5 papers
VLOD-TTA: Test-Time Adaptation of Vision-Language Object Detectors
Atif Belal, Heitor R. Medeiros, Marco Pedersoli +1
Vision-language object detectors (VLODs) such as YOLO-World and Grounding DINO exhibit strong zero-shot generalization, but their performance degrades under distribution shift. Tes…
WiSE-OD: Benchmarking Robustness in Infrared Object Detection
Heitor R. Medeiros, Atif Belal, Masih Aminbeidokhti +2
Object detection (OD) in infrared (IR) imagery is critical for low-light and nighttime applications. However, the scarcity of large-scale IR datasets forces models to rely on weigh…
Low-Rank Expert Merging for Multi-Source Domain Adaptation in Person Re-Identification
Taha Mustapha Nehdi, Nairouz Mrabah, Atif Belal +2
Adapting person re-identification (reID) models to new target environments remains a challenging problem that is typically addressed using unsupervised domain adaptation (UDA) meth…
Visual Modality Prompt for Adapting Vision-Language Object Detectors
Heitor R. Medeiros, Atif Belal, Srikanth Muralidharan +2
The zero-shot performance of object detectors degrades when tested on different modalities, such as infrared and depth. While recent work has explored image translation techniques…
Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptation of Object Detectors
Atif Belal, Akhil Meethal, Francisco Perdigon Romero +2
Domain adaptation methods for object detection (OD) strive to mitigate the impact of distribution shifts by promoting feature alignment across source and target domains. Multi-sour…