REVIEW 6 cited by
A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The Visual Question Answering (VQA) task aspires to provide a meaningful testbed for the development of AI models that can jointly reason over visual and natural language inputs. Despite a proliferation of VQA datasets, this goal is hindered by a set of common limitations. These include a reliance on relatively simplistic questions that are repetitive in both concepts and linguistic structure, little world knowledge needed outside of the paired image, and limited reasoning required to arrive at the correct answer. We introduce A-OKVQA, a crowdsourced dataset composed of a diverse set of about 25K questions requiring a broad base of commonsense and world knowledge to answer. In contrast to the existing knowledge-based VQA datasets, the questions generally cannot be answered by simply querying a knowledge base, and instead require some form of commonsense reasoning about the scene depicted in the image. We demonstrate the potential of this new dataset through a detailed analysis of its contents and baseline performance measurements over a variety of state-of-the-art vision-language models. Project page: http://a-okvqa.allenai.org/
Forward citations
Cited by 6 Pith papers
-
SoftReason: A Fully Differentiable Neuro-Soft-Symbolic Deductive Reasoning Architecture over High-Dimensional Perceptual Data
SoftReason learns a differentiable soft deductive closure operator over perceptual facts and reports 94.3% Hit@1 on KVQA entity linking.
-
NLKI: A lightweight Natural Language Knowledge Integration Framework for Improving Small VLMs in Commonsense VQA Tasks
NLKI combines fine-tuned dense retrieval, LLM-generated explanations, and noise-robust losses to improve small VLMs on commonsense VQA.
-
Reconstructing Content with Collaborative Attention for Universal Multimodal Representation Learning
CoCoA forces an MLLM to reconstruct masked text through a single EOS token, improving multimodal embedding quality on MMEB-V1 and matching MoCa at 3B with far less pretraining data.
-
Energy-Guided Decoding for Object Hallucination Mitigation
An energy-guided, training-free decoding rule that chooses the layer with minimal energy reduces object hallucination and yes-bias on several benchmarks.
-
Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models
Vision-language models improve little, often not at all, when given demonstrations, even when demonstrations contain explicit reasoning steps.
-
An Open-Source Software Toolkit & Benchmark Suite for the Evaluation and Adaptation of Multimodal Action Models
MultiNet provides an open-source benchmark, data SDK, evaluation harness, and adapted VLA models for assessing generalization across vision, language, and action tasks.
Discussion (0). Continue with ORCID to comment.