3 papers
cs.CV2025
Quantifying Context Bias in Domain Adaptation for Object Detection
Hojun Son, Asma Almutairi, Arpan Kusari
Domain adaptation for object detection (DAOD) has become essential to counter performance degradation caused by distribution shifts between training and deployment domains. However…
cs.CV2025
Mitigating Context Bias in Domain Adaptation for Object Detection using Mask Pooling
Hojun Son, Asma Almutairi, Arpan Kusari
Context bias refers to the association between the foreground objects and background during the object detection training process. Various methods have been proposed to minimize th…
cs.CV2025
MatPredict: a dataset and benchmark for learning material properties of diverse indoor objects
Yuzhen Chen, Hojun Son, Arpan Kusari
Determining material properties from camera images can expand the ability to identify complex objects in indoor environments, which is valuable for consumer robotics applications.…