https://github.com/zhilingyan/gpt4v-medical-report
공식 코드Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V
Papers with Code 아카이브
Visual Question Answering (VQA)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/zhiqic/chartreader
공식 코드ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rules
https://github.com/zhiweizhang97/arsjointmodel
공식 코드Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification
https://github.com/zhjohnchan/ptunifier
공식 코드Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts
https://github.com/zhoucz97/ecpe-mm-r
공식 코드https://github.com/zhougroup/BAM
공식 코드https://github.com/zhoukanglei/asal_cvqa
공식 코드https://github.com/zhourobotics/reasondrive
공식 코드https://github.com/zhouxian/act3d-chained-diffuser
공식 코드Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation
https://github.com/zhuchen03/vqa-sva
공식 코드https://github.com/zhuemann/contextualsegmentation
공식 코드ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax
https://github.com/zhuyiche/llava-phi
공식 코드LLaVA-Phi: Efficient Multi-Modal Assistant with Small Language Model
https://github.com/zhwwwww/attribute-comprehension-of-vlms
공식 코드Evaluating Attribute Comprehension in Large Vision-Language Models
https://github.com/zihao-ai/vdc
공식 코드https://github.com/zihuaevan/acfmodel
공식 코드Adaptively Clustering Neighbor Elements for Image-Text Generation
https://github.com/zinengtang/tvlt
공식 코드https://github.com/ziqipang/lm4visualencoding
공식 코드Frozen Transformers in Language Models Are Effective Visual Encoder Layers
https://github.com/zivenzhu/multi-chart-qa
공식 코드MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems
https://github.com/ziyuguo99/point-bind_point-llm
공식 코드https://github.com/zjukg/kg-mm-survey
공식 코드Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
https://github.com/zlccccc/3dgqa
공식 코드Towards Explainable 3D Grounded Visual Question Answering: A New Benchmark and Strong Baseline
https://github.com/zrrskywalker/llama-adapter
공식 코드LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
https://github.com/zted/sbvqa
공식 코드https://github.com/zwenyu/SPHERE-VLM
공식 코드SPHERE: A Hierarchical Evaluation on Spatial Perception and Reasoning for Vision-Language Models
https://github.com/zwq2018/embodied_reasoner
공식 코드Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks
https://github.com/zwq2018/multi-modal-self-instruct
공식 코드https://github.com/zwq803/mm-skin
공식 코드https://github.com/zwx8981/BVQA-2021
공식 코드Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception
https://github.com/zyq0000/gama
공식 코드Think Before You Act: A Two-Stage Framework for Mitigating Gender Bias Towards Vision-Language Tasks
https://github.com/zytx121/awesome-vlgfm
공식 코드https://github.com/zyz0000/finbert-mrc
공식 코드https://github.com/zzc-1998/md-vqa
공식 코드MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos
https://github.com/zzc-1998/vqa_pc
공식 코드Evaluating Point Cloud from Moving Camera Videos: A No-Reference Metric
https://github.com/zzxslp/som-llava
공식 코드List Items One by One: A New Data Source and Learning Paradigm for Multimodal LLMs
https://gitlab.com/pl8787/CaffeAttention
공식 코드https://huggingface.co/TownsWu/PEG
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.