Why Does the VQA Model Answer No?: Improving Reasoning through Visual and Linguistic Inference

1 INTRODUCTION . Recently , the interpretability and reliability of Artificial Intelligence ( AI ) are being seriously discussed ( Kim et al. , 2018b ) , since the opacity of its internal structure makes it hard to figure out the exact reason when it malfunctions ( Gunning , 2017 ; Kurakin et al. , 2016 ) . Previous works show that decision process of conventional machine learning models ( e.g. , decision tree ) can be modified to be transparent while maintaining their original performance ( Si & Zhu , 2013 ; Letham et al. , 2015 ) . For deep learning models , Ribeiro et al . ( 2016 ) suggest a method for reasoning their decisions by visualizing critical parts of the input for the image classification . However , it is harder to infer the decision process for more complicated tasks such as VQA . VQA is one of the most active research fields in AI with strong expectation for real applications . It can be used for either assisting radiologists ( Letham et al. , 2015 ) or help visually impaired people to understand their environment more flexibly ( Antol et al. , 2015 ) . Therefore , state-of-the-art VQA models have been continually proposed , but their internal processes are yet opaque . Displaying attended region might be used for indirect reasoning . However , it has limitation on describing what the model perceives in detail . The most spotlighted recent research for endowing interpretability to VQA model is making the model to produce an answer and its explanation at the same time ( Park et al. , 2018 ; Li et al. , 2018 ; Hudson & Manning , 2019 ) . To make it possible , they provide a ground truth explanation dataset for each image , question and answer pairs , and fine-tune the model to generate explanations in an end-to-end process . Previous explanation models can successfully describe relationships between objects in the image . However , when the answer to the question is “ no , ” the previous methods have difficulty on providing logical bases . Also , even though there are questions that require common sense , existing models do not utilize it : previous models memorize explanations appropriate to each situation in training datasets ( Park et al. , 2018 ; Zellers et al. , 2019 ) . In this paper , we propose a novel method that generates explanations for a VQA model ’ s answer . First , the proposed method selects keywords that play an essential role in inferring the answer to the question . We extract these keywords in the same way as Wang et al . ( 2019 ) . Next , given a pre-trained explanation generator , we use the proposed Variable-Constrained Beam Search ( VCBS ) algorithm to generate a phrase that best describes the relationship between the keywords by inferring the image ( See Fig . 1 ) . The phrase is fed to the generator to complete the remaining part of the explanation . Finally , we use Natural Language Inference ( NLI ) ( Wang et al. , 2019 ; Talman et al. , 2019 ) to check whether the contents of the question can be inferred from the explanation using common sense . For the overview of our method , please refer to Fig . 4 . Our main contributions are : • We propose the novel method to generate textual explanations for the VQA model ’ s answer . The proposed method not only produces more appropriate explanations ( Kim et al. , 2018b ) but can also be used as a post-processing of the VQA model to boost the performance . We validate our method on one hundred randomly sampled data from VQA 2.0 test-std split , through a user study conducted in MTurk . By modifying the predicted answer using the generated explanation , we also compare the prediction accuracy of the method on complete VQA 2.0 test-std split . Our proposed method has gained a MOS score higher than previous methods , and get a VQA performance improvement of about 1.1 % . • We propose the Variable-Constrained Beam Search ( VCBS ) algorithm that helps to identify the visual relationship between objects and attributes of interest in the image . Unlike the previous Constrained Beam Search ( CBS ) algorithm ( Sipser et al. , 2006 ) , the VCBS has the variable accepting states . The variable accepting states help to satisfy the given constraint as much as possible but allow the final beam to exclude some constraints if necessary . 2 RELATED WORKS . We first summarize recent research in VQA . Then we review the explanatory model for VQA and discuss the distinguishing characteristics of our work . Visual Question Answering : The VQA challenge was first launched in 2016 , and representative studies at that time ( Fukui et al. , 2016 ; Lu et al. , 2016 ; Kim et al. , 2017 ) first extract image features using residual networks ( He et al. , 2016 ) and established joint learning through question features and attention obtained from gated recurrent units ( GRUs ) . In the VQA 2017 challenge , Anderson et al . ( 2018b ) proposed a method that exploits Faster-RCNN ( Ren et al. , 2015 ) to extract image features for the attended object candidates . Following its scheme , most of the subsequent VQA studies also used Faster-RCNN . In the VQA 2018 challenge , Kim et al . ( 2018a ) presented bi-linear attention so that both question features and visual features were considered together when generating attention . They have shown that bilinear attention improves the residuality of the attention presented in ( Kim et al. , 2016 ) successfully . Although various attention-based VQA models have shown excellent performance , the models heavily rely on linguistic bias rather than visual information in most cases ( Ramakrishnan et al. , 2018 ) . For this reason , recent studies are interested in identifying relationships among objects in images and using them to predict answers ( Hudson & Manning , 2019 ) . Explanatory model for VQA : Visualizing attention ( Lu et al. , 2016 ) or utilizing Grad-CAM ( Selvaraju et al. , 2017 ) is one of the most widespread approach to understand the decision process of the VQA model . However , as mentioned in Li et al . ( 2018 ) , we need detailed textual explanations rather than merely visualizing these areas . For this reason , recent research not only released ground truth explanation datasets ( VQA-X ( Park et al. , 2018 ) and VQA-E ( Li et al. , 2018 ) ) but also proposed models that generates an answer and its textual explanation at the same time . While VQA-X is made up of more elaborate and detailed explanations , VQA-E is composed of more extensive training and validation data . According to the results of the Li et al . ( 2018 ) , fine-tuning through the VQA-E dataset increases the prediction accuracies of VQA models noticeably . While both of the models are good at justifying the answer for the given question and image , they are poor at providing introspective explanations of the VQA model . First , the PJ-X model proposed by Park et al . ( 2018 ) consists of the sequence of a VQA model and an explanatory model . Since the PJ-X does not refer to the attention generated in the VQA model , the explanation does not reflect the decision process of the target VQA model . On the other hand , the multi-task VQA-E model proposed by Li et al . ( 2018 ) directly utilize the attention distribution of pre-trained VQA models . Although the model generates explanations based on visual information about the attended area , it often produces sentences that are not related to the question . This is because multi-task VQA-E does not directly specify the relationship between the objects related to the question . Since it is important for VQA task to grasp the relationship between specific objects and attributes in an image , recent studies infer an answer through a scene graph or a modular network formed for the image ( Hudson & Manning , 2019 ; Hu et al. , 2018 ) . Although both of them provides explanatory insights into the model , the methods lack a logical basis for the explanation in situations where the answer is “ no. ” ( See Fig . 3 ) . Natural Language Inference : The goal of NLI is to classify the relationship between two sentences : “ premise ” and “ hypothesis. ” If the “ hypothesis ” can be inferred from the “ premise , ” we classify the relationship between the two sentences as “ entailment. ” While most NLI models use LSTM to classify the relationship between the two sentences ( Talman et al. , 2019 ) , Wang et al . ( 2019 ) proposed a method to grasp the relation by using ConceptNet ( Speer et al. , 2017 ) ( a common sense knowledge graph obtained through crowd-sourcing ) as external knowledge . We use state-ofart NLI models ( the HBMP ( Talman et al. , 2019 ) , and the ConSeqNet ( Wang et al. , 2019 ) ) to identify if the question could be answered using common sense and our explanation ( See Fig . 4 ( b ) ) . 3 APPROACH . For an input image , the Recurrent Neural Network ( RNN ) in the captioner forms the Variable Order Markov ( VOM ) model between words ( Begleiter et al. , 2004 ) . This VOM model can be represented as a directed graph G = ( Σ , E ) , where Σ is the complete vocabulary , and E is probability distribution at each node ( See Fig . 1 ) . If the captioner is ideal , the goal of VQA can be regarded as ( i ) checking whether a path that contains specific nodes can be found ( for the question with yes/no answers . See Fig . 1 ) ; ( ii ) or completing the path that pass through the specific nodes ( for the other type of questions . ( Lin et al. , 2019 ) ) . Algorithm 1 Variable-Constrained Beam Search decoding 1 : procedure VCBS ( Θ , b , T , C , A = ( ∑ , S , s0 , fSF ( p ) , δ ) ) 2 : M ← φ . for Memoization 3 : for s ∈ S do 4 : Bs ← { } if s = s0 else φ 5 : for t← 1 to T do 6 : Es ← ⋃ s′∈S { ( y′ , w ) |y′ ∈ Bs ′ , w ∈ ∑ , δ ( s′ , w ) = s } 7 : Bs ← arg maxE′⊂ES , |E′|=b ∑ yE′∈E′ Θ ( y E′ ) . Beam extension 8 : if s ∈ fSF ( p ) then . for Beams in accepting states 9 : M ← arg maxyM∪Bs∈M∪Bs |C ∩ yM∪B s | . keep beams satisfying more constraints 10 : M ← arg maxM ′⊂M , |M ′|=1 ∑ yM′∈M ′ Θ ( y M ′ ) . only keeps the best beam 11 : return y ∈M if M 6= φ else φ 3.1 CONSTRAINTS . First , we identify Part-Of-Speech ( POS ) tags ( Màrquez & Rodrı́guez , 1998 ) of each word in the question . Then we take all detected nouns and verbs ( excluding auxiliary verbs ) as keywords . If the VQA model ’ s answer is “ yes ” or “ no , ” we add adposition to keywords ( See Fig . 1 ) . Otherwise , we use the answer as additional keywords ( See Fig . 4 ( a ) ) . As in the work of Anderson et al . ( 2018a ) , we construct Wordnet synsets ( Fellbaum , 2010 ) containing plural and singular forms for each keyword . Thus , the nodes in Fig . 1 correspond to the synsets of each keyword . We use a set of these synsets as constraints set C in our VCBS algorithm . For simplicity in notation , we assume that C is composed of keywords , not synsets , in the rest of this paper . The set C is further divided into two subsets , strict constraints ( Cstrict ) and soft constraints ( Csoft ) . Cstrict is a set of keywords that the final beam must satisfy in order to be in accepting state ( See Fig . 2 ) . Csoft is a set of keywords that are recommended to satisfy in order for the final beam to be accepted , but not necessarily required . We use the answer as strict constraints only if the answer is not “ yes ” or “ no ” ( See Fig . 4 ) .

Summary: The paper proposes a novel method for explaining VQA systems. Different from most previous work, the proposed approach generates textual explanations based on three steps. First, it extracts keywords from the question. Then an explanation sentence is decoded (based on an RNN image captioner) through the proposed Variable-Constrained Beam Search (VCBS) algorithm to satisfy the keyword constraints. Finally, 3) checking through linguistic inference whether the explanation sentence can be used as a premise to infer the question and answer.