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Visual Question Answering

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Papers

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Showing 976–1000 of 2177 papers

TitleStatusHype
OMCAT: Omni Context Aware Transformer—0
MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling—0
Eliminating the Language Bias for Visual Question Answering with fine-grained Causal Intervention—0
Surgical-LLaVA: Toward Surgical Scenario Understanding via Large Language and Vision Models—0
MMCOMPOSITION: Revisiting the Compositionality of Pre-trained Vision-Language Models—0
VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment—0
Declarative Knowledge Distillation from Large Language Models for Visual Question Answering DatasetsCode0
Zero-shot Commonsense Reasoning over Machine ImaginationCode0
ViT3D Alignment of LLaMA3: 3D Medical Image Report Generation—0
Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training—0
Emerging Pixel Grounding in Large Multimodal Models Without Grounding Supervision—0
PAR: Prompt-Aware Token Reduction Method for Efficient Large Multimodal Models—0
Beyond Captioning: Task-Specific Prompting for Improved VLM Performance in Mathematical Reasoning—0
Multimodal Large Language Models and Tunings: Vision, Language, Sensors, Audio, and BeyondCode0
Core Tokensets for Data-efficient Sequential Training of TransformersCode0
ERVQA: A Dataset to Benchmark the Readiness of Large Vision Language Models in Hospital EnvironmentsCode0
VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks—0
MM-R^3: On (In-)Consistency of Multi-modal Large Language Models (MLLMs)—0
TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable QuestionsCode0
Gamified crowd-sourcing of high-quality data for visual fine-tuning—0
Backdooring Vision-Language Models with Out-Of-Distribution Data—0
Why context matters in VQA and Reasoning: Semantic interventions for VLM input modalities—0
BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured DataCode0
FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks—0
Unleashing the Potentials of Likelihood Composition for Multi-modal Language ModelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MMCTAgent (GPT-4 + GPT-4V)GPT-4 score74.24—Unverified
2Qwen2-VL-72BGPT-4 score74—Unverified
3InternVL2.5-78BGPT-4 score72.3—Unverified
4GPT-4o +text rationale +IoTGPT-4 score72.2—Unverified
5Lyra-ProGPT-4 score71.4—Unverified
6GLM-4V-PlusGPT-4 score71.1—Unverified
7Phantom-7BGPT-4 score70.8—Unverified
8InternVL2.5-38BGPT-4 score68.8—Unverified
9InternVL2-26B (SGP, token ratio 64%)GPT-4 score65.6—Unverified
10Baichuan-Omni (7B)GPT-4 score65.4—Unverified