IEEETRANSACTIONSONMULTIMEDIA 1 RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training Muhe Ding, Yang Ma, Pengda Qin, Jianlong Wu, Member, IEEE, Yuhong Li, Liqiang Nie, Senior Member, IEEE rec A en bs tl t y ra r c e t c — ei M ve u d lt s i u m b o s d ta a n l t L ia a l r i g n e te L r a e n st g , u w a h g i e ch M s o h d o e w ls s ( t M he L ir L e M m s e ) r h g a in v g e (a) × × In S n i e m r i P la ro ri d ty uct (b) 𝐻 …["#$] … 𝐼) T S o i k m en il - a W rit i y se potential as general-purpose models for various vision-language 𝐻,-./012 𝐼& tasks.MLLMsinvolvesignificantexternalknowledgewithintheir ContrastiveLearning 𝐻["#$] 𝐻& 𝐻'()* …𝐻!"#$%&' 𝐼) 𝐼* 𝐼+ …𝐼& parameters; however, it is challenging to continually update these models with the latest knowledge, which involves huge Text Image Text Image Encoder Encoder Encoder Encoder computationalcostsandpoorinterpretability.Retrievalaugmen- tation techniques have proven to be effective plugins for both LLMsandMLLMs.Inthisstudy,weproposemultimodaladap- Arockband Arockband isplaying isplaying tive Retrieval-Augmented Bootstrapping Language-Image Pre- training (RA-BLIP), a novel retrieval-augmented framework for (c) Multimodal Knowledge Semantic Space various MLLMs. Considering the redundant information within vision modality, we first leverage the question to instruct the extractionofvisualinformationthroughinteractionswithoneset of learnable queries, minimizing irrelevant interference during retrieval and generation. Besides, we introduce a pre-trained multimodal adaptive fusion module to achieve question text-to- MultimodalAdaptiveRA-BLIP multimodalretrievalandintegrationofmultimodalknowledgeby projectingvisualandlanguagemodalitiesintoaunifiedsemantic … … … … space.Furthermore,wepresentanAdaptiveSelectionKnowledge Generation (ASKG) strategy to train the generator to au- tonomously discern the relevance of retrieved knowledge, which Which streetwas paved with realizes excellent denoising performance. 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QuestionPrompt Inter-Modal Intra-Modal Relevant Index Terms—Retrieval-augmented model, vision-language Instruction Interaction Interaction Irrelevant pre-training, multimodal retrieval, open question answering. Question Image Image-Text Text Embeddings Embeddings Embeddings Embeddings I. INTRODUCTION Fig. 1. Illustration of different multimodal retrieval approaches. (a) Cross- THE birth of the Internet has triggered an unprecedented modality retrieval. (b) Late-interaction retrieval. (c) RA-BLIP multimodal information revolution, catapulting humanity into the era adaptive retrieval. For RA-BLIP, questions, documents, images, and image- textpairsareprojectedintoaunifiedmultimodalspace. of information explosion. It is a great challenge to efficiently find answers from a vast amount of information based on our questions. Open Multimodal Multihop Question Answer- BLIP-2 [10], GPT-4 [11], etc., have been notably explored to ing (MMQA) [1]–[7] can help alleviate this problem of in- enhancetheirperformancebyimplicitlyencodingasubstantial formationoverloadbyretrievingexternalknowledgebasedon amount of external knowledge within their parameters, which questionsandgeneratingcorrectanswers.Inrecentyears,sev- now scale into the hundreds of billions [12]. While these eraladvancedLLMsandMLLMslikeFlanT5[8],LLaMA[9], models have yielded exciting results on various multimodal ThisworkwassupportedinpartbytheNationalNaturalScienceFoundation tasks, they have also encountered high computational costs ofChinaunderGrant62376069,inpartbyYoungEliteScientistsSponsorship and significant challenges in terms of interpretability. ProgrambyCASTunderGrant2023QNRC001,andinpartbyGuangdongBa- To alleviate the challenge, many researchers proposed re- sicandAppliedBasicResearchFoundationunderGrant2024A1515012027. Muhe Ding, Jianlong Wu and Liqiang Nie are with the School trievalaugmentationtechniquesthatdividethemodelintotwo of Computer Science and Technology, Harbin Institute of Technology keycomponents:theretrieverandthegenerator[13]–[16].The (Shenzhen),Shenzhen518055,China(e-mail:dmh1216380870@gmail.com, retriever accesses relevant knowledge from a knowledge base jlwu1992@pku.edu.cn,nieliqiang@gmail.com). Yang Ma is with the School of Computer Science, University of Sydney, based on the posed question, while the generator leverages Sydney,NSW2006,Australia(e-mail:yama5878@uni.sydney.edu.au). this information to create textual output in response. In ear- Pengda Qin and Yuhong Li are with the Security Department, Al- lier stages, text-modality retrieval-augmented models, such as ibaba Group, Hangzhou 311121, China (e-mail: qinpengda0406@163.com, daniel.yuhong@gmail.com). REALM [13], RAG [17], and so on [18], [19], have been 4202 tcO 81 ]MM.sc[ 1v45141.0142:viXra | IEEETRANSACTIONSONMULTIMEDIA | | | | | | | | | | | | | | | 2 | | ---------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | proposed to solve text-only question answering. They build an adaptive selection knowledge generation strategy, which the dense index as a non-parametric document memory from gives the generator the capability of selecting knowledge by extensive textual sources like Wikipedia for effective knowl- data enhancement to make the model automatically judge the edge retrieval, and the generator produces answers based on relevance of knowledge. ASKG strategy allows the generator the retrieved knowledge. More recently, multimodal retrieval- tonotsimplyrelyonthewordsimilaritybetweenthequestion augmented models, such as MuRAG [14], SKURG [20], and and knowledge, but to understand the semantic information soon[16],[21],haveemergedoneafteranother.Thesemodels of question and know which knowledge contains the answer. extend the knowledge memory across various modalities, em- Furthermore, the parameters of the image encoder and LLM ployingpre-trainedvisuallanguagemodelstoretrieverelevant of our framework are frozen, significantly reducing computa- evidence and support reasoning for answers. tionalcosts.ExtensiveexperimentsonthreerepresentativeQA However, existing methods exhibit certain limitations. The datasets demonstrate the effectiveness of our methods. first limitation is the insufficient integration and interaction Overall, our key contributions are as follows: | between | vision | and | language. | On | the one | hand, | existing | | | | | | | | | | ------- | ------ | -------- | ----------- | --- | ---------- | ------------ | -------- | --------- | ------- | ---------- | ---------- | -------- | -------- | -------- | ---------- | | | | | | | | | | • We | propose | a novel | multimodal | | adaptive | | retrieval- | | methods | lack | explicit | integration | of | multimodal | information, | | | | | | | | | | | | | | | | | | | augmented | | framework, | which | achieves | | question | text-to- | hindering the alignment of questions and multimodal knowl- multimodalretrievalandknowledge-intensivemultimodal | edge in | the semantic | | space. | As shown | in | Fig. | 1(a), some | | | | | | | | | | ------- | ------------ | --- | ------ | -------- | --- | ---- | ---------- | --- | -------------- | --- | ------ | ------------ | --- | ---------- | --- | | | | | | | | | | QA | by integrating | | visual | and language | | modalities | and | methods [14], [22], [23] have employed separate visual en- projecting them into a unified semantic space. | coder and | text | encoder | for individual | | modality | encoding | and | | | | | | | | | | --------- | ---- | ------- | -------------- | --- | -------- | -------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | • Weintroduceanadaptiveselectionknowledgegeneration | adopted | contrastive | learning | | [24] for | multimodal | | alignment | | | | | | | | | | ------- | ----------- | -------- | --- | -------- | ---------- | --- | --------- | -------- | ---- | --------- | --- | -------- | ------------ | --- | ------- | | | | | | | | | | strategy | that | leverages | the | powerful | capabilities | | of LLMs | to retrieve. This may lead to an unbalanced and biased to select the relevant retrieved knowledge for answer | multimodal | retrieval | | and reasoning | | process | towards | specific | | | | | | | | | | ---------- | --------- | --- | ------------- | --- | ------- | ------- | -------- | --------- | --- | ------------- | --- | --- | --- | --- | --- | | | | | | | | | | reasoning | | autonomously. | | | | | | modalities.Besides,late-interactionretrievalapproaches[25]– We conduct extensive experiments on various multi- • [27] in Fig. 1(b), retain dual-encoder independent encoding modal and multihop datasets (i.e., WebQA [4], Multi- | architecture | and | perform | token-wise | | interactions | only | in the | | | | | | | | | | ------------ | --- | ------- | ---------- | --- | ------------ | ---- | ------ | ------- | --- | -------- | ------ | ----- | ------- | --- | ------ | | | | | | | | | | modalQA | | [5], and | MMCoQA | [6]). | RA-BLIP | | demon- | late scoring stage, which sacrifices the retrieval efficiency for strates superiority over the existing state-of-the-art thebenefitsoffine-grainedfeaturelearning.Ontheotherhand, | | | | | | | | | retrieval-augmented | | | models. | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | ------------------- | --- | --- | ------- | --- | --- | --- | --- | existingmethods[14],[20],[28],[29]donotutilizequestions | to instruct | the | image | encoder | in selectively | | extracting | visual | | | | | | | | | | ------------- | ----------- | -------------- | ------- | -------------- | ------------- | ---------- | ---------- | ------------------ | --- | ------------ | ----------- | ----- | ------- | ---------------- | ------ | | | | | | | | | | | | II. | RELATEDWORK | | | | | | features, | and | thus suffer | from | interference | | and noise | caused | | | | | | | | | | | | | | | | | | A. Vision-Language | | Pretraining | | | | | | | by redundant | information | | in | images. | Moreover, | such | methods | | | | | | | | | | lack mutual | instruction | | when | encoding | different | | modal fea- | | | | | | | | | | | | | | | | | | Vision-language | | pre-training | | (VLP) | aims | to train | models | | tures, making | | it challenging | | to model | relationships | | between | | | | | | | | | | | | | | | | | | on large-scale | | image-text | datasets | to | capture | the relationship | | multiple sources. The second limitation is that existing ap- between these two modalities. Broadly, VLP methodologies proachesdonotinspectthecorrectnessoftheretrievedrelevant | | | | | | | | | fall into | two categories | | based | on their | training | approach: | 1) | | --- | --- | --- | --- | --- | --- | --- | --- | --------- | -------------- | --- | ----- | -------- | -------- | --------- | --- | knowledge at the generation stage. However, the retrieved End-to-end Methods: This category includes methods [35]– | knowledge | contains | significant | | noise, | resulting | in poor | model | | | | | | | | | | --------- | -------- | ----------- | --- | ------ | --------- | ------- | ----- | --- | --- | --- | --- | --- | --- | --- | --- | [38]thattrainmodelsend-to-end,backpropagatinglearnedsig- | anti-interference | | and | robustness. | The | generator | assumes | all | | | | | | | | | | ----------------- | --- | --- | ----------- | --- | --------- | ------- | --- | --------------- | --- | ------ | -------- | ------- | --------- | --- | ----------- | | | | | | | | | | nals to achieve | | mutual | learning | between | different | | modalities. | retrieved relevant knowledge is correct, potentially leading to 2)ModularMethods:Incontrast,modularmethods,asseenin | the utilization | | of irrelevant | or | confusing | information. | | | | | | | | | | | | --------------- | --- | ------------- | --- | --------- | ------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | worksby[39]–[43],involvekeepingtheparametersofspecific To address the above issues, we propose multimodal adap- pre-trainedcomponents(likeimageencodersorlargelanguage tiveRetrieval-AugmentedBootstrappingLanguage-ImagePre- models) fixed while focusing on refining other aspects of the | training | (RA-BLIP). | | RA-BLIP | consists | of | two key | compo- | | | | | | | | | | -------- | ---------- | --- | ------- | -------- | --- | ------- | ------ | ------ | ------------- | --- | ---- | -------- | ------------- | --- | ------ | | | | | | | | | | model. | For instance, | LiT | [44] | utilizes | a pre-trained | | frozen | nents: a multimodal adaptive retrieval-augmented framework image encoder from CLIP, while Flamingo [45] and BLIP-2 | and an | adaptive | selection | | knowledge | generation | | (ASKG) | | | | | | | | | | ------ | -------- | --------- | --- | --------- | ---------- | --- | ------ | --- | --- | --- | --- | --- | --- | --- | --- | [10]freezethelanguagemodeltointegrateLLMsintovision- strategy. To tackle the first limitation, RA-BLIP is based on language tasks better. Besides, instruction tuning is also an the InstructBLIP architecture [30] and adopts Q-Former to effective approach during VLP. InstructBLIP [30] represents implementinstruction-awarevisualfeatureextractionthatuses arecentadvancementinthisarea,achievinginstruction-aware questions as instructions. The question instruction interacts visual feature extraction and instruction-guided LLM genera- withthequeryembeddingsthroughsharedself-attentionlayers | | | | | | | | | tion. The | unique | capability | of | InstructBLIP | to | extract | features | | -------------- | --- | -------------- | --- | -------------------- | --- | --- | ----------- | --------- | ------ | ------------- | --- | ------------ | ---- | ------- | ----------- | | and encourages | | the extraction | | of question-relevant | | | visual fea- | | | | | | | | | | | | | | | | | | based on | prompt | instructions, | | combined | with | its | utilization | tures. Additionally, we incorporate a pre-trained multimodal of frozen LLMs and image encoders, positions it as an ideal | adaptive | fusion | module | to fuse | vision | and | text information, | | | | | | | | | | | ------------- | ---------- | -------- | ------------------ | ---------- | --- | ----------------- | --------- | -------- | ------- | -------- | ------------------- | --- | --- | ---------- | --- | | | | | | | | | | backbone | for our | proposed | retrieval-augmented | | | framework. | | | obtaining | multimodal | | features | [31]–[33]. | | As a result, | RA- | | | | | | | | | | BLIP achieves | | question | text-to-multimodal | | | retrieval | by align- | | | | | | | | | ing the questions and multimodal knowledge bases in the B. Text-modality Retrieval-Augmented Models semantic space of three modalities: text, image, and image- Retrieval-augmented techniques have proven to be ef- text [34], as shown in Fig. 1(c). For the second limitation, fective plugins for both LLMs and MLLMs in academia. we leverage the implicit capabilities of LLMs and introduce These techniques extract pertinent world knowledge from IEEETRANSACTIONSONMULTIMEDIA 3 extensive databases, subsequently integrating this information A. Problem Formulation to formulate answers. Pioneering methods like ORQA [3] This paper presents a multimodal adaptive retrieval- have employed inverse cloze tasks for retriever pre-training, augmentedframeworkcalledRA-BLIPforopenmultihopand showcasing their efficacy on open-ended question answering multimodalQA,integratingretrievalandgenerationfunctions. datasets. Following suit, REALM [13] extends this by retriev- For knowledge-intensive QA, we deconstruct the task into ing and processing documents from comprehensive sources two stages: retrieval and generation, which are implemented like Wikipedia for logical reasoning, which is pre-trained to by the retriever and generator respectively. The goal of our reason over a large corpus of knowledge on the fly during model training is to learn the distribution P(y|x ) to generate q inference. Furthermore, RAG [17] adopts a pre-trained model a textual output y conditioned on input question x and q andnon-parametricmemoryforlanguagegeneration.FiD[18] multimodal knowledge base KB (KB = k ,...,k ). Firstly, 1 n leveragesencoder-decodertransformermodelsforknowledge- the retriever encodes questions, images, and texts from the intensivetasks,settingnewbenchmarksinQA.Morerecently, knowledge base KB. It identifies the most relevant retrieved RETRO [19] has advanced these methods by handling longer knowledge, K ⊂KB (K is the retrieved knowledge) for ret ret sequences and accessing diverse documents for segmented each question x , which is modeled as p(K |x ). Secondly, q ret q sequences from expansive retrieval datasets. Text-modality the generator utilizes an LLM to generate answers y, condi- retrieval-augmented models construct dense indices as non- tionedonboththequestionandtheretrievedknowledge,which parametricdocumentmemories,usingextensivetextualknowl- is modeled as p(y|x ,K ). We treat multimodal knowledge q ret edge bases to align the retrieval process with specific queries. K asalatentvariablefromtheexternalknowledgebaseand ret Despite these advancements, a notable limitation remains the marginalize it to increase the overall likelihood of the answer needforthesemethodstoeffectivelyleveragevastmultimodal y. The overall process is encapsulated in the equation: knowledge, thereby constraining their applicability in the (cid:88) domain of open multimodal question answering. p(y |x q )= p(K ret |x q )·p(y |x q ,K ret ). (1) (cid:124) (cid:123)(cid:122) (cid:125) (cid:124) (cid:123)(cid:122) (cid:125) Kret⊂KB Retrieval Generation C. Multimodal Retrieval-Augmented Models This dual-stage framework effectively addresses the complex- To overcome the limitations of text-modality retrieval- ities of open multimodal QA by balancing the retrieval of augmented models, recent research [10], [15], [30], [46], [47] multimodal data and knowledge-based generation, and has has made strides in integrating multimodal knowledge. No- been validated by extensive experiments and ablation studies. tableefforts,includingAutoRouting[5]andMAE[6],involve trainingdistinctmodelsforeachmodalityandusingclassifiers B. Model Architecture for task-specific routing, though this approach often hampers RA-BLIP is built on a simple backbone model that is pre- cross-modal reasoning. MuRAG [14] seeks to overcome this trained to encode image-text pairs so that they are suitable limitation by employing separate encoders for visual and for both knowledge base retrieval and answer generation. textualmodalities,followedbyajointencoderformultimodal The overall framework of RA-BLIP is shown in Fig. 2. The fusion. However, this approach lacks integrated guidance for backbone model consists of a multimodal encoder f (·) and θ different modalities and cannot model the relations between decoderg (·),whichareusedascomponentsoftheRA-BLIP θ knowledge sources during retrieval. SKURG [20] attempts to model to implement retrieval and generation. The multimodal bridge this gap by using an entity-centered fusion encoder encoder f (·) contains a frozen image encoder ViT [48], θ to align modalities, yet faces challenges in computational Q-Former architecture [30], and the pre-trained multimodal efficiency and limited interpretability. Besides, Solar [21] adaptive fusion module. The decoder g (·) is composed of θ transforms multimodal inputs into a unified language format a LLM FlanT5 [8]. Querying Transformer (Q-Former) [10] but falls short in handling complex tasks and generalizing is a lightweight Transformer consisting of two modules that visual information. REVAL [16] leverages large-scale knowl- sharethesameself-attentionlayer:oneisanimagetransformer edgegraphstoassistvisuallanguagepre-training,butitbrings that interacts with the frozen image encoder ViT for visual a lot of calculations. In contrast, RA-BLIP distinguishes itself featureextraction,andtheotherisatexttransformercanactas by seamlessly integrating visual and language modalities into both text encoder and text decoder for text feature. The visual a cohesive semantic space, enabling the autonomous selection encoder, composed of ViT and Q-Former image transformer, of relevant knowledge for reasoning, thus addressing these hasinstruction-awarevisualfeatureextractioncapabilitiesand limitations more effectively. can extract visual information based on question instructions. We input N learnable query embeddings into the Q-Former III. METHODOLOGY imagetransformer,whichinteracts withfrozenimagefeatures In this section, we first formulate the research problem through cross-attention layers to obtain visual representation and subsequently elaborate on the model architecture of our f (I) ∈ RN×D, where D is the hidden dimension of the Q- θ retrieval-augmented framework. Then, we describe the learn- Former.Additionally,weusetheQ-Formertexttransformerto ablequeryinteractionapproach,followedbymultimodaladap- encode text, taking the [CLS] token as the text representation tive fusion module. Subsequently, we show how to train the f (T)∈R1×D.Toobtainmultimodalfeaturescombiningboth θ retrieverandranktherelevantknowledge.Lastly,weintroduce imageandtext,weintroduceapre-trainedmultimodaladaptive the adaptive selection knowledge generation strategy. fusion module M (·) to obtain the multimodal representation θ IEEETRANSACTIONSONMULTIMEDIA 4 Q:The Marina Bay Sands in Singapore is made of what building material? Retrieval Knowledge Template: {Q}. Which item is related to this question? Positive & Negative Knowledge Pair Answer Generation Stage: Q-Former Q-Former Q-Former Fusion Multimodal LLM Retrieval Stage: Learnable queries 🔥 🔥 Image 𝑓!(𝑘"#) … Encoder 𝑓!(𝑘") Marina Bay Q K1 🔥Q-Former Sands Singapore 🔥 Q:The Marina Bay Sands 𝑓!(𝑘#) in Singapore is made of what building material? It is close to some of Singapore's famous In Asia, the Marina 🔥 S l u a n n t d e m c a C r i k t s y , s u M c a h r i a n s a S B i a n y g a S p a o n r d e s w lo a c s a a te d d d e in d 𝑓!(𝑘#) Bay Sands, Esplanade, to the company's portfolio in 2010… … ModalityRepresentation … Text Question QA Data Format Image Relevant Knowledge Input Learnable queries Image-Text concat Q K2 Image Encoder Instruction Top-K Q K3 Q K4 Question& Caption Marina Bay ASKG Data Format Sands Singapore FeatureEncoder In 2010, when it opened, at total cosTt he Downtown MRInT Asia, the Marina 🔥 Classifier inc o lu f d S in $ g 8 l b a i n ll d io c n o stm, st e a t t e io rs n t i o s t b h u e i l W t a e s fe St B w oi a nf y g a S p a o n r d e s w lo a c s a a te d d d e in d Marina Bay Sands… the building. to the company's portfolio in 2010… Maximum Inner Product Search Ranking Fig. 2. The overall workflow of RA-BLIP consists of a retrieval stage and a generation stage. We utilize multimodal encoder f θ (·) to project questions and multimodal knowledge into a unified semantic space to achieve question text-to-multimodal retrieval, and exclude confusing knowledge by ranking. Additionally, we employ ASKG strategy to filter out invalid knowledge, enabling precise reasoning. The parameters of LLM and image encoder are fixed, onlytheQ-Formerandclassifieraretrainable. f (IT) ∈ R(N+1)×D. Consequently, the multimodal encoder Q-Former has aligned visual and text feature representation, θ can simultaneously encode image, text, and image-text fea- we use Image-Text Matching loss and Image-grounded Text tures. Questions and knowledge are encoded into multimodal Generation loss for pre-training. As shown in Fig. 3, our information through the multimodal encoder and then input approach is to fix the parameters of the image encoder and into the decoder for answer generation. In the generation Q-Former, and solely fine-tune the parameters of the multi- stage, compared with the retriever, the multimodal encoder modal adaptive fusion module. The module concatenates the discards the multimodal adaptive fusion module to reduce the visual embedding and text embedding with a dimension of computational cost. R(N+L)×D, where N is the learnable query embeddings and L is the length of text tokens. Image-text matching loss is used to fuse image and text representations, and the results C. Learnable Query Interaction for Multi-images of the fusion module are fed into a binary linear classifier Questions and image captions are used as instructions to foreachoutputquerytoobtainthelogitsandtaketheaverage extract visual features to get learnable query embeddings logitsofallqueriesasthematchingscore.Givenapre-training and input them together with text knowledge to LLMs for dataset X = {I ,T }n , we randomly sample negative texts i i i=1 generation. We employ a novel approach for extracting visual foreachimageandrandomlysamplenegativeimagesforeach information to alleviate the burden of LLMs in distinguishing text, to generate negative training data. Therefore, we denote knowledge. In the original Q-Former in [30], multiple images the ground truth label as y ∈ {1,0} for each image-text pair are processed by employing multiple separate sets of queries (I ,T ), indicating if the input image-text pair is relevant or i i for each, with each set of queries independently extracting not.Weusethemultimodalencoderf (·)toencodeimage-text θ visualfeatures.Thisresultsinusingmultiplequeryfeaturesfor pairs and input it into the multimodal adaptive fusion module generation, which can be computationally intensive and less M (·). The objective function is defined as follows: θ efficientincapturingtheinterrelationsamongdifferentimages. 1 (cid:88) In contrast, our method innovates by succinctly utilizing one L =− ylog(ρ(M (f (I );f (T )))), (2) itm n θ θ i θ i set of learnable queries to directly interact with and extract Ii,Ti∈X features from multiple images in a unified manner. This pro- whereρ(·)isthesoftmaxfunction.Image-groundedTextGen- cessoccursduringtheQ-Formerstage,enablingmoreefficient eration loss trains the fusion module to generate texts, given and integrated interaction among multiple visual references. input images as the condition [10], [30]. For the image-text By employing one set of query interaction approach, RA- pairs in the pre-training dataset, each image I corresponds to BLIP not only simplifies the feature extraction process but a text sentence y ={y ,...,y } of length T. We employ a 1:T 1 T also enhances the efficiency of information extraction. This multimodalcausalself-attentionmaskformultimodalencoder unified interaction allows the model to understand better and f (·)andmultimodaladaptivefusionmoduleM (·)tocontrol θ θ represent the collective information presented in multiple im- the interaction between queries and text. The visual query ages, enabling more effective and cohesive feature utilization, functions as a prefix causal, ensuring that queries can attend especially when dealing with complex scenes or subjects to each other while excluding text tokens. Similarly, each text across multiple images. token y can attend to all visual queries and preceding text tokens. The loss function is defined as: D. Multimodal Adaptive Fusion Module T (cid:88) L =− logM (f ((y |y ,I)). (3) The pre-trained multimodal adaptive fusion module M (·) itg θ θ t