Standard
Class-Agnostic Object Counting with Text-to-Image Diffusion Model. / Hui, Xiaofei; Wu, Qian
; Rahmani, Hossein et al.
Computer Vision -- ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LXIX. ed. / Aleš Leonardis; Elisa Ricci; Stefan Roth; Olga Russakovsky; Torsten Sattler; Gül Varol. Cham: Springer, 2024. p. 1-18 (Lecture Notes in Computer Science ; Vol. 15127).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Hui, X, Wu, Q
, Rahmani, H & Liu, J 2024,
Class-Agnostic Object Counting with Text-to-Image Diffusion Model. in A Leonardis, E Ricci, S Roth, O Russakovsky, T Sattler & G Varol (eds),
Computer Vision -- ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LXIX. Lecture Notes in Computer Science , vol. 15127, Springer, Cham, pp. 1-18.
https://doi.org/10.1007/978-3-031-72890-7_1APA
Hui, X., Wu, Q.
, Rahmani, H., & Liu, J. (2024).
Class-Agnostic Object Counting with Text-to-Image Diffusion Model. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, & G. Varol (Eds.),
Computer Vision -- ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LXIX (pp. 1-18). (Lecture Notes in Computer Science ; Vol. 15127). Springer.
https://doi.org/10.1007/978-3-031-72890-7_1Vancouver
Hui X, Wu Q
, Rahmani H, Liu J.
Class-Agnostic Object Counting with Text-to-Image Diffusion Model. In Leonardis A, Ricci E, Roth S, Russakovsky O, Sattler T, Varol G, editors, Computer Vision -- ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LXIX. Cham: Springer. 2024. p. 1-18. (Lecture Notes in Computer Science ). Epub 2024 Nov 3. doi: 10.1007/978-3-031-72890-7_1
Author
Hui, Xiaofei ; Wu, Qian
; Rahmani, Hossein et al. /
Class-Agnostic Object Counting with Text-to-Image Diffusion Model. Computer Vision -- ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LXIX. editor / Aleš Leonardis ; Elisa Ricci ; Stefan Roth ; Olga Russakovsky ; Torsten Sattler ; Gül Varol. Cham : Springer, 2024. pp. 1-18 (Lecture Notes in Computer Science ).
Bibtex
@inproceedings{93c9bfc01ccb46508fc5a6348767f5b2,
title = "Class-Agnostic Object Counting with Text-to-Image Diffusion Model",
abstract = "Class-agnostic object counting aims to count objects of arbitrary classes with limited information (e.g., a few exemplars or the class names) provided. It requires the model to effectively acquire the characteristics of the target objects and accurately perform counting, which can be challenging. In this work, inspired by that text-to-image diffusion models hold rich knowledge and comprehensive understanding of real-world objects, we propose to leverage the pre-trained text-to-image diffusion model to facilitate class-agnostic object counting. Specifically, we propose a novel framework named CountDiff with careful designs, leveraging the pre-trained diffusion model{\textquoteright}s comprehensive understanding of image contents to perform class-agnostic object counting. The experiments show the effectiveness of CountDiff on both few-shot setting with exemplars provided and zero-shot setting with class names provided.",
author = "Xiaofei Hui and Qian Wu and Hossein Rahmani and Jun Liu",
year = "2024",
month = dec,
day = "7",
doi = "10.1007/978-3-031-72890-7_1",
language = "English",
isbn = "9783031728891",
series = "Lecture Notes in Computer Science ",
publisher = "Springer",
pages = "1--18",
editor = "Leonardis, {Ale{\v s} } and Elisa Ricci and Stefan Roth and Olga Russakovsky and Torsten Sattler and G{\"u}l Varol",
booktitle = "Computer Vision -- ECCV 2024",
}
RIS
TY - GEN
T1 - Class-Agnostic Object Counting with Text-to-Image Diffusion Model
AU - Hui, Xiaofei
AU - Wu, Qian
AU - Rahmani, Hossein
AU - Liu, Jun
PY - 2024/12/7
Y1 - 2024/12/7
N2 - Class-agnostic object counting aims to count objects of arbitrary classes with limited information (e.g., a few exemplars or the class names) provided. It requires the model to effectively acquire the characteristics of the target objects and accurately perform counting, which can be challenging. In this work, inspired by that text-to-image diffusion models hold rich knowledge and comprehensive understanding of real-world objects, we propose to leverage the pre-trained text-to-image diffusion model to facilitate class-agnostic object counting. Specifically, we propose a novel framework named CountDiff with careful designs, leveraging the pre-trained diffusion model’s comprehensive understanding of image contents to perform class-agnostic object counting. The experiments show the effectiveness of CountDiff on both few-shot setting with exemplars provided and zero-shot setting with class names provided.
AB - Class-agnostic object counting aims to count objects of arbitrary classes with limited information (e.g., a few exemplars or the class names) provided. It requires the model to effectively acquire the characteristics of the target objects and accurately perform counting, which can be challenging. In this work, inspired by that text-to-image diffusion models hold rich knowledge and comprehensive understanding of real-world objects, we propose to leverage the pre-trained text-to-image diffusion model to facilitate class-agnostic object counting. Specifically, we propose a novel framework named CountDiff with careful designs, leveraging the pre-trained diffusion model’s comprehensive understanding of image contents to perform class-agnostic object counting. The experiments show the effectiveness of CountDiff on both few-shot setting with exemplars provided and zero-shot setting with class names provided.
U2 - 10.1007/978-3-031-72890-7_1
DO - 10.1007/978-3-031-72890-7_1
M3 - Conference contribution/Paper
SN - 9783031728891
T3 - Lecture Notes in Computer Science
SP - 1
EP - 18
BT - Computer Vision -- ECCV 2024
A2 - Leonardis, Aleš
A2 - Ricci, Elisa
A2 - Roth, Stefan
A2 - Russakovsky, Olga
A2 - Sattler, Torsten
A2 - Varol, Gül
PB - Springer
CY - Cham
ER -
