https://github.com/rikeilong/bay-cat
공식 코드CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios
Papers with Code 아카이브
Visual Question Answering (VQA)
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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/rikeilong/mcd-foravqa
공식 코드Answering Diverse Questions via Text Attached with Key Audio-Visual Clues
https://github.com/ritual-uh/edustory
공식 코드https://github.com/rizar/systematic-generalization-sqoop
공식 코드Systematic Generalization: What Is Required and Can It Be Learned?
https://github.com/rkadlec/asreader
공식 코드https://github.com/rlhf-v/rlaif-v
공식 코드RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness
https://github.com/rlhf-v/rlhf-v
공식 코드https://github.com/robinzixuan/Video-Question-Answering-HRI
공식 코드Open-Ended Multi-Modal Relational Reasoning for Video Question Answering
https://github.com/robotics-upo/og-sgg
공식 코드https://github.com/rohit901/VANE-Bench
공식 코드VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs
https://github.com/rshnk73/pistaq-sreqa
공식 코드Disentangling Extraction and Reasoning in Multi-hop Spatial Reasoning
https://github.com/rubenpt91/MP-DocVQA-Framework
공식 코드https://github.com/rucaibox/lamoc
공식 코드Zero-shot Visual Question Answering with Language Model Feedback
https://github.com/rucaibox/pope
공식 코드Evaluating Object Hallucination in Large Vision-Language Models
https://github.com/russellyq/medhalltune
공식 코드https://github.com/s3vqa/s3vqa.github.io
공식 코드Select, Substitute, Search: A New Benchmark for Knowledge-Augmented Visual Question Answering
https://github.com/saccharomycetes/mllms_know
공식 코드MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs
https://github.com/saccharomycetes/visual_crop_zsvqa
공식 코드Towards Perceiving Small Visual Details in Zero-shot Visual Question Answering with Multimodal LLMs
https://github.com/safolab-wisc/fiubench
공식 코드Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset
https://github.com/sahilkhose/floodnet_vqa
공식 코드https://github.com/sail-sg/vgt
공식 코드https://github.com/salanueva/CBM
공식 코드Image Captioning for Effective Use of Language Models in Knowledge-Based Visual Question Answering
https://github.com/salesforce/BiST
공식 코드BiST: Bi-directional Spatio-Temporal Reasoning for Video-Grounded Dialogues
https://github.com/salesforce/alpro
공식 코드Align and Prompt: Video-and-Language Pre-training with Entity Prompts
https://github.com/salesforce/lavis
공식 코드Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
https://github.com/salesforceairesearch/taco
공식 코드TACO: Learning Multi-modal Action Models with Synthetic Chains-of-Thought-and-Action
https://github.com/samarth4149/scramble
공식 코드Enhancing Compositional Reasoning in Vision-Language Models with Synthetic Preference Data
https://github.com/sameerdharur/sorting-vqa
공식 코드SOrT-ing VQA Models : Contrastive Gradient Learning for Improved Consistency
https://github.com/sandeep-sm/live-sc
공식 코드Subjective and Objective Analysis of Indian Social Media Video Quality
https://github.com/sangwu99/simplot
공식 코드SIMPLOT: Enhancing Chart Question Answering by Distilling Essentials
https://github.com/sanjayss34/codevqa
공식 코드https://github.com/saptarshi059/cdqa-project
공식 코드Towards Efficient Methods in Medical Question Answering using Knowledge Graph Embeddings
https://github.com/saurabhhssaurabh/reviews_summarization
공식 코드MRCBert: A Machine Reading ComprehensionApproach for Unsupervised Summarization
https://github.com/sayazhang/spatial-query-by-sketch
공식 코드DeepSSN: a deep convolutional neural network to assess spatial scene similarity
https://github.com/sberbank-ai/fusion_brain_aij2021
공식 코드https://github.com/schlevik/dataset-analysis
공식 코드A Framework for Evaluation of Machine Reading Comprehension Gold Standards
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.