The goal of this paper is to improve the generality and accuracy of open-vocabulary object counting in images. To improve the generality, we repurpose an open-vocabulary detection foundation model (GroundingDINO) for the counting task, and also extend its capabilities by introducing modules to enable specifying the target object to count by visual exemplars. In turn, these new capabilities - being able to specify the target object by multi-modalites (text and exemplars) - lead to an improvement in counting accuracy.
We make three contributions: First, we introduce the first open-world counting model, CountGD, where the prompt can be specified by a text description or visual exemplars or both; Second, we show that the performance of the model significantly improves the state of the art on multiple counting benchmarks - when using text only, CountGD is comparable to or outperforms all previous text-only works, and when using both text and visual exemplars, we outperform all previous models; Third, we carry out a preliminary study into different interactions between the text and visual exemplar prompts, including the cases where they reinforce each other and where one restricts the other. The code and an app to test the model are available at https://www.robots.ox.ac.uk/~vgg/research/countgd/.
Integrating multispectral data in object detection, especially visible and infrared images, has received great attention in recent years. Since visible (RGB) and infrared (IR) images can provide complementary information to handle light variations, the paired images are used in many fields, such as multispectral pedestrian detection, RGB-IR crowd counting and RGB-IR salient object detection. Compared with natural RGB-IR images, we find detection in aerial RGB-IR images suffers from cross-modal weakly misalignment problems, which are manifested in the position, size and angle deviations of the …
Crowd counting aims to estimate the number of persons in a scene. Most state-of-the-art crowd counting methods based on color images can't work well in poor illumination conditions due to invisible objects. With the widespread use of infrared cameras, crowd counting based on color and thermal images is studied. Existing methods only achieve multi-modal fusion without count objective constraint. To better excavate multi-modal information, we use count-guided multi-modal fusion and modal-guided count enhancement to achieve the impressive performance. The proposed count-guided multi-modal fusion …
Crowd counting research has made significant advancements in real-world applications, but it remains a formidable challenge in cross-modal settings. Most existing methods rely solely on the optical features of RGB images, ignoring the feasibility of other modalities such as thermal and depth images. The inherently significant differences between the different modalities and the diversity of design choices for model architectures make cross-modal crowd counting more challenging. In this paper, we propose Cross-modal Spatio-Channel Attention (CSCA) blocks, which can be easily integrated into any…
RGB-Thermal (RGB-T) crowd counting is a challenging task, which uses thermal images as complementary information to RGB images to deal with the decreased performance of unimodal RGB-based methods in scenes with low-illumination or similar backgrounds. Most existing methods propose well-designed structures for cross-modal fusion in RGB-T crowd counting. However, these methods have difficulty in encoding cross-modal contextual semantic information in RGB-T image pairs. Considering the aforementioned problem, we propose a two-stream RGB-T crowd counting network called Multi-Attention Fusion Netwo…
University of Kansas（堪萨斯大学）；NUST（巴基斯坦国立科技大学）；Ryerson University（瑞尔森大学）
Crowd estimation is a very challenging problem. The most recent study tries to exploit auditory information to aid the visual models, however, the performance is limited due to the lack of an effective approach for feature extraction and integration. The paper proposes a new audiovisual multi-task network to address the critical challenges in crowd counting by effectively utilizing both visual and audio inputs for better modalities association and productive feature extraction. The proposed network introduces the notion of auxiliary and explicit image patch-importance ranking (PIR) and patch-w…
T2ICount: Enhancing Cross-modal Understanding for Zero-Shot Counting
University of Nottingham（诺丁汉大学）；University of St Andrews（圣安德鲁大学）；City University of Hong Kong（香港城市大学）；Nanyang Technology University（南洋理工大学）；Harbin Institute of Technology（哈尔滨工业大学）
Beyond Image-Text Matching: Verb Understanding in Multimodal Transformers Using Guided Masking
Faculty of Information Technology, Brno University of Technology（布尔诺理工大学信息技术学院）；Kempelen Institute of Intelligent Technologies（肯佩伦智能技术研究所）；George Mason University（乔治梅森大学）
Sun Yat-sen University（中山大学）；Pazhou Lab（琶洲实验室）；Anhui University（安徽大学）；DarkMatter AI Research（DarkMatter AI 研究院）
Crowd counting is a fundamental yet challenging task, which desires rich information to generate pixel-wise crowd density maps. However, most previous methods only used the limited information of RGB images and cannot well discover potential pedestrians in unconstrained scenarios. In this work, we find that incorporating optical and thermal information can greatly help to recognize pedestrians. To promote future researches in this field, we introduce a large-scale RGBT Crowd Counting (RGBT-CC) benchmark, which contains 2,030 pairs of RGB-thermal images with 138,389 annotated people. Furthermor…
Baidu Research（百度研究院）；German Aerospace Center（德国航空航天中心）；City University of Hong Kong（香港城市大学）；Northwestern Polytechnical University（西北工业大学）；Technical University of Munich（慕尼黑工业大学）
Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images. Albeit successful, vision-based crowd counting approaches could fail to capture informative features in extreme conditions, e.g., imaging at night and occlusion. In this work, we introduce a novel task of audiovisual crowd counting, in which visual and auditory information are integrated for counting purposes. We collect a large-scale benchmark, named auDiovISual Crowd cOunting (DISCO) dataset, consisting of 1,935 images and the corresponding audio clips, and 170,270 annotated instan…
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
Attention models are widely used in Vision-language (V-L) tasks to perform the visual-textual correlation. Humans perform such a correlation with a strong linguistic understanding of the visual world. However, even the best performing attention model in V-L tasks lacks such a high-level linguistic understanding, thus creating a semantic gap between the modalities. In this paper, we propose an attention mechanism - Linguistically-aware Attention (LAT) - that leverages object attributes obtained from generic object detectors along with pre-trained language models to reduce this semantic gap. LAT…
Hierarchical Alignment-enhanced Adaptive Grounding Network for Generalized Referring Expression Comprehension
School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；Institute of Big Data, Fudan University（复旦大学大数据学院）；Shanghai University of Finance and Economics（上海财经大学）；Nanyang Technological University（南洋理工大学）
Object Counting with GPT-4o and GPT-5: A Comparative Study
Software Engineering Institute, Obuda University, Budapest, Hungary（奥布达大学软件工程研究所）；Doctoral School of Applied Informatics（应用信息科学博士学院）；Applied Mathematics, Obuda University, Budapest, Hungary（应用数学，奥布达大学）；John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary（约翰·冯·诺伊曼信息学院，奥布达大学）；Faculty of Economics（经济学院）
Heidelberg University（海德堡大学）；University of Malta（马耳他大学）；New York University（纽约大学）；University of Amsterdam（阿姆斯特丹大学）
We investigate the reasoning ability of pretrained vision and language (V&L) models in two tasks that require multimodal integration: (1) discriminating a correct image-sentence pair from an incorrect one, and (2) counting entities in an image. We evaluate three pretrained V&L models on these tasks: ViLBERT, ViLBERT 12-in-1 and LXMERT, in zero-shot and finetuned settings. Our results show that models solve task (1) very well, as expected, since all models are pretrained on task (1). However, none of the pretrained V&L models is able to adequately solve task (2), our counting probe, and they ca…
A Transformer-based Multimodal Fusion Model for Efficient Crowd Counting Using Visual and Wireless Signals
School of Electrical and Electronic Engineering, University College Dublin（都柏林大学电子与电气工程学院）；College of Electrical Engineering and Automation, Shandong University of Science and Technology（山东科技大学电气工程与自动化学院）
Bootstrapping MLLM for Weakly-Supervised Class-Agnostic Object Counting
College of Electronic and Information Engineering, Tongji University（电子信息工程学院，同济大学）；College of Computer Science, Wuhan University（计算机科学学院，武汉大学）；College of Computer Science, Tongji University（计算机科学学院，同济大学）；State Key Laboratory of Autonomous Intelligent Unmanned Systems（自主智能无人系统国家重点实验室）
RGB-D Indiscernible Object Counting in Underwater Scenes
ETH Zurich（苏黎世联邦理工学院）；Pioneer Centre for Artificial Intelligence, University of Copenhagen（哥本哈根大学先锋人工智能中心）；School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications（南京邮电大学通信与信息工程学院）；College of Computer Science, Nankai University（南开大学计算机学院）
IBM Research, Almaden（IBM研究院阿尔马登分院）；University of Chicago（芝加哥大学）
Visual Question Answering (VQA) is a novel problem domain where multi-modal inputs must be processed in order to solve the task given in the form of a natural language. As the solutions inherently require to combine visual and natural language processing with abstract reasoning, the problem is considered as AI-complete. Recent advances indicate that using high-level, abstract facts extracted from the inputs might facilitate reasoning. Following that direction we decided to develop a solution combining state-of-the-art object detection and reasoning modules. The results, achieved on the well-ba…
Object Attribute Matters in Visual Question Answering
Jilin University（吉林大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）
