25 citations · 25 across the 4 of their papers we have counts for
8 papers
DESA-TTA: Dynamic EMA and Source Anchoring for Test-Time Adaptation
Atif Belal, Lilian Hollard, Marco Pedersoli +1
Vision-language object detectors (VLODs) achieve strong zero-shot performance but remain vulnerable to distribution shifts during deployment. Mean-teacher methods for test-time ada…
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…
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…
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…
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…
Multi-Source Domain Adaptation for Object Detection with Prototype-based Mean-teacher
Atif Belal, Akhil Meethal, Francisco Perdigon Romero +2
Adapting visual object detectors to operational target domains is a challenging task, commonly achieved using unsupervised domain adaptation (UDA) methods. Recent studies have show…
Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains
Le Thanh Nguyen-Meidine, Atif Belal, Madhu Kiran +3
Beyond the complexity of CNNs that require training on large annotated datasets, the domain shift between design and operational data has limited the adoption of CNNs in many real-…