https://github.com/vectorinstitute/humanibench
공식 코드HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation
Papers with Code 아카이브
Visual Question Answering
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/vedantpalit/towards-vision-language-mechanistic-interpretability
공식 코드Towards Vision-Language Mechanistic Interpretability: A Causal Tracing Tool for BLIP
https://github.com/vinceouti/citynavagent
공식 코드https://github.com/vipulgupta1011/swapmix
공식 코드https://github.com/vision-cair/minigpt-4
공식 코드MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
https://github.com/vision-cair/minigpt-med
공식 코드MiniGPT-Med: Large Language Model as a General Interface for Radiology Diagnosis
https://github.com/vision-x-nyu/thinking-in-space
공식 코드Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces
https://github.com/visual-haystacks/vhs_benchmark
공식 코드Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark
https://github.com/visual-text-QA/VTQA-Demo
공식 코드VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media Reasoning
https://github.com/visualaikhu/missing-avqa
공식 코드https://github.com/vita-group/o1-planning
공식 코드On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability
https://github.com/vityavitalich/imad
공식 코드https://github.com/vl2g/KaLMA
공식 코드https://github.com/vlgiitr/Are-VLMs-Really-Blind
공식 코드https://github.com/vmurahari3/visdial-bert
공식 코드Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline
https://github.com/vqaonline/vqaonlinevisualization
공식 코드Fully Authentic Visual Question Answering Dataset from Online Communities
https://github.com/vt-nlp/socratic-questioning
공식 코드The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models
https://github.com/vuhoangminh/vqa_medical
공식 코드A Question-Centric Model for Visual Question Answering in Medical Imaging
https://github.com/wad3birch/AutoBench-V
공식 코드AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?
https://github.com/waltonfuture/InstructionGPT-4
공식 코드InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4
https://github.com/wangzheallen/STL-VQA
공식 코드Structured Triplet Learning with POS-tag Guided Attention for Visual Question Answering
https://github.com/wayveai/lingoqa
공식 코드https://github.com/weichenzh/open3dvqa
공식 코드https://github.com/weihuanglin/inf-llava
공식 코드INF-LLaVA: Dual-perspective Perception for High-Resolution Multimodal Large Language Model
https://github.com/wenboown/generative-ai-for-semiconductor-physical-design
공식 코드Enhancing Reasoning to Adapt Large Language Models for Domain-Specific Applications
https://github.com/wenchuan-zhang/patho-r1
공식 코드Patho-R1: A Multimodal Reinforcement Learning-Based Pathology Expert Reasoner
https://github.com/wenhuang2000/vhtest
공식 코드https://github.com/wenkehuang/awesome-mllm-tuning
공식 코드Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model
https://github.com/wenliangdai/vlp-object-hallucination
공식 코드Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training
https://github.com/wentaoyuan/sornet
공식 코드SORNet: Spatial Object-Centric Representations for Sequential Manipulation
https://github.com/westlake-baichuan-mllm/bc-omni
공식 코드https://github.com/westlake-baichuan-mllm/ocean-omni
공식 코드https://github.com/whb139426/qa-prompts-eccv-24
공식 코드https://github.com/windweller/Pragmatic-ISIC
공식 코드https://github.com/winogavil/winogavil-experiments
공식 코드WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models
https://github.com/wjnwjn59/CLAugViVQA
공식 코드Enhancing Vietnamese VQA through Curriculum Learning on Raw and Augmented Text Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.