Therefore, we propose. SpeedDETR, a novel speed-aware transformer for end-to-end object detectors, achieving high-speed inference on multiple devices.
Apr 24, 2023 · We propose SpeedDETR, a novel speed-aware transformer for end-to-end object detectors, achieving high-speed inference on multiple devices.
This work proposes SpeedDETR, a novel speed-aware transformer for end-to-end object detectors, achieving high-speed inference on multiple devices and ...
Jul 23, 2023 · Therefore, we propose SpeedDETR, a novel speed-aware transformer for end-to-end object detectors, achieving high-speed inference on multiple ...
SpeedDETR: Speed-aware Transformers for End-to-end Object Detection · Peiyan Dong · I2. ICML 2023 ; DART: Articulated Hand Model with Diverse Accessories and Rich ...
This repo contains a comprehensive paper list of Vision Transformer & Attention, including papers, codes, and related websites.
Jun 4, 2024 · SpeedDETR: Speed-aware Transformers for End-to-end Object Detection. ICML 2023: 8227-8243; 2022. [c4]. view. electronic edition via DOI (open ...
A novel decoder-free fully transformer-based (DFFT) object detector is proposed, achieving high efficiency in both training and inference stages, ...
May 26, 2020 · We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline.
Missing: SpeedDETR: Speed- aware
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Jul 27, 2024 · DETR is a deep learning model for object detection that leverages transformers to predict bounding boxes and class labels for an image.