Prompt-based Learning for Unpaired Image Captioning
Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Peng Cheng Laboratory（鹏城实验室）；Anhui University（安徽大学）；Beijing Institute of Technology（北京理工大学）；Sun Yat-Sen University（中山大学）；The Hong Kong Polytechnic University（香港理工大学）
Unpaired Image Captioning (UIC) has been developed to learn image descriptions from unaligned vision-language sample pairs. Existing works usually tackle this task using adversarial learning and visual concept reward based on reinforcement learning. However, these existing works were only able to learn limited cross-domain information in vision and language domains, which restrains the captioning performance of UIC. Inspired by the success of Vision-Language Pre-Trained Models (VL-PTMs) in this research, we attempt to infer the cross-domain cue information about a given image from the large VL-PTMs for the UIC task. This research is also motivated by recent successes of prompt learning in many downstream multi-modal tasks, including image-text retrieval and vision question answering. In this work, a semantic prompt is introduced and aggregated with visual features for more accurate caption prediction under the adversarial learning framework. In addition, a metric prompt is designed to select high-quality pseudo image-caption samples obtained from the basic captioning model and refine the model in an iterative manner. Extensive experiments on the COCO and Flickr30K datasets validate the promising captioning ability of the proposed model. We expect that the proposed prompt-based UIC model will stimulate a new line of research for the VL-PTMs based captioning.
Nanyang Technological University（南洋理工大学）；Alibaba AI Labs（阿里巴巴人工智能实验室）
Image captioning is a multimodal task involving computer vision and natural language processing, where the goal is to learn a mapping from the image to its natural language description. In general, the mapping function is learned from a training set of image-caption pairs. However, for some language, large scale image-caption paired corpus might not be available. We present an approach to this unpaired image captioning problem by language pivoting. Our method can effectively capture the characteristics of an image captioner from the pivot language (Chinese) and align it to the target language …
Nanyang Technological University（南洋理工大学）；Salesforce Research Asia（Salesforce亚洲研究院）；Monash University（莫纳什大学）；Adobe Research（Adobe研究院）；Alibaba Group（阿里巴巴集团）
Most of current image captioning models heavily rely on paired image-caption datasets. However, getting large scale image-caption paired data is labor-intensive and time-consuming. In this paper, we present a scene graph-based approach for unpaired image captioning. Our framework comprises an image scene graph generator, a sentence scene graph generator, a scene graph encoder, and a sentence decoder. Specifically, we first train the scene graph encoder and the sentence decoder on the text modality. To align the scene graphs between images and sentences, we propose an unsupervised feature align…
Peking University（北京大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Foreign Studies University（北京外国语大学）
Recently, image captioning has aroused great interest in both academic and industrial worlds. Most existing systems are built upon large-scale datasets consisting of image-sentence pairs, which, however, are time-consuming to construct. In addition, even for the most advanced image captioning systems, it is still difficult to realize deep image understanding. In this work, we achieve unpaired image captioning by bridging the vision and the language domains with high-level semantic information. The motivation stems from the fact that the semantic concepts with the same modality can be extracted…
Training supervised video captioning model requires coupled video-caption pairs. However, for many targeted languages, sufficient paired data are not available. To this end, we introduce the unpaired video captioning task aiming to train models without coupled video-caption pairs in target language. To solve the task, a natural choice is to employ a two-step pipeline system: first utilizing video-to-pivot captioning model to generate captions in pivot language and then utilizing pivot-to-target translation model to translate the pivot captions to the target language. However, in such a pipelin…
We present a novel data-efficient semi-supervised framework to improve the generalization of image captioning models. Constructing a large-scale labeled image captioning dataset is an expensive task in terms of labor, time, and cost. In contrast to manually annotating all the training samples, separately collecting uni-modal datasets is immensely easier, e.g., a large-scale image dataset and a sentence dataset. We leverage such massive unpaired image and caption data upon standard paired data by learning to associate them. To this end, our proposed semi-supervised learning method assigns pseud…
Despite the remarkable progress of image captioning, existing captioners typically lack the controllable capability to generate desired image captions, e.g., describing the image in a rough or detailed manner, in a factual or emotional view, etc. In this paper, we show that a unified model is qualified to perform well in diverse domains and freely switch among multiple styles. Such a controllable capability is achieved by embedding the prompt learning into the image captioning framework. To be specific, we design a set of prompts to fine-tune the pre-trained image captioner. These prompts allo…
The Chinese University of Hong Kong (Shen Zhen)（香港中文大学（深圳））；Peng Cheng Laboratory（鹏城实验室）；SUN YAT-SEN UNIVERSITY（中山大学）；Wuhan University（武汉大学）；The Hong Kong Polytechnic University（香港理工大学）
The goal of unpaired image captioning (UIC) is to describe images without using image-caption pairs in the training phase. Although challenging, we except the task can be accomplished by leveraging a training set of images aligned with visual concepts. Most existing studies use off-the-shelf algorithms to obtain the visual concepts because the Bounding Box (BBox) labels or relationship-triplet labels used for the training are expensive to acquire. In order to resolve the problem in expensive annotations, we propose a novel approach to achieve cost-effective UIC. Specifically, we adopt image-le…
Player-Centric Multimodal Prompt Generation for Large Language Model Based Identity-Aware Basketball Video Captioning
Beijing University of Technology（北京理工大学）；Shanghai Jiao Tong University（上海交通大学）；Chinese Academy of Sciences（中国科学院）；The Hong Kong Polytechnic University（香港理工大学）
We introduce a zero-shot video captioning method that employs two frozen networks: the GPT-2 language model and the CLIP image-text matching model. The matching score is used to steer the language model toward generating a sentence that has a high average matching score to a subset of the video frames. Unlike zero-shot image captioning methods, our work considers the entire sentence at once. This is achieved by optimizing, during the generation process, part of the prompt from scratch, by modifying the representation of all other tokens in the prompt, and by repeating the process iteratively, …
AVC-DPO: Aligned Video Captioning via Direct Preference Optimization
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院）；College of Artificial Intelligence, Nankai University（南开大学人工智能学院）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）
RACap: Relation-Aware Prompting for Lightweight Retrieval-Augmented Image Captioning
School of biological science and medical engineering, Southeast University, China（生物科学与医学工程学院，东南大学，中国）；Department of Neurosurgery, The second affiliated hospital of Xinjiang Medical University, China（神经外科部门，新疆医科大学第二附属医院，中国）
This paper presents a Keyword-driven and N-gram Graph based approach for Image Captioning (KENGIC). Most current state-of-the-art image caption generators are trained end-to-end on large scale paired image-caption datasets which are very laborious and expensive to collect. Such models are limited in terms of their explainability and their applicability across different domains. To address these limitations, a simple model based on N-Gram graphs which does not require any end-to-end training on paired image captions is proposed. Starting with a set of image keywords considered as nodes, the gen…
DualCap: Enhancing Lightweight Image Captioning via Dual Retrieval with Similar Scenes Visual Prompts
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）
RxnCaption: Reformulating Reaction Diagram Parsing as Visual Prompt Guided Captioning
Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Beihang University（北航）；Peking University（北京大学）；South China Normal University（华南师范大学）；Northwestern Polytechnical University（西北工业大学）
The Devil is in the Distributions: Explicit Modeling of Scene Content is Key in Zero-Shot Video Captioning
School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；Macquarie University（麦考瑞大学）；Australian Institute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）
BTCChat: Advancing Remote Sensing Bi-temporal Change Captioning with Multimodal Large Language Model
State Key Laboratory of Networking and Switching Technology（网络与交换技术国家重点实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）；Aerospace Information Research Institute（航天信息研究所）；Chinese Academy of Sciences（中国科学院）；School of Geographical Sciences（地理科学学院）；Hunan Normal University（湖南师范大学）
INESC-ID（葡萄牙国家系统与计算机工程研究所）；Instituto Superior Técnico（高等技术学院）；University of Lisbon（里斯本大学）；University of Copenhagen（哥本哈根大学）；Pioneer Center for AI（先锋人工智能中心）
Recent advances in image captioning have focused on scaling the data and model size, substantially increasing the cost of pre-training and finetuning. As an alternative to large models, we present SmallCap, which generates a caption conditioned on an input image and related captions retrieved from a datastore. Our model is lightweight and fast to train, as the only learned parameters are in newly introduced cross-attention layers between a pre-trained CLIP encoder and GPT-2 decoder. SmallCap can transfer to new domains without additional finetuning and can exploit large-scale data in a trainin…
Controllable Image Captioning with Prompt-Conditioned Scene Rewards
POSTECH（浦项科技大学）；Graduate School of Artificial Intelligence, POSTECH（浦项科技大学人工智能研究生院）；Department of Computer Science and Engineering, POSTECH（浦项科技大学计算机科学与工程系）
IntentVCNet: Bridging Spatio-Temporal Gaps for Intention-Oriented Controllable Video Captioning
University of Science and Technology of China（中国科学技术大学）；Hefei Institutes of Physical Science,Chinese Academy of Sciences（合肥物理研究所，中国科学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究所，合肥综合性国家科学中心）；State Key Lab. for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；University of Chicago（芝加哥大学）；National University of Defense Technology（国防科技大学）；Harbin Institute of Technology（哈尔滨工业大学）
Northwestern Polytechnical University（西北工业大学）；Center for OPTical IMagery Analysis and Learning (OPTIMAL)（光学影像分析与学习中心（OPTIMAL））
The spatial attention is a straightforward approach to enhance the performance for remote sensing image captioning. However, conventional spatial attention approaches consider only the attention distribution on one fixed coarse grid, resulting in the semantics of tiny objects can be easily ignored or disturbed during the visual feature extraction. Worse still, the fixed semantic level of conventional spatial attention limits the image understanding in different levels and perspectives, which is critical for tackling the huge diversity in remote sensing images. To address these issues, we propo…
Zhejiang University（浙江大学）；National University of Singapore（新加坡国立大学）；Ningbo University（宁波大学）
Text-based image captioning (TextCap) requires simultaneous comprehension of visual content and reading the text of images to generate a natural language description. Although a task can teach machines to understand the complex human environment further given that text is omnipresent in our daily surroundings, it poses additional challenges in normal captioning. A text-based image intuitively contains abundant and complex multimodal relational content, that is, image details can be described diversely from multiview rather than a single caption. Certainly, we can introduce additional paired tr…
Transferable Decoding with Visual Entities for Zero-Shot Image Captioning
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Tencent（腾讯）；Shanghai Jiao Tong University（上海交通大学）；Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Meta AI
Image captioning is a longstanding problem in the field of computer vision and natural language processing. To date, researchers have produced impressive state-of-the-art performance in the age of deep learning. Most of these state-of-the-art, however, requires large volume of annotated image-caption pairs in order to train their models. When given an image dataset of interests, practitioner needs to annotate the caption for each image in the training set and this process needs to happen for each newly collected image dataset. In this paper, we explore the task of unsupervised image captioning…
CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Due to the rapid emergence of short videos and the requirement for content understanding and creation, the video captioning task has received increasing attention in recent years. In this paper, we convert traditional video captioning task into a new paradigm, \ie, Open-book Video Captioning, which generates natural language under the prompts of video-content-relevant sentences, not limited to the video itself. To address the open-book video captioning problem, we propose a novel Retrieve-Copy-Generate network, where a pluggable video-to-text retriever is constructed to retrieve sentences as h…
Generating image descriptions in different languages is essential to satisfy users worldwide. However, it is prohibitively expensive to collect large-scale paired image-caption dataset for every target language which is critical for training descent image captioning models. Previous works tackle the unpaired cross-lingual image captioning problem through a pivot language, which is with the help of paired image-caption data in the pivot language and pivot-to-target machine translation models. However, such language-pivoted approach suffers from inaccuracy brought by the pivot-to-target translat…
