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Lilian Weng details fast object detection models like YOLO and SSD

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Two new research papers propose novel approaches to object detection. VFM4SDG aims to improve single-domain generalized object detection by using a frozen vision foundation model to maintain cross-domain stability, addressing issues with weather and illumination changes. UHR-DETR tackles the challenge of detecting small objects in ultra-high-resolution remote sensing imagery by efficiently allocating computational resources and integrating global and local scene information. AI

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RANK_REASON Two new arXiv papers present novel methods for object detection, falling under the research category.

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Lilian Weng details fast object detection models like YOLO and SSD

COVERAGE [6]

  1. Lil'Log (Lilian Weng) TIER_1 ·

    Object Detection Part 4: Fast Detection Models

    <!-- Part 4 of the "Object Detection for Dummies" series focuses on one-stage models for fast detection, including SSD, RetinaNet, and models in the YOLO family. These models skip the explicit region proposal stage but apply the detection directly on dense sampled areas. --> <p>I…

  2. Lil'Log (Lilian Weng) TIER_1 ·

    Object Detection for Dummies Part 3: R-CNN Family

    <!-- In Part 3, we would examine four object detection models: R-CNN, Fast R-CNN, Faster R-CNN, and Mask R-CNN. These models are highly related and the new versions show great speed improvement compared to the older ones. --> <p><span class="update">[Updated on 2018-12-20: Remove…

  3. Lil'Log (Lilian Weng) TIER_1 ·

    Object Detection for Dummies Part 2: CNN, DPM and Overfeat

    <!-- Part 2 introduces several classic convolutional neural work architecture designs for image classification (AlexNet, VGG, ResNet), as well as DPM (Deformable Parts Model) and Overfeat models for object recognition. --> <p><a href="https://lilianweng.github.io/posts/2017-10-29…

  4. Lil'Log (Lilian Weng) TIER_1 ·

    Object Detection for Dummies Part 1: Gradient Vector, HOG, and SS

    <!-- In this series of posts on "Object Detection for Dummies", we will go through several basic concepts, algorithms, and popular deep learning models for image processing and objection detection. Hopefully, it would be a good read for people with no experience in this field but…

  5. arXiv cs.CV TIER_1 · Liang Wan ·

    VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection

    In real-world scenarios, continual changes in weather, illumination, and imaging conditions cause significant domain shifts, leading detectors trained on a single source domain to degrade severely in unseen environments. Existing single-domain generalized object detection (SDGOD)…

  6. arXiv cs.CV TIER_1 · Gui-Song Xia ·

    UHR-DETR: Efficient End-to-End Small Object Detection for Ultra-High-Resolution Remote Sensing Imagery

    Ultra-High-Resolution (UHR) imagery has become essential for modern remote sensing, offering unprecedented spatial coverage. However, detecting small objects in such vast scenes presents a critical dilemma: retaining the original resolution for small objects causes prohibitive me…

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