[
    {
        "id": "osp-24476",
        "type": "article-journal",
        "title": "AHMAD: Adaptive Hybrid Multi-task Vision Learning with Assisted Distillation for Keypoint Detection",
        "author": [
            {
                "family": "Mahdi",
                "given": "Mohammad"
            },
            {
                "family": "Prisadnikov",
                "given": "Nedyalko"
            },
            {
                "family": "Fu",
                "given": "Yuqian"
            },
            {
                "family": "Scribano",
                "given": "Carmelo"
            },
            {
                "family": "Paudel",
                "given": "Danda Pani"
            },
            {
                "family": "Van Gool",
                "given": "Luc"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/ahmad-adaptive-hybrid-multi-task-vision-learning-with-assisted-distillation-for-keypoint-detection",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Generalist multitasking vision models aim to unify multiple vision tasks within a single framework, enabling more efficient and versatile learning. However, handling diverse vision tasks -- spanning dense and sparse predictions -- remains challenging due to their inherently varying output structures. In this paper, we propose AHMAD, a simple yet effective framework for generalist multitask learning that integrates different key vision tasks: semantic segmentation, instance segmentation, depth estimation, keypoint detection, and object detection. Our approach incorporates these five tasks into a unified structure: a shared encoder-decoder with several lightweight task-specific projectors. Under the multitask learning paradigm, we observed a complementary performance gain, achieving a state-of-the-art PQ of 53.1 and an mIoU of 66.5 for COCO-val panoptic and semantic segmentation, respectively. Additionally, for top-down keypoint detection, which typically incurs high computational overhead due to multiple forward passes, we introduce a knowledge distillation-based method that enables a single forward pass over the entire image, greatly improving efficiency. Ultimately, our model delivers a lightweight yet effective generalist multitask learning framework, demonstrating strong performance across five vision tasks."
    }
]