The goal of the YouMakeup VQA Challenge 2020 is to provide a common benchmark for fine-grained action understanding in domain-specific videos e.g. makeup instructional videos. We propose two novel question-answering tasks to evaluate models' fine-grained action understanding abilities. The first task is \textbf{Facial Image Ordering}, which aims to understand visual effects of different actions expressed in natural language to the facial object. The second task is \textbf{Step Ordering}, which aims to measure cross-modal semantic alignments between untrimmed videos and multi-sentence texts. In this paper, we present the challenge guidelines, the dataset used, and performances of baseline models on the two proposed tasks. The baseline codes and models are released at \url{https://github.com/AIM3-RUC/YouMakeup_Baseline}.
Pix2Fact: When Vision Is Not Enough -- Benchmarking Fine-Grained VQA with Web Verification on High-Resolution Real-World Scenes
GADE Union (Global AI Data Experts Union)（GADE联盟（全球人工智能数据专家联盟））；Shanghai Jiao Tong University（上海交通大学）；Nanyang Technological University（南洋理工大学）；New York University（纽约大学）；Cambridge University（剑桥大学）；The University of Hong Kong（香港大学）
Wright State University（莱特州立大学）；NEC Laboratories America（美国电气股份有限公司实验室）
Existing visual reasoning datasets such as Visual Question Answering (VQA), often suffer from biases conditioned on the question, image or answer distributions. The recently proposed CLEVR dataset addresses these limitations and requires fine-grained reasoning but the dataset is synthetic and consists of similar objects and sentence structures across the dataset. In this paper, we introduce a new inference task, Visual Entailment (VE) - consisting of image-sentence pairs whereby a premise is defined by an image, rather than a natural language sentence as in traditional Textual Entailment tasks…
M$^3$-VQA: A Benchmark for Multimodal, Multi-Entity, Multi-Hop Visual Question Answering
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Hangzhou Dianzi University（杭州电子科技大学）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；The University of Sydney（悉尼大学）
Visual question answering (VQA) is challenging because it requires a simultaneous understanding of both the visual content of images and the textual content of questions. The approaches used to represent the images and questions in a fine-grained manner and questions and to fuse these multi-modal features play key roles in performance. Bilinear pooling based models have been shown to outperform traditional linear models for VQA, but their high-dimensional representations and high computational complexity may seriously limit their applicability in practice. For multi-modal feature fusion, here …
We introduce CARETS, a systematic test suite to measure consistency and robustness of modern VQA models through a series of six fine-grained capability tests. In contrast to existing VQA test sets, CARETS features balanced question generation to create pairs of instances to test models, with each pair focusing on a specific capability such as rephrasing, logical symmetry or image obfuscation. We evaluate six modern VQA systems on CARETS and identify several actionable weaknesses in model comprehension, especially with concepts such as negation, disjunction, or hypernym invariance. Interestingl…
The use of complex attention modules has improved the performance of the Visual Question Answering (VQA) task. This work aims to learn an improved multi-modal representation through dense interaction of visual and textual modalities. The proposed model has an attention block containing both self-attention and co-attention on image and text. The self-attention modules provide the contextual information of objects (for an image) and words (for a question) that are crucial for inferring an answer. On the other hand, co-attention aids the interaction of image and text. Further, fine-grained inform…
University of Sydney（悉尼大学）；JD Explore Academy（京东探索研究院）；DATA61, CSIRO（联邦科学与工业研究组织数据61研究所）
Multi-modal reasoning in visual question answering (VQA) has witnessed rapid progress recently. However, most reasoning models heavily rely on shortcuts learned from training data, which prevents their usage in challenging real-world scenarios. In this paper, we propose a simple but effective cross-modal contrastive learning strategy to get rid of the shortcut reasoning caused by imbalanced annotations and improve the overall performance. Different from existing contrastive learning with complex negative categories on coarse (Image, Question, Answer) triplet level, we leverage the corresponden…
Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion
School of Artificial Intelligence, Tiangong University（天津工业大学人工智能学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Linying Medical Technology (Shenzhen) Co., Ltd.（临影医疗科技（深圳）有限公司）
Seeing Clearly without Training: Mitigating Hallucinations in Multimodal LLMs for Remote Sensing
School of Computer Science, Wuhan University, China（武汉大学计算机学院）；Zhongguancun Academy, China（中关村学院）；School of Computer Science, Chongqing University, China（重庆大学计算机学院）
Hangzhou Dianzi University（杭州电子科技大学）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；University of Sydney（悉尼大学）
Visual question answering (VQA) is challenging because it requires a simultaneous understanding of both visual content of images and textual content of questions. To support the VQA task, we need to find good solutions for the following three issues: 1) fine-grained feature representations for both the image and the question; 2) multi-modal feature fusion that is able to capture the complex interactions between multi-modal features; 3) automatic answer prediction that is able to consider the complex correlations between multiple diverse answers for the same question. For fine-grained image and…
Spatial reasoning poses a particular challenge for intelligent agents and is at the same time a prerequisite for their successful interaction and communication in the physical world. One such reasoning task is to describe the position of a target object with respect to the intrinsic orientation of some reference object via relative directions. In this paper, we introduce GRiD-A-3D, a novel diagnostic visual question-answering (VQA) dataset based on abstract objects. Our dataset allows for a fine-grained analysis of end-to-end VQA models' capabilities to ground relative directions. At the same …
The problem of realistic VQA (RVQA), where a model has to reject unanswerable questions (UQs) and answer answerable ones (AQs), is studied. We first point out 2 drawbacks in current RVQA research, where (1) datasets contain too many unchallenging UQs and (2) a large number of annotated UQs are required for training. To resolve the first drawback, we propose a new testing dataset, RGQA, which combines AQs from an existing VQA dataset with around 29K human-annotated UQs. These UQs consist of both fine-grained and coarse-grained image-question pairs generated with 2 approaches: CLIP-based and Per…
RadImageNet-VQA: A Large-Scale CT and MRI Dataset for Radiologic Visual Question Answering
Raidium；Université de Paris Cité, Hôpital Européen Georges Pompidou, AP-HP（巴黎西岱大学，乔治·蓬皮杜欧洲医院，AP-HP）；Department of Vascular and Oncological Interventional Radiology, INSERM（血管与肿瘤介入放射学系，法国国家健康与医学研究院）
SAP SE（SAP公司）；TU Kaiserslautern（凯泽斯劳滕工业大学）；CIMeC - University of Trento（特伦托大学认知与脑科学中心）；DFKI（德国人工智能研究中心）
We introduce MASSES, a simple evaluation metric for the task of Visual Question Answering (VQA). In its standard form, the VQA task is operationalized as follows: Given an image and an open-ended question in natural language, systems are required to provide a suitable answer. Currently, model performance is evaluated by means of a somehow simplistic metric: If the predicted answer is chosen by at least 3 human annotators out of 10, then it is 100% correct. Though intuitively valuable, this metric has some important limitations. First, it ignores whether the predicted answer is the one selected…
Hangzhou Dianzi University（杭州电子科技大学）；University of Sydney（悉尼大学）；Huawei（华为）
Visual Question Answering (VQA) requires a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions. Therefore, designing an effective `co-attention' model to associate key words in questions with key objects in images is central to VQA performance. So far, most successful attempts at co-attention learning have been achieved by using shallow models, and deep co-attention models show little improvement over their shallow counterparts. In this paper, we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Atte…
Rochester Institute of Technology（罗切斯特理工学院）；Adobe Research（Adobe 研究院）；Paige；Cornell Tech（康奈尔科技校区）
Chart question answering (CQA) is a newly proposed visual question answering (VQA) task where an algorithm must answer questions about data visualizations, e.g. bar charts, pie charts, and line graphs. CQA requires capabilities that natural-image VQA algorithms lack: fine-grained measurements, optical character recognition, and handling out-of-vocabulary words in both questions and answers. Without modifications, state-of-the-art VQA algorithms perform poorly on this task. Here, we propose a novel CQA algorithm called parallel recurrent fusion of image and language (PReFIL). PReFIL first learn…
FaceBench: A Multi-View Multi-Level Facial Attribute VQA Dataset for Benchmarking Face Perception MLLMs
Shenzhen University（深圳大学）；Tsinghua University（清华大学）；The Hong Kong Polytechnic University（香港理工大学）；City University of Hong Kong（香港城市大学）；Sun Yat-sen University（中山大学）
Shandong University（山东大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Illinois Institute of Technology（伊利诺伊理工学院）
Several studies have recently pointed that existing Visual Question Answering (VQA) models heavily suffer from the language prior problem, which refers to capturing superficial statistical correlations between the question type and the answer whereas ignoring the image contents. Numerous efforts have been dedicated to strengthen the image dependency by creating the delicate models or introducing the extra visual annotations. However, these methods cannot sufficiently explore how the visual cues explicitly affect the learned answer representation, which is vital for language reliance alleviatio…
EyeSim-VQA: A Free-Energy-Guided Eye Simulation Framework for Video Quality Assessment
State Key Laboratory of Integrated Services Networks, School of Electronic Engineering, Xidian University（信息服务网络国家重点实验室，电子工程学院，西安电子科技大学）；Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University（协同智能系统重点实验室，教育部，西安电子科技大学）；College of Mathematical Science, Inner Mongolia Normal University（数学科学学院，内蒙古师范大学）；School of Electronic Engineering, Xidian University（电子工程学院，西安电子科技大学）
Location-Aware Pretraining for Medical Difference Visual Question Answering
Department of Electrical and Computer Engineering, Carnegie Mellon University, Kigali, Rwanda（电气与计算机工程系，卡内基梅隆大学，刚果（金）基利齐）；University of Melbourne, Melbourne, Australia（墨尔本大学，墨尔本，澳大利亚）
Université de Lyon（里昂大学）；INSA-Lyon（里昂国立应用科学学院）；Orange Labs（Orange实验室）
The large adoption of the self-attention (i.e. transformer model) and BERT-like training principles has recently resulted in a number of high performing models on a large panoply of vision-and-language problems (such as Visual Question Answering (VQA), image retrieval, etc.). In this paper we claim that these State-Of-The-Art (SOTA) approaches perform reasonably well in structuring information inside a single modality but, despite their impressive performances , they tend to struggle to identify fine-grained inter-modality relationships. Indeed, such relations are frequently assumed to be impl…
