Towards Efficient Use of Multi-Scale Features in Transformer-Based Object Detectors

24 August 2022

Papers citing "Towards Efficient Use of Multi-Scale Features in Transformer-Based Object Detectors"

8 / 8 papers shown

Title
Cross Resolution Encoding-Decoding For Detection Transformers Ashish Kumar Jaesik Park ViT 21 0 0 05 Oct 2024
DETR Doesn't Need Multi-Scale or Locality Design Yutong Lin Yuhui Yuan Zheng-Wei Zhang Chen Li Nanning Zheng Han Hu 25 5 0 03 Aug 2023
DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR Shilong Liu Feng Li Hao Zhang X. Yang Xianbiao Qi Hang Su Jun Zhu Lei Zhang ViT 138 703 0 28 Jan 2022
ViDT: An Efficient and Effective Fully Transformer-based Object Detector Hwanjun Song Deqing Sun Sanghyuk Chun Varun Jampani Dongyoon Han Byeongho Heo Wonjae Kim Ming-Hsuan Yang 78 75 0 08 Oct 2021
Hallucination Improves Few-Shot Object Detection Weilin Zhang Yu-xiong Wang ObjD 52 109 0 04 May 2021
BorderDet: Border Feature for Dense Object Detection Han Qiu Yuchen Ma Zeming Li Songtao Liu Jian-jun Sun ObjD 82 122 0 21 Jul 2020
Frustratingly Simple Few-Shot Object Detection Xin Wang Thomas E. Huang Trevor Darrell Joseph E. Gonzalez F. I. F. Richard Yu ObjD 75 535 0 16 Mar 2020
Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning Xiaopeng Yan Ziliang Chen Anni Xu Xiaoxi Wang Xiaodan Liang Liang Lin ObjD 151 440 0 28 Sep 2019