Object detection can be effectively formulated as a denoising diffusion process, where the Consistency Model offers a more efficient one-step denoising mechanism compared to the conventional iterative denoising of Diffusion Models.
InstaGen, a novel paradigm to enhance object detection capabilities by training on synthetic datasets generated from diffusion models, demonstrates superior performance over existing state-of-the-art methods in open-vocabulary and data-sparse scenarios.
Combining few-shot learning and object detection techniques to rapidly adapt to novel objects with limited annotated samples.
RT-DETR, the first real-time end-to-end object detector, outperforms previously advanced YOLO detectors in both speed and accuracy, while eliminating the negative impact of NMS post-processing.
The core message of this paper is to propose a novel knowledge distillation method that simultaneously considers the classification and regression tasks of object detectors, enabling accurate assessment of the student model's learning condition and enhancing the effectiveness of knowledge distillation.
RadarDistill improves radar-based object detection by distilling LiDAR knowledge, achieving state-of-the-art performance.
Introducing BAM, a novel method for real-time Out-of-Distribution (OoD) detection in object detection without the need for architectural changes.
Efficiently aligning class instances across domains improves object detection performance.
Efficiently reducing model size while maintaining high detection accuracy through knowledge distillation in YOLOX-ViT for side-scan sonar object detection.
The author presents a novel framework for robust 3D object detection from LiDAR and radar point clouds using cross-modal hallucination, achieving superior performance in both modalities.