1 citations · 2 across the 3 of their papers we have counts for
4 papers
From Words to Wavelengths: VLMs for Few-Shot Multispectral Object Detection
Manuel Nkegoum, Minh-Tan Pham, Élisa Fromont +2
Multispectral object detection is critical for safety-sensitive applications such as autonomous driving and surveillance, where robust perception under diverse illumination conditi…
FSMODNet: A Closer Look at Few-Shot Detection in Multispectral Data
Manuel Nkegoum, Minh-Tan Pham, Élisa Fromont +2
Few-shot multispectral object detection (FSMOD) addresses the challenge of detecting objects across visible and thermal modalities with minimal annotated data. In this paper, we ex…
Localize to Classify and Classify to Localize: Mutual Guidance in Object Detection
Heng Zhang, Elisa Fromont, Sébastien Lefevre +1
Most deep learning object detectors are based on the anchor mechanism and resort to the Intersection over Union (IoU) between predefined anchor boxes and ground truth boxes to eval…
Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks
Heng Zhang, Elisa Fromont, Sébastien Lefevre +1
Multispectral images (e.g. visible and infrared) may be particularly useful when detecting objects with the same model in different environments (e.g. day/night outdoor scenes). To…