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20242026
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cs.CV2026

Why Domain Matters: Domain-Aware Benchmarking of Underwater Object Detection and Annotation Quality

Melanie Wille, Dimity Miller, Tobias Fischer +1

Underwater object detection is strongly affected by domain shift, where performance can vary significantly across different locations, habitats, and deployment conditions. However,…

cs.CV2026

From Pixels to Primitives: Scene Change Detection in 3D Gaussian Splatting

Chamuditha Jayanga Galappaththige, Jason Lai, Timothy Patten +3

Scene change detection methods built on Gaussian splatting universally follow a render-then-compare paradigm: the pre-change scene is rendered into 2D and compared against post-cha…

cs.CV2026

Why Domain Matters: A Preliminary Study of Domain Effects in Underwater Object Detection

Melanie Wille, Dimity Miller, Tobias Fischer +1

Domain shift, where deviations between training and deployment data distributions degrade model performance, is a key challenge in underwater environments. Existing benchmarks test…

cs.CV2026

Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis

Chamuditha Jayanga Galappaththige, Thomas Gottwald, Peter Stehr +4

Recent advances in 3D Gaussian Splatting have enabled impressive photorealistic novel view synthesis. However, to transition from a pure rendering engine to a reliable spatial map…

cs.CV2025

Intra-Class Probabilistic Embeddings for Uncertainty Estimation in Vision-Language Models

Zhenxiang Lin, Maryam Haghighat, Will Browne +1

Vision-language models (VLMs), such as CLIP, have gained popularity for their strong open vocabulary classification performance, but they are prone to assigning high confidence sco…

cs.CV2025

Changes in Real Time: Online Scene Change Detection with Multi-View Fusion

Chamuditha Jayanga Galappaththige, Jason Lai, Lloyd Windrim +3

Online Scene Change Detection (SCD) is an extremely challenging problem that requires an agent to detect relevant changes on the fly while observing the scene from unconstrained vi…