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EchoSight: Advancing Visual-Language Models with Wiki Knowledge
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EchoSight: Advancing Visual-Language Models with Wiki Knowledge
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Knowledge-based Visual Question Answering (KVQA) tasks require answering questions about images using extensive background knowledge. Despite significant advancements, generative models often struggle with these tasks due to the limited integration of external knowledge. In this paper, we introduce EchoSight, a novel multimodal Retrieval-Augmented Generation (RAG) framework that enables large language models (LLMs) to answer visual questions requiring fine-grained encyclopedic knowledge. To strive for high-performing retrieval, EchoSight first searches wiki articles by using visual-only information, subsequently, these candidate articles are further reranked according to their relevance to the combined text-image query. This approach significantly improves the integration of multimodal knowledge, leading to enhanced retrieval outcomes and more accurate VQA responses. Our experimental results on the Encyclopedic VQA and InfoSeek datasets demonstrate that EchoSight establishes new state-of-the-art results in knowledge-based VQA, achieving an accuracy of 41.8% on Encyclopedic VQA and 31.3% on InfoSeek.
Forward citations
Cited by 9 Pith papers
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WikiVQABench: A Knowledge-Grounded Visual Question Answering Benchmark from Wikipedia and Wikidata
WikiVQABench is a human-curated collection of Wikipedia-based VQA items that require both visual evidence and external knowledge from Wikidata to answer correctly.
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WikiCLIP: An Efficient Contrastive Baseline for Open-domain Visual Entity Recognition
WikiCLIP delivers an efficient contrastive baseline for open-domain visual entity recognition that improves accuracy by 16% on OVEN unseen entities and runs nearly 100 times faster than leading generative models.
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Ground Then Rank: Revisiting Knowledge-Based VQA with Training-Free Entity Identification
A decoupled training-free IBA framework for KB-VQA selects entities via MLLM candidate choice then ranks evidence with off-the-shelf re-rankers, outperforming coupled fine-tuned baselines on Encyclopedic-VQA and InfoSeek.
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WikiCLIP: An Efficient Contrastive Baseline for Open-domain Visual Entity Recognition
WikiCLIP reaches 28.5% OVEN-unseen accuracy (vs 24.5% AutoVER) at 14.5 ms latency by vision-guided LLM embeddings plus hard-negative text swaps.
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Wiki-R1: Incentivizing Multimodal Reasoning for Knowledge-based VQA via Data and Sampling Curriculum
Controllable retrieval-difficulty curriculum plus reward-propagation sampling lets RL close the pretrain-to-KB-VQA gap and beat prior SOTA on two hard encyclopedic VQA benchmarks.
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MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval
MCERF delivers a 41.1% relative accuracy gain on the DesignQA benchmark by combining ColPali vision-language retrieval with four specialized reasoning modes and dynamic routing.
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Reason Before You Retrieve: Agentic Planning for Multi-modal RAG
MM-R2 claims SOTA multimodal RAG accuracy on InfoSeek and Encyclopedic VQA via intent grounding plus a 10-topic KnowledgeMap, but its teacher trajectories leak the gold Wikipedia page and omit the image.
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MathVis-Fine: Aligning Visual Supervision with Necessity via Progressive Dependency-Guided Training for Multimodal Mathematical Reasoning
MathVis-Fine proposes a dataset with fine-grained visual annotations and dependency ratings plus a progressive two-stage training paradigm to align visual supervision with sample-specific necessity in multimodal mathe...
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MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval
A multimodal RAG framework with ColPali retrieval and task-specific reasoning variants reports 32.6% relative improvement over prior RAG baselines on DesignQA, but the gain is inflated by test-set-fitted routing and a...
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