosis, yet its clinical translation is hindered by the limited availability…

cs.LG2026

PCA-VAE: Differentiable Subspace Quantization without Codebook Collapse

Hao Lu, Onur C. Koyun, Yongxin Guo +3

Vector-quantized autoencoders deliver high-fidelity latents but suffer inherent flaws: the quantizer is non-differentiable, requires straight-through hacks, and is prone to collaps…

cs.CV2026

Beyond Stationarity: Rethinking Codebook Collapse in Vector Quantization

Hao Lu, Onur C. Koyun, Yongxin Guo +3

Vector Quantization (VQ) underpins many modern generative frameworks such as VQ-VAE, VQ-GAN, and latent diffusion models. Yet, it suffers from the persistent problem of codebook co…

cs.LG2024

Do deep neural networks utilize the weight space efficiently?

Onur Can Koyun, Behçet Uğur Töreyin

Deep learning models like Transformers and Convolutional Neural Networks (CNNs) have revolutionized various domains, but their parameter-intensive nature hampers deployment in reso…

cs.CV2022★ 107 cited

Focus-and-Detect: A Small Object Detection Framework for Aerial Images

Onur Can Koyun, Reyhan Kevser Keser, İbrahim Batuhan Akkaya +1

Despite recent advances, object detection in aerial images is still a challenging task. Specific problems in aerial images makes the detection problem harder, such as small objects…