Scene graph generation (SGGen) is a challenging task due to a complex visual context of an image. Intuitively, the human visual system can volitionally focus on attended regions by salient stimuli associated with visual cues. For example, to infer the relationship between man and horse, the interaction between human leg and horseback can provide strong visual evidence to predict the predicate ride. Besides, the attended region face can also help to determine the object man. Till now, most of the existing works studied the SGGen by extracting coarse-grained bounding box features while understanding fine-grained visual regions received limited attention. To mitigate the drawback, this article proposes a region-aware attention learning method. The key idea is to explicitly construct the attention space to explore salient regions with the object and predicate inferences. First, we extract a set of regions in an image with the standard detection pipeline. Each region regresses to an object. Second, we propose the object-wise attention graph neural network (GNN), which incorporates attention modules into the graph structure to discover attended regions for object inference. Third, we build the predicate-wise co-attention GNN to jointly highlight subject's and object's attended regions for predicate inference. Particularly, each subject-object pair is connected with one of the latent predicates to construct one triplet. The proposed intra-triplet and inter-triplet learning mechanism can help discover the pair-wise attended regions to infer predicates. Extensive experiments on two popular benchmarks demonstrate the superiority of the proposed method. Additional ablation studies and visualization further validate its effectiveness.

Knowledge-based Visual Question Answering (KB-VQA) aims to answer the image-aware question via the external knowledge, which requires an agent to not only understand images but also explicitly retrieve and integrate knowledge facts. Intuitively, to accurately answer the question, we humans can validate the retrieved knowledge based on our memory, and then align the knowledge facts with the image regions to infer answers. However, most existing methods ignore the process of knowledge validation and alignment. In this paper, we propose the Multi-Modal Validation and Domain Interaction Learning method, which consists of two components: 1) Multi-modal validation for knowledge retrieval. We propose the multi-modal validation module (MMV) to evaluate the confidence of each retrieved knowledge fact via images and questions, which preserves knowledge candidates effective for inferring answers. 2) Domain interaction for knowledge integration. We propose the Domain Interaction TRansformer module (DI-TR) to align visual regions with knowledge facts by the interaction learning in the improved transformer. Specifically, the inter-domain and intra-domain masks are injected into each self-attention layer to control the integration scope. The proposed method outperforms several strong baselines on three widely-used knowledge-based datasets: KRVQA, OK-VQA and VQA2.0. Extensive experiments and ablation studies demonstrate the effectiveness of multi-modal knowledge validation and domain interaction learning.

Visual Dialog (VD) requires an agent to answer the current question by engaging in a conversation with humans referring to an image. Despite the recent progress, it is beneficial to introduce external commonsense knowledge to fully understand the given image and dialog history. However, the existing knowledge-based VD models are inclined to rely on severe learning bias brought by commonsense, e.g., the retrieved $\lt {\mathtt{bus}}, {\mathtt{capable\;of}}, {\mathtt{transport\;people}}\gt $ , $\lt {\mathtt{bus}}, {\mathtt{is\;a}}, {\mathtt{public\;transport}}\gt $ , and $\lt {\mathtt{bus}}, {\mathtt{is\;a}}, {\mathtt{car}}\gt $ can induce a spurious correlation between the question “What is the bus used for?” and the false answer “City bus”. There are two challenges to make commonsense learning more robust against spurious correlations: 1) how to disentangle the true effect of “good” commonsense knowledge from the whole, and 2) how to estimate and remove the effect of “bad” commonsense bias on answers. In this paper, we propose a novel CounterFactual Commonsense learning scheme for the Visual Dialog task (CFC-VD). First, comparing with the causal graph of existing VD models, we add one new commonsense node and one new link to multi-modal information from history, question, and image. Since the retrieved knowledge prior is subtle and uncontrollable, we consider it as an unobserved confounder in the commonsense node, which leads to spurious correlations for the answer inference. Then, to remove the effect of the confounder, we formulate it as the direct causal effect of commonsense on answers and remove the direct language effect by subtracting it from the total causal effect via counterfactual reasoning. Experimental results certify the effectiveness of our method on the prevailing Visdial v0.9 and Visdial v1.0 datasets.

Image captioning is a fundamental task in computer vision that aims to generate precise and comprehensive descriptions of images automatically. Intuitively, humans initially rely on the image content, e.g., “cake on a plate”, to gradually gather relevant knowledge facts e.g., “birthday party”, “candles”, which is a process referred to as divergence. Then, we perform step-by-step reasoning based on the images to refine, and rearrange these knowledge facts for explicit sentence generation, a process referred to as focus. However, existing image captioning methods mainly rely on the encode-decode framework that does not well fit the “divergence-focus” nature of the task. To this end, we propose the knowledge “divergence-focus” method for Image Captioning (K-DFIC) to gather and polish knowledge facts for image understanding, which consists of two components: (a) Knowledge Divergence Module aims to leverage the divergence capability of large-scale pre-trained model to acquire knowledge facts relevant to the image content. To achieve this, we design a scene-graph-aware prompt that serves as a “trigger” for GPT-3.5, encouraging it to “diverge” and generate more sophisticated, human-like knowledge. (b) Knowledge Focus Module aims to refine acquired knowledge facts and further rearrange them in a coherent manner. We design the interactive refining network to encode knowledge, which is refined with the visual features to remove irrelevant words. Then, to generate fluent image descriptions, we design the large-scale pre-trained model-based rearrangement method to estimate the importance of each knowledge word for an image. Finally, we fuse the refined knowledge and visual features to assist the decoder in generating captions. We demonstrate the superiority of our approach through extensive experiments on the MSCOCO dataset. Our approach surpasses state-of-the-art performance across all metrics in the Karpathy split. For example, our model obtains the best CIDEr-D score of 148.4%. Additional ablation studies and visualization further validate our effectiveness.

Knowledge-based Scene Graph Generation (SGG) requires external commonsense knowledge beyond the visual scene to infer the relation between objects. Such knowledge can be obtained in a variety of forms, such as vision, text, and graph. However, there are two drawbacks as follows: 1) Commonsense knowledge essentially has uncertainty, but current works usually represent knowledge in a deterministic manner, which is not well matched to its nature. 2) Using commonsense knowledge without denoising will introduce irrelevant information. This can increase the burden on the relation classifier and only obtain marginal gains over a large amount of data. In this paper, we propose a novel Gaussian distribution-aware commonsense knowledge learning method for SGG. First, we associate each object pair with a Gaussian distribution, which parametrizes visual context and commonsense as mean and variance, respectively. We prove that Gaussian modeling can provide a probabilistic soft space to measure the uncertainty of external knowledge, which allows diverse predictions. Second, to reduce semantic noise in commonsense, we sample multiple variables from the Gaussian distribution and train multi-expert classifiers, which can be dynamically examined for the ensemble softmax classification. Extensive comparative experiments on two benchmarks confirm that our method can achieve competitive performance against the state-of-the-art. Ablation studies verify the essential roles of individual components. Moreover, the visualization of multi-expert classifiers confirms our ability to integrate commonsense for relation inference.

Knowledge-based visual question answering (KBVQA) aims to retrieve the external knowledge out of images to answer questions. However, current methods always introduce various irrelevant knowledge due to two drawbacks: (1) Synonymy issue. Existing methods heavily rely on words from questions or object labels in images to match knowledge from databases, which disregards the same word may hold multiple meanings within different contexts. (2) Knowledge uncertainty issue. Due to the absence of supervisory signals, recent methods can not determine which knowledge is applicable for answer inference, which can mislead to admit useless knowledge. To address these two problems, we propose to supervise the process of knowledge retrieval over a tree structure for KB-VQA task. For the synonymy issue, we construct a hierarchical knowledge tree to capture the subordination information between knowledge facts, mitigating the impact of synonyms on knowledge retrieval. For the knowledge uncertainty issue, we use the retrieval history as the ground truth to supervise the knowledge retrieval, which facilitates the QA model to form an explicit path of knowledge facts for answer understanding. Finally, we integrate the image, question, and retrieved knowledge into a variant of transformer to predict answers. Experimental results validate the effectiveness of the proposed method on KR-VQA, OK-VQA and VQA v2 datasets.