Papers with Code 아카이브

Visual Question Answering (VQA)

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 9
평가표: 76
보존된 평가 기록: 725

Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.

Image Source: visualqa.org

코드

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

https://github.com/ai4sd/multiscale-byte-lm

공식 코드

Multiscale Byte Language Models -- A Hierarchical Architecture for Causal Million-Length Sequence Modeling

https://github.com/aidaslab/second

공식 코드

SECOND: Mitigating Perceptual Hallucination in Vision-Language Models via Selective and Contrastive Decoding

https://github.com/aidc-ai/ovis

공식 코드

Ovis: Structural Embedding Alignment for Multimodal Large Language Model

https://github.com/aied2021TRMRC/AIED_2021_TRMRC_code

공식 코드

Automatic Task Requirements Writing Evaluation via Machine Reading Comprehension

https://github.com/aifeg/benchgpt

공식 코드

BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models

https://github.com/aifeg/benchlmm

공식 코드

BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models

https://github.com/ailab-cvc/vl-gpt

공식 코드

VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and Generation

https://github.com/aim-uofa/SegAgent

공식 코드

SegAgent: Exploring Pixel Understanding Capabilities in MLLMs by Imitating Human Annotator Trajectories

https://github.com/aimagelab/reflectiva

공식 코드

Augmenting Multimodal LLMs with Self-Reflective Tokens for Knowledge-based Visual Question Answering

https://github.com/aimbrain/vqa-project

공식 코드

Learning Conditioned Graph Structures for Interpretable Visual Question Answering

https://github.com/aiming-lab/mmedpo

공식 코드

MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware Multimodal Preference Optimization

https://github.com/aioz-ai/ECCVW20_MILQT

공식 코드

Multiple interaction learning with question-type prior knowledge for constraining answer search space in visual question answering

https://github.com/aioz-ai/ICCV19_VQA-CTI

공식 코드

Compact Trilinear Interaction for Visual Question Answering

https://github.com/aioz-ai/MICCAI19-MedVQA

공식 코드

Overcoming Data Limitation in Medical Visual Question Answering

https://github.com/aioz-ai/MICCAI21_MMQ

공식 코드

Multiple Meta-model Quantifying for Medical Visual Question Answering

https://github.com/aioz-ai/cfr_vqa

공식 코드

Coarse-to-Fine Reasoning for Visual Question Answering

https://github.com/aioz-ai/crf_vqa

공식 코드

Coarse-to-Fine Reasoning for Visual Question Answering

https://github.com/airsplay/lxmert

공식 코드

LXMERT: Learning Cross-Modality Encoder Representations from Transformers

https://github.com/aistairc/virtualhome2kg

공식 코드

Synthesizing Event-centric Knowledge Graphs of Daily Activities Using Virtual Space

https://github.com/aitmlouk/fedn-client-fedqas-tf

공식 코드

FedQAS: Privacy-aware machine reading comprehension with federated learning

https://github.com/akirafukui/vqa-mcb

공식 코드

Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding

https://github.com/akshay107/mimic-and-fool

공식 코드

Mimic and Fool: A Task Agnostic Adversarial Attack

https://github.com/akshaygopalkr/em-vlm4ad

공식 코드

Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous Driving

https://github.com/alab-nii/eval_vl_glue

공식 코드

Effect of Visual Extensions on Natural Language Understanding in Vision-and-Language Models

https://github.com/alanaai/evud

공식 코드

AlanaVLM: A Multimodal Embodied AI Foundation Model for Egocentric Video Understanding

https://github.com/alanlusun/openkd

공식 코드

OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint Detection

https://github.com/alexdelitzas/3d-vqa

공식 코드

CLIP-Guided Vision-Language Pre-training for Question Answering in 3D Scenes

https://github.com/alexrame/rewardedsoups

공식 코드

Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards

https://github.com/ali-vilab/VGen

공식 코드

Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation

https://github.com/alibaba-nlp/omnisearch

공식 코드

Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent

https://github.com/alibaba/AliceMind

공식 코드

StructuralLM: Structural Pre-training for Form Understanding

https://github.com/alibaba/AliceMind/tree/main/mPLUG

공식 코드

mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections

https://github.com/alibaba/EasyNLP

공식 코드

KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering

https://github.com/alibaba/conv-llava

공식 코드

ConvLLaVA: Hierarchical Backbones as Visual Encoder for Large Multimodal Models

https://github.com/alibabaresearch/advancedliteratemachinery

공식 코드

ProcTag: Process Tagging for Assessing the Efficacy of Document Instruction Data

https://github.com/aliborji/binaryvqa

공식 코드

BinaryVQA: A Versatile Test Set to Evaluate the Out-of-Distribution Generalization of VQA Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.