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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
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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
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realizes excellent denoising performance. Extensive experiments boards; Little Champlain
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Question Image Image-Text Document
the state-of-the-art retrieval-augmented models.
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
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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 <t
θ
t=1
consists of a 3-layer BERT network [31], [32]. Since the
| IEEETRANSACTIONSONMULTIMEDIA | | | | | | | | | | | | | | | 5 |
| ---------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | ----------- | ------ | ------ | -------- | ---------- | --- | -------- |
| | | | | | | | | at | the feature | level, | yet it | fails to | understand | the | question |
Image-grounded Text
| unmasked | masked | | Image-Text Matching | | | | | | | | | | | | |
| -------- | ------ | --- | ------------------- | --- | --- | ---------- | --- | --- | ------ | ------- | --------- | ------ | -------- | ----- | ----- |
| | | | | | | Generation | | and | cannot | provide | an answer | in the | semantic | space | [14]. |
Q: query embeddings
| | | | | | | | | For | instance, | consider | the | question “The | Marina | | Bay Sands |
| -------------- | --- | -------------------- | --- | --- | --- | ---------------- | --- | --- | --------- | -------- | ------- | ------------- | ----------- | --- | --------- |
| T: text tokens | Q | T 🔥🔥MultimodalFusion | | | | MultimodalFusion | | | | | | | | | |
| | | | | | 🔥🔥 | | | in | Singapore | is made | of what | building | material?”, | | and the |
Q
| | | | Feed Forward | | | Feed Forward | | | | | | | | | |
| --- | --- | --- | ------------ | --- | --- | ------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- |
ITM Attention relevanttextknowledge“Singaporeisalsothenewdowntown
| | | T | | | share | | | | | | | | | | |
| --- | --- | --- | --- | --- | ----- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
Self Attention Self Attention of Singapore, built on reclaimed land.”. The question is about
Q
× N × N the building materials of a Singapore hotel, but this text is
ITG Attention
T about the location of a Singapore hotel. Despite their token-
··· ··· wise similarity in the words, they cannot answer this question
Image andcauseconfusion.Toaddressthisissue,weintroducearank
Q-Former
Encoder strategy to sort the Top-K candidates and exclude confusing
| | | | | | | | | samples,whereK | | isthemaximumnumberofpositivesamples | | | | | |
| --- | --- | --- | ----------------- | --- | --- | ----------------------- | --- | -------------- | --- | ----------------------------------- | --------- | ------------ | ------- | ----- | -------- |
| | | | | ··· | | Redfoxsittinginthegrass | | | | | | | | | |
| | | | | | | | | corresponding | | to the | question. | We select | the | Top-K | samples, |
| | | | Learnable Queries | | | Image Caption | | | | | | | | | |
| | | | | | | | | categorize | | them into | positive | and negative | samples | | based on |
Fig.3. Schematicdiagramofthepre-trainingprocessofmultimodaladaptive thegroundtruth,andthenperformranktraining.SincetheQ-
fusionmodule. Former does not have a classifier, we input the multimodal
(kI;kT;kIT)
| | | | | | | | | features | | output f θ | | by | the multimodal | | encoder |
| --- | --- | --- | --- | --- | --- | --- | --- | -------- | --- | ---------- | --- | --- | -------------- | --- | ------- |
intothefixed-parameterLLMsencoderandtrainableclassifier
| E. Retrieval-Augmented | | | Retriever | Training | | | | | | | | | | | |
| ---------------------------------------------------- | ------------- | --- | --------- | ------------- | -------- | --- | ------------ | --- | -------- | ------------- | ---------- | ---------------------- | --- | --- | ------- |
| | | | | | | | | z. | The loss | function | is defined | as: | | | |
| During | the retrieval | | stage, | the retriever | utilizes | | the question | | | | | | | | |
| | | | | | | | | | | | | (cid:0) (kI,kT,kIT)),y | | | (cid:1) |
| | | | | | | | | | L | =CrossEntropy | | z(f | | | , (6) |
| x toretrieverelevantknowledgefrommultimodalknowledge | | | | | | | | | cls | | | θ | | | |
q
base KB. To achieve this, we apply the multimodal encoder where y is the ground truth about the knowledge is relevant
| f θ (·), which | encodes | | the question | | x q along | with | all latent | or | not. | | | | | | |
| -------------- | --------- | --- | ------------ | ------------ | --------- | ----- | ----------- | --- | ---- | --- | --- | --- | --- | --- | --- |
| multimodal | knowledge | | into | an embedding | | space | to identify | | | | | | | | |
the Top-K most relevant candidates, illustrated in Fig. 2 F. Adaptive Selection Knowledge Generation
retrieval stage. We use contrastive learning to construct pos- During the generation stage, the retrieved multimodal
| itive and | negative | samples | for | training. | The | knowledge | type | | | | | | | | |
| --------- | -------- | ------- | --- | --------- | --- | --------- | ------- | ----------------------------------- | --- | --- | --- | --- | --- | ------------- | --- |
| | | | | | | | | knowledgeiscombinedwiththequestionx | | | | | q | asanaugmented | |
| | | | | | | | kI, kT, | | | | | | | | |
consists primarily of three components: image text input [k ,...,k ,x ], which is fed to the multimodal encoder
| | | | | | | | | | 1 | l q | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
and image-text kIT. Thus, the l-th example in the dataset is and LLMs [8] to produce multimodal representation encoding
| | | ,{kˆI,kˆT,kˆIT} | | | I | T | IT | | | | | | | | |
| --- | --- | --------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
represented as (x l ,y l l ,{k ,k ,k } l ), where and generate answers. We observe that existing methods [14],
| | | | i | i i | j | j | j | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
kˆ is the i-th positive (image, text, image-text) sample and [21] directly rely on retrieval results without distinguishing
i
k j represents j-th negative (image, text, image-text) sample. thecorrectnessoftheretrievedknowledge,potentiallyleading
For a batch of knowledge examples, we gather all associ- to the utilization of incorrect, confusing, or irrelevant infor-
ated positive and negative knowledge sources into a batch mation. To address this, we propose an adaptive selection
| | | | I | T IT | | I | T IT | | | | | | | | |
| --- | --- | --- | --- | ---- | --- | --- | ---- | --- | --- | --- | --- | --- | --- | --- | --- |
K = {{kˆI,kˆT,kˆIT} ,{k ,k ,k } ,...,{k ,k ,k } }. knowledge generation (ASKG) strategy based on a question-
| B | i | i i | 1 j | j j | 1 | j | j j B | | | | | | | | |
| -------------- | --- | ------- | -------------- | --- | --- | -------- | -------- | ---------- | --- | ------------ | ----- | ------- | --- | ---------- | ------ |
| | | | | | | | | and-answer | | formulation, | shown | in Fig. | 2 | generation | stage. |
| The multimodal | | encoder | is responsible | | for | encoding | the mul- | | | | | | | | |
ASKGstrategyenablesthegeneratortogobeyondmereword
| timodal | feature | representations | | and | aligning | the questions | and | | | | | | | | |
| ------- | ------- | --------------- | --- | --- | -------- | ------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
knowledge within the unified semantic space. This alignment similarity between the question and the retrieved knowledge,
| | | | | | | | | allowing | | it to grasp the | semantic | information | | of the | question |
| ----------- | ----------- | --- | ------------- | --- | ------- | --- | ------------ | -------- | --- | --------------- | -------- | ----------- | --- | ------ | -------- |
| facilitates | identifying | | the proximity | | between | a | question and | | | | | | | | |
itscorrespondingknowledgethroughcontrastivelearning.The and identify which piece of knowledge contains the answer.
objective function is defined as follows: Specifically, we manually construct question-and-answer data
| | | | | | | | | to | enable | the model | to discriminate | | the relevance | | of multi- |
| --- | --- | ----- | --- | --- | --------------- | --- | --- | --- | ------ | --------- | --------------- | --- | ------------- | --- | --------- |
| | | exp(f | (x | )·f | (kˆI;kˆT;kˆIT)) | | | | | | | | | | |
θ q θ modalknowledge,therebyutilizingtheimplicitcapabilitiesof
| L | =−log | (cid:80) | | | | | , (4) | | | | | | | | |
| --- | ----- | -------- | --- | --- | --- | --- | ----- | --- | --- | --- | --- | --- | --- | --- | --- |
con exp(f (x )·f (kI;kT;kIT)) LLMs for knowledge filtering. Based on the original dataset,
| | | | | θ q | θ | | | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
k∈KB
| | | | | | | | | we | select | relevant knowledge | | as positive | examples | | and irrel- |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | ------ | ------------------ | --- | ----------- | -------- | --- | ---------- |
where f (·) is the multimodal encoder and K is a batch of evant knowledge as negative examples. We create an ASKG
| | θ | | | | | B | | | | | | | | | |
| --------- | -------- | --- | --- | -------------- | --- | ------- | ------- | -------- | --- | ----------- | ------- | --- | --------- | --------- | --- |
| | | | | | | | | enhanced | | dataset and | combine | the | knowledge | according | to |
| knowledge | sources. | We | use | the multimodal | | encoder | trained | | | | | | | | |
to encode text, image, and image-text features, and apply templates, with the identifier of the positive examples serving
| | | | | | | | | as | the answer. | The | template | for the | question | x | is : “We |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | ----------- | --- | -------- | ------- | -------- | --- | -------- |
Maximum Inner Product Search (MIPS) [49] to select Top-K (cid:101)q
from knowledge base KB as the relevant K , as shown in would like to request your feedback on ranking the questions
ret
the following: accordingtotheirrelevancetothereferencesbelow.Relevance
| | | | | | | | | refers | to | the degree | to which | the reference | | can answer | the |
| ------ | --- | ----------- | --- | --- | ----------------- | --- | --- | ------ | --- | ---------- | -------- | ------------- | --- | ---------- | --- |
| TopK(K | | |x )=TopK{f | | (x | )·f (kI;kT;kIT)}. | | (5) | | | | | | | | |
ret q θ q θ question. The input format is Question: [content], Reference
k∈KB [knowledge ID]: [content]. The output format is: Related
Although the retriever is more efficient for many retrieval contentis[knowledgeID].”,andtheansweryisintheformof
(cid:101)
tasks, its accuracy is lower on open multimodal question “The most relevant reference is Reference [knowledge ID].”.
answering. There are instances where certain knowledge is We refer to the above enhanced dataset of questions and
confusing and bears token-wise similarity to the question answers as x (cid:101)q and y (cid:101) = {y (cid:101)1 ,...,y (cid:101)M }, where M is the text
IEEETRANSACTIONSONMULTIMEDIA 6
Algorithm 1 The Training pipeline for RA-BLIP.
TABLEI
Retrieval Training Stage OVERALLDETAILSOFDOWNSTREAMDATASETS.
Input: question {x }N , knowledge base KB
qi i=1
Dataset Train Dev Test
for sampled mini-batch x and K do
q B
Compute contrastive loss L by Eq. (4) WebQA[4] 34.2K 5K 7.5K
con
MultimodalQA[5] 23.8K 2.4K 3.6K
end for
MMCoQA[6] 4.6K 0.6K 0.6K
Return retrieval model θ
ret
Input: question {x }N , ground truth y, Top-K
qi i=1
retrieved knowledge from θ
ret across text, image, and tabular data types. The perfor-
for sampled mini-batch x , and TopK do
q B mance of MultimodalQA is measured by F1 score at the
Calculate crossentropy loss L by Eq. (6)
cls word level and the Exact Match (EM) of the answers.
end for
• MMCoQA [6] is the first dataset constructed for mul-
Return ranking model θ
ran timodal conversational QA tasks and aims to answer
Generation Training Stage
users’ questions with multimodal knowledge sources via
Input: question {x }N , answer y, K from θ ,
qi i=1 ret ran multi-turnconversations.Itcomprisesmultiplesupervised
ASKG datasets x and y
(cid:101)q (cid:101) signals, including decontextualized questions, answers,
for sampled mini-batch x , K and x do
q ret (cid:101)q and corresponding evidence.
Compute generation loss L by Eq. (7)
gen 2) Compared Methods: For WebQA, MultimodalQA, and
end for
MMCoQA, we make comparisons with different baseline
Return generation model θ
gen
methods. The model parameter quantity comparison is shown
in Table II. The number of Solar parameters is not pub-
lished, and both Solar and SKURG use other models to
length.Giventhedatasetquestionx andground-turthanswer
q exploit multimodal information without accounting for the
oflengthT,y ={y ,...,y },aswellastheconstructedx
1:T 1 T (cid:101)q parameter counts of other models. We have frozen LLM and
and y, the generator g (·) utilizes attention over question x
(cid:101) θ q ViT, focusing solely on training Q-Former, which has fewer
and relevant knowledge K encoded by multimodal encoder
ret trainable parameters and bfloat16 encoding. In order to verify
f (·) to generate textual outputs token by token. The final
θ thescalinglaw[51],weselectedmorepowerfulFlanT5xxlfor
generation loss is defined by:
experiment. To compare LLMs with other methods of similar
parameter magnitude, we utilized T5-base and T5-large as
T
L = (cid:88) −logg (y |y ,f (x ,K )) benchmarksforafaircomparison.SinceT5-baseandT5-large
gen θ i 1:i−1 θ q ret
arenotalignedwiththemodelthroughpre-training,theyneed
i=1 (7)
M to be fine-tuned during training.
(cid:88)
+α −logg θ (y (cid:101)i |y (cid:101)1:i−1 ,f θ (x (cid:101)q )), • VLP [4], [52] pre-trains its transformer-based encoder-
i=1 decoder with both textual and visual information. They
first retrieve knowledge based on the question and feed
where α is the hyperparameter which will be discussed in
it into the model to generate answers. In addition, VLP
section IV-D. To give a clear illustration of RA-BLIP, we
integrates VinVL [41] to improve performance.
summarize the training pipeline in Algorithm 1.
• MuRAG [14] encodes the question and selects Top-K
nearest neighbors from multimodal memory. They are
IV. EXPERIMENTS then fed into the backbone encoder-decoder to generate
textualoutputstokenbytoken.Thebackbonemodeluses
A. Experimental Settings
T5-base [53] and ViT-large [48], respectively.
1) Datasets: WeevaluateourmethodonthreeQAdatasets: • SKURG [20] takes multimodal information sources as
WebQA [4], MultimodalQA [5], and MMCoQA [6]. The inputandencodesthemseparately,thenutilizesanentity-
details of these datasets are showcased in Table I. centered fusion encoder to align the sources of different
modalities via the shared entities and structured knowl-
• WebQA [4] is a large-scale dataset for multimodal and
edge. The method adopts OFA-base [54] and BART-base
multihop QA where all questions are knowledge-seeking
[55]. Besides, it integrates ELMo-based NER [56] and
queries that require two or more knowledge sources.
OpenNRE [57] for entity and relation extraction.
Evaluation metrics are retrieval F1 and QA for assessing
answer generation quality, which is measured as both • Solar [21] first converts multimodal inputs into textual
data and then utilizes a T5 [53] to generate answers
fluency (QA-FL) and accuracy (QA-ACC). We calculate
through retrieval, ranking, and decoding. It retrieves and
fluency through BARTScore [50] and evaluate accuracy
ranks the information using BERT [32]. Additionally,
via F1 and recall. The fluency score and accuracy score
are multiplied FL∗Acc to calculate the overall score. it adopts BLIP [58] for image caption generation and
VinVL [41] for image-attribute feature extraction.
• MultimodalQA [5] is a collection of multihop QA pairs
thatnecessitatethefusionofknowledgefromtext,tables, 3) Implementation Details: Our method includes multi-
and images. This dataset requires retrieval and reasoning modal fusion pre-training, retrieval, ranking, and generation.
| IEEETRANSACTIONSONMULTIMEDIA | | | | | | | | | | | | | | | 7 |
| ---------------------------- | --- | --- | ------- | --- | --- | --- | --- | --- | --- | --- | -------- | --- | --- | --- | --- |
| | | | TABLEII | | | | | | | | TABLEIII | | | | |
COMPARISONOFPARAMETERQUANTITY.PARAMETERQUANTITIESOF RESULTSOFWEBQAOFFICIALTEST-SET.∗REPRESENTSLLMIS
OTHERMETHODSREFERTO[20],[21]. FLANT5XL,WHILE†REPRESENTSLLMISFLANT5XXL.BOLDAND
UNDERLINEDENOTETHEBESTANDPREVIOUSSOTARESULTS.
| Model | | | #TrainableParams | | | #TotalParams | | | | | | | | | |
| ------------------ | --- | --- | ---------------- | ----- | --- | ------------ | --- | ----------------- | --- | --- | -------- | ------ | ------- | ---- | ---- |
| VLP+VinVL[4] | | | | 220M | | 220M | | Model | | | Retr-F1↑ | QA-FL↑ | QA-Acc↑ | | QA↑ |
| MuRAG[14] | | | | 527M | | 527M | | VLP[52] | | | 0.69 | 42.6 | | 36.7 | 22.6 |
| SKURG[20] | | | | 447M | | 447M | | VLP+VinVL[41] | | | 0.71 | 44.2 | | 38.9 | 24.1 |
| ImplicitDecomp[5] | | | | 1310M | | 1310M | | MuRAG[14] | | | 0.75 | 55.7 | | 54.6 | 36.1 |
| RA-BLIP(T5-base) | | | | 387M | | 1398M | | SKURG[20] | | | 0.88 | 55.4 | | 57.1 | 37.7 |
| RA-BLIP(T5-large) | | | | 902M | | 1913M | | | | | | | | | |
| | | | | | | | | Solar[21] | | | 0.89 | 60.9 | | 58.9 | 40.9 |
| RA-BLIP(FlanT5xl) | | | | 109M | | 4.1B | | | | | | | | | |
| | | | | | | | | InstructBLIP∗[30] | | | - | 51.7 | | 59.0 | 31.4 |
| RA-BLIP(FlanT5xxl) | | | | 109M | | 12.1B | | | | | | | | | |
| | | | | | | | | InstructBLIP†[30] | | | - | 53.4 | | 62.5 | 35.0 |
| | | | | | | | | RA-BLIP(T5-base) | | | - | 62.6 | | 59.7 | 41.6 |
| | | | | | | | | RA-BLIP(T5-large) | | | - | 62.9 | | 60.9 | 42.5 |
For WebQA and MultimodalQA, we follow all steps as men- RA-BLIP∗ 0.83 65.1 65.3 45.8
| | | | | | | | | RA-BLIP† | | | 0.89 | 65.5 | | 68.7 | 48.5 |
| --- | --- | --- | --- | --- | --- | --- | --- | -------- | --- | --- | ---- | ---- | --- | ---- | ---- |
tionedabove.ForMMCoQA,wemanuallyincludethepositive
| clues in | the retrieval | | results | without | performing | | subsequent | | | | | | | | |
| -------- | ------------- | --- | ------- | ------- | ----------- | --- | ---------- | ------- | -------- | ---- | ----- | ------ | ------------ | ---- | ------- |
| ranking, | following | the | process | used | in previous | | work [21]. | | | | | | | | |
| | | | | | | | | used in | previous | work | [21]. | We set | the learning | rate | as 1e-5 |
WeadoptInstructBLIP[30]withfrozenEVA-ViT-g/14[59]as
andbatchsizeas32for10epochsattheretrievalstage.Then,
| well as | LLM (FlanT5xl | | and | FlanT5xxl) | [8] | for generation. | In | | | | | | | | |
| ------- | ------------- | --- | --- | ---------- | --- | --------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
order to compare LLMs with fewer than 1 billion parameters, we use a cosine learning rate of 1e-6, warmup of 1K steps,
| | | | | | | | | and a batch | size | of 16 | for 10 | epochs | at the generation | | stage. |
| ----------- | --- | --- | -------- | ---- | -------- | --- | -------- | ----------- | ---- | ----- | ------ | ------ | ----------------- | --- | ------ |
| we replaced | the | LLM | backbone | with | T5-large | and | T5-base. | | | | | | | | |
DuetotheinconsistentdimensionsbetweenT5andQ-Former, We use the standard evaluation protocol for each dataset and
reportthesamemetrics,aswellastherandomseedsarefixed
| we added | a linear | layer | to | align the | dimensions | | and did not | | | | | | | | |
| -------- | -------- | ----- | --- | --------- | ---------- | --- | ----------- | --- | --- | --- | --- | --- | --- | --- | --- |
for reproducibility.
| perform | pre-training. | | We keep | the | image | encoder | and the | | | | | | | | |
| ------------ | ------------- | ------ | -------- | -------- | ----- | ------- | ---------- | --- | --- | --- | --- | --- | --- | --- | --- |
| LLMs frozen, | | tuning | only the | Q-Former | | and the | multimodal | | | | | | | | |
B. Main Results
| fusion module | | during | the pre-training | | and | retrieval | stage. We | | | | | | | | |
| ------------- | --- | ------ | ---------------- | --- | --- | --------- | --------- | --- | --- | --- | --- | --- | --- | --- | --- |
adopt the multimodal encoder and LLM encoder as feature Results on WebQA.
| | | | | | | | | | | | We | show | the WebQA | | results in |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---- | --------- | --- | ---------- |
encoder at ranking stage. At the generation stage, we froze Table III. We can see that RA-BLIP surpasses all baselines in
the image encoder as well as LLMs and only trained the Q- termsofbothQAandretrievalF1scores.RA-BLIP(FlanT5xl)
| Former | with ASKG. | We | froze | the | image encoder | | and FlanT5 | | | | | | | | |
| ------ | ---------- | --- | ----- | --- | ------------- | --- | ---------- | -------- | ----- | --------- | ----- | --- | ----- | ------ | -------- |
| | | | | | | | | achieves | 45.8% | accuracy, | which | is | +4.9% | higher | than the |
duringalltrainingprocesses,aswellasusedbfloat16encoding state-of-the-art Solar [21]. Especially the metric QA-Acc is
to achieve RA-BLIP with the fewest trainable parameters. +6.4% higher, proving the model’s powerful generation abil-
For pre-training, we pre-train the multimodal adaptive fu- ity.Besides,RA-BLIP(FlanT5xxl)beatsSOTASolarby7.6%
sion module on the SBU dataset [60]. Our approach is to onoverallQAaccuracy,whichshowsthatRA-BLIPcomplies
| fix the parameters | | of | the image | encoder | | and Q-Former, | and | | | | | | | | |
| ------------------ | --- | --- | --------- | ------- | --- | ------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
withscalinglaw[51]andcanimprovethegenerationaccuracy
solely fine-tune the parameters of the multimodal adaptive by using more advanced LLM. In order to prove that it is our
fusionmodule.WeusetheAdamWoptimizerandadoptcosine RA-BLIP framework rather than the advanced MLLM back-
learning rate of 1e-5, warmup of 1K steps, and batch size 64 bone that improves the generative performance, we conducted
for 10 epochs. For fine-tuning, we use the AdamW optimizer experiments on InstructBLIP [30] on WebQA. We used RA-
| with β | = 0.9, | β | = 0.99, | and | a weight | decay | of 0.01 | | | | | | | | |
| ------ | ------ | --- | ------- | --- | -------- | ----- | ------- | --- | --- | --- | --- | --- | --- | --- | --- |
1 2 BLIP’s optimal 0.89 search result for InstructBLIP generation
uniformly for three datasets. For WebQA retrieval, we use and found that its accuracy was 5.9% lower than Solar, which
a cosine learning rate of 1e-5, warmup of 1K steps, and batch further proves the effectiveness of RA-BLIP framework and
size 4 for 10 epochs. For WebQA ranking, we select the top ASKG.RA-BLIP(T5-base)and(T5-large)arenotpre-trained
10 samples to train the model with a cosine learning rate of to align with Q-Former, but they achieve 41.6% and 42.5%
| 1e-5, warmupof | | 1Ksteps | and | batchsize | 40 | for 5 | epochs. For | | | | | | | | |
| -------------- | --- | ------- | --- | --------- | --- | ----- | ----------- | -------- | ------------ | --- | ----- | ------- | --------------- | --- | ---------- |
| | | | | | | | | accuracy | respectively | | based | on 0.89 | search results, | | surpassing |
generation, we adopt cosine learning rate of 1e-6, warmup of Solar and proving it is the RA-BLIP framework rather than
1K steps, and batch size 4 for 10 epochs. We set learning rate LLM that improves performance. Compared with Solar and
of 5e-5 for T5-large and T5-base. SKURG which require additional model assistance, RA-BLIP
For MultimodalQA [5] and MMCoQA [6], we tested the does not use additional models, but it also achieves very good
results on the dev set of MultimodalQA as well as the dev 14%
| | | | | | | | | results in | retrieval | and | is | higher | than | MuRAG, | which |
| --- | --- | --- | --- | --- | --- | --- | --- | ---------- | --------- | --- | --- | ------ | ---- | ------ | ----- |
and test sets of the MMCoQA. For MultimodalQA retrieval, similarly does not use additional models.
weuseacosinelearningrateof1e-5,warmupof1Ksteps,and Results on MultimodalQA. We demonstrate Multi-
a batch size 4 for 10 epochs. For MultimodalQA generation, modalQA results in Table IV. MultimodalQA contains tables
we adopt a cosine learning rate of 1e-6, warmup of 1K steps, and has many multihop questions that require combining
and a batch size of 8 for 10 epochs. We set learning rate multimodal information. RA-BLIP also improved EM and
of 1e-5 for T5-large and 5e-5 for T5-base. For MMCoQA, F1 by 6.0% and 6.6%, respectively, compared to state-of-
we manually include the positive clues in the retrieval results the-art Solar, which demonstrates the generative ability of
without performing subsequent ranking,following the process our method in incorporating multihop knowledge. In addition,
IEEETRANSACTIONSONMULTIMEDIA 8
TABLEIV
MULTIMODALQADEV-SETRESULTS.∗REPRESENTSLLMISFLANT5XL,
WHILE†REPRESENTSLLMISFLANT5XXL.SINGLE-MODALAND
MUTLI-MODALRESPECTIVELYINDICATEWHETHERREASONINGRELIES ONSINGLE-MODALORMUTLI-MODALKNOWLEDGE.BOLDAND UNDERLINEDENOTETHEBESTANDSOTARESULTS,RESPECTIVELY.
Model Single-Modal Mutli-Modal All
EM F1 EM F1 EM F1
AutoRouting[5] 51.7 58.5 34.2 40.2 44.7 51.1
ImplicitDecomp[5] 51.6 58.4 44.6 51.2 48.8 55.5
) O D Q 7 [ O ) O D Q 7 [ [ O
SKURG[20] 66.1 69.7 52.5 57.2 59.8 64.0
Solar[21] 69.7 74.8 55.5 65.4 59.8 66.1
RA-BLIP(T5-base) 65.4 71.6 59.7 65.7 63.1 69.3
RA-BLIP(T5-large) 65.2 71.9 62.6 68.4 64.1 70.5
RA-BLIP* 70.1 77.6 59.3 65.5 65.8 72.7
RA-BLIP† 69.9 76.4 59.1 65.6 65.6 72.1
TABLEV
MMCOQATEST-DEV-SETRESULTS.∗REPRESENTSLLMISFLANT5XL, WHILE†REPRESENTSLLMISFLANT5XXL.BOLDANDUNDERLINE
DENOTETHEBESTANDSOTARESULTS,RESPECTIVELY.
Dev Test
Model
EM F1 EM F1
ORConvQA[61] 1.0 3.0 1.0 1.9
ManyModelQA[62] 0.7 2.3 1.0 1.8
MAE[6] 21.5 30.2 24.9 32.3
Solar[21] 56.8 62.5 57.3 64.6
RA-BLIP* 59.2 67.1 61.0 67.8
RA-BLIP† 58.7 66.7 59.5 66.7
RA-BLIP’s accuracy is ahead of SOTA Solar in both single-
modality and multi-modality, demonstrating our model can
well combine multiple contextual semantic knowledge for
cross-modal reasoning. Both RA-BLIP (T5-large) and RA-
BLIP(T5-base)surpassSolar,indicatingthattheperformance
improvements are due to the RA-BLIP framework rather than
the underlying LLM. Notably, the accuracy of more powerful
FlanT5xxl is lower than that of FlanT5xl, probably because
the powerful LLM is overfitted.
Results on MMCoQA. Our results on MMCoQA are
shown in Table V. Compared with WebQA and Multi-
modalQA,MMCoQArequiresthemodeltocorrectlyincorpo-
ratedialoghistoryanddevelopdeepmultimodalunderstanding
and reasoning capabilities across multiple conversations. RA-
BLIP achieves a margin of 3.7% enhancement over the best
Solar for the EM score and 3.2% for the F1 score in the
test split. These results suggest the generalization ability
and versatility of our model. Due to the small dataset, RA-
BLIP (FlanT5xxl) has resulted in overfitting, which prevents
further performance improvement.
C. Ablation Study
To analyze the effectiveness of our proposed method, we
conducted comprehensive ablations on the WebQA dataset in
both retrieval and generation stages. As shown in Fig. 4 (a),
RA-BLIP (FlanT5xl) and RA-BLIP (FlanT5xxl) with ASKG
strategy improved by 1.9% and 2.7% respectively, which
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5 $ % / , 3 Z $ 6 . *
) O D Q 7 [ O ) O D Q 7 [ [ O
\ F D U X F F $ $ 4
E , Q W H U D F W L R Q T X H U L H V
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2 Q H V H W R I T X H U L H V
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Fig.4. AblationstudyforRA-BLIPgenerationandretrievalonWebQA.
+ \ S H U S D U D P H W H U
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Fig.5. Influenceofvaryingthehyperparameterαofgenerationloss.
validates that employing ASKG strategy can assist LLMs in
efficientlydiscerningtherelevanceofretrievedknowledgeand
effectively activate the implicit capabilities of more powerful
LLMs. In Fig. 4 (b), we compared the effects of one set of
queries and multiple sets of queries, where one set of queries
has a significant improvement. This proves that compared
to multiple sets of queries that simply concatenate visual
informationfromdifferentimages,onesetofqueriescanbetter
interactwithandextractmixedvisualinformationfrommulti-
ple images at the feature level. We demonstrated the ablation
results of RA-BLIP at the retrieval stage in Fig. 4 (c). The
result of the retriever without the multimodal adaptive fusion
module exhibits lower performance than that of the complete
retriever, which suggests that fusing vision and language in a
unified semantic space is essential for multimodal retrieval.
By utilizing a retrieval-rank strategy, the retrieval result is
significantly improved 20%, demonstrating the necessity of
denoising confusing knowledge through fine-level ranking.
The retrieval-rank process employed in our method facilitates
precise retrieval among candidate knowledge sources that are
primarily relevant but potentially confusing.
D. Sensitivity Analysis
The α is the trade-off hyperparameter of generation loss
with ASKG strategy in Eq. (7). We set the range of α from
0.01 to 5. According to Fig. 5, we can see that even for such
a large range, the difference between the best and the lowest
resultsislessthan0.04%,indicatingourmethodisrobustand
insensitive to this parameter.
| IEEETRANSACTIONSONMULTIMEDIA | | | | | | | | | | | | | | 9 |
| ---------------------------- | --- | --- | --- | --- | --- | --- | -------- | ------ | ------- | --- | ------------ | ---------------- | --- | --- |
| | | | | | | | RA-BLIP. | In the | future, | we | will explore | image-multimodal | | |
(1) Q: What is in the man's mouth in L'hommeà la Tulipe?
Retrieval retrieval and multimodal-multimodal retrieval to realize om-
| | | Multimodal Knowledge Base | | | | | nipotent | retrieval-augmented | | models. | | | | |
| --- | ------------------- | ------------------------- | ------------------------------- | --- | --- | --- | -------- | ------------------- | --- | ------- | --- | --- | --- | --- |
| | L'homme à la tulipe | | HerfilmdébutwasinPasdepitiépour | | | | | | | | | | | |
lesfemmes(1951),followedbyFanfan
laTulipe(1952),inwhichsheplayed
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