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Visual Question Answering

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하위 태스크

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평가표

30

보존된 평가 기록

334

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/shantipriyap/hausavqa

공식 코드

HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language

https://github.com/sharlinu/marc

공식 코드

Investigating Relational State Abstraction in Collaborative MARL

https://github.com/sharonal10/langint

공식 코드

Language-Informed Visual Concept Learning

https://github.com/shawnhuang497/medplib

공식 코드

Towards a Multimodal Large Language Model with Pixel-Level Insight for Biomedicine

https://github.com/shengyuzhang/DeVLBert

공식 코드

DeVLBert: Learning Deconfounded Visio-Linguistic Representations

https://github.com/shenyedepisa/rscmqa

공식 코드

Copy-Move Forgery Detection and Question Answering for Remote Sensing Image

https://github.com/shi-labs/cumo

공식 코드

CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts

https://github.com/shi-labs/vcoder

공식 코드

VCoder: Versatile Vision Encoders for Multimodal Large Language Models

https://github.com/shidaizi/remud

공식 코드

HAIBU-ReMUD: Reasoning Multimodal Ultrasound Dataset and Model Bridging to General Specific Domains

https://github.com/shikiw/modality-integration-rate

공식 코드

Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate

https://github.com/shikras/shikra

공식 코드

Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic

https://github.com/shiqichen17/adaptvis

공식 코드

Why Is Spatial Reasoning Hard for VLMs? An Attention Mechanism Perspective on Focus Areas

https://github.com/shishicode/agot

공식 코드

Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning

https://github.com/showlab/clvqa

공식 코드

Symbolic Replay: Scene Graph as Prompt for Continual Learning on VQA Task

https://github.com/showlab/lova3

공식 코드

LOVA3: Learning to Visual Question Answering, Asking and Assessment

https://github.com/shrivatsagrawal/are-llms-the-master-of-all-trades-exploring-domain-agnostic-reasoning-skills-of-llms

공식 코드

Are LLMs the Master of All Trades? : Exploring Domain-Agnostic Reasoning Skills of LLMs

https://github.com/shtuplus/iccc_cvpr2024

공식 코드

Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language Reasoning

https://github.com/shuvenduroy/multimodal

공식 코드

Benchmarking Vision-Language Contrastive Methods for Medical Representation Learning

https://github.com/siddk/vqa-outliers

공식 코드

Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering

https://github.com/sidgan/whats_in_a_question

공식 코드

What's in a Question: Using Visual Questions as a Form of Supervision

https://github.com/simula/Kvasir-VQA

공식 코드

Kvasir-VQA: A Text-Image Pair GI Tract Dataset

https://github.com/sivandoveh/iploc

공식 코드

Teaching VLMs to Localize Specific Objects from In-context Examples

https://github.com/sjoshi804/mm-gen

공식 코드

MM-GEN: Enhancing Task Performance Through Targeted Multimodal Data Curation

https://github.com/skywalkerzhang/Defeasible_Visual_Entailment

공식 코드

Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven Optimization

https://github.com/sled-group/COMFORT

공식 코드

Do Vision-Language Models Represent Space and How? Evaluating Spatial Frame of Reference Under Ambiguities

https://github.com/sled-group/moh

공식 코드

Multi-Object Hallucination in Vision-Language Models

https://github.com/smileformylove/smartfreeedit

공식 코드

SmartFreeEdit: Mask-Free Spatial-Aware Image Editing with Complex Instruction Understanding

https://github.com/snap-stanford/med-flamingo

공식 코드

Med-Flamingo: a Multimodal Medical Few-shot Learner

https://github.com/snova-jonathanl/hudocvqa

공식 코드

Synthetic Document Question Answering in Hungarian

https://github.com/social-ai-studio/pro-cap

공식 코드

Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection

https://github.com/sohojoe/clip_visual-spatial-reasoning

공식 코드

Visual Spatial Reasoning

https://github.com/souradipp76/mm-poe

공식 코드

MM-PoE: Multiple Choice Reasoning via. Process of Elimination using Multi-Modal Models

https://github.com/sourjyadip/ervqa-data

공식 코드

ERVQA: A Dataset to Benchmark the Readiness of Large Vision Language Models in Hospital Environments

https://github.com/spearsheep/APT-Architectural-Planning-LLM-Agent

공식 코드

APT: Architectural Planning and Text-to-Blueprint Construction Using Large Language Models for Open-World Agents

https://github.com/standardmodelbio/llama3-med

공식 코드

Advancing High Resolution Vision-Language Models in Biomedicine

https://github.com/stare-bench/stare

공식 코드

Unfolding Spatial Cognition: Evaluating Multimodal Models on Visual Simulations

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