https://github.com/dvlab-research/LLMGA
공식 코드LLMGA: Multimodal Large Language Model based Generation Assistant
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/dvlab-research/Lyra
공식 코드Lyra: An Efficient and Speech-Centric Framework for Omni-Cognition
https://github.com/dvlab-research/llama-vid
공식 코드LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models
https://github.com/dvlab-research/minigemini
공식 코드Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
https://github.com/dvlab-research/visionzip
공식 코드VisionZip: Longer is Better but Not Necessary in Vision Language Models
https://github.com/dz-osamu/UMIT
공식 코드UMIT: Unifying Medical Imaging Tasks via Vision-Language Models
https://github.com/e-bug/iglue
공식 코드IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
https://github.com/e-bug/volta
공식 코드IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
https://github.com/easonnie/mlp-vil
공식 코드MLP Architectures for Vision-and-Language Modeling: An Empirical Study
https://github.com/ecnu-dase-nlp/rqp
공식 코드https://github.com/edchengg/infoseek_eval
공식 코드Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?
https://github.com/edchengg/oven_eval
공식 코드Open-domain Visual Entity Recognition: Towards Recognizing Millions of Wikipedia Entities
https://github.com/efficient-large-model/vila
공식 코드https://github.com/egeozsoy/LF-SGG
공식 코드https://github.com/ekinakyurek/lexsym
공식 코드https://github.com/elad-amrani/ssml
공식 코드Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning
https://github.com/ellenzhuwang/VK_OOD
공식 코드Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis
https://github.com/elvisnava/hyperclip
공식 코드https://github.com/elviswxy/LVLM_fairness
공식 코드https://github.com/emanuelevivoli/awesome-comics-understanding
공식 코드One missing piece in Vision and Language: A Survey on Comics Understanding
https://github.com/embedded-robotics/path-rag
공식 코드Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question Answering
https://github.com/embodied-generalist/embodied-generalist
공식 코드https://github.com/embodiedcity/embodied-r.code
공식 코드https://github.com/endernewton/tf-faster-rcnn
공식 코드https://github.com/ensec-ai/vlm-uncertainty-bench
공식 코드https://github.com/eric-ai-lab/probmed
공식 코드https://github.com/ericlee8/multi-party-dialogue-mrc
공식 코드https://github.com/ermongroup/teochat
공식 코드TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data
https://github.com/erobic/bias-mitigators
공식 코드https://github.com/erobic/negative_analysis_of_grounding
공식 코드Visual Grounding Methods for VQA are Working for the Wrong Reasons!
https://github.com/erobic/ramen
공식 코드Answer Them All! Toward Universal Visual Question Answering Models
https://github.com/esteng/ambiguous_vqa
공식 코드Why Did the Chicken Cross the Road? Rephrasing and Analyzing Ambiguous Questions in VQA
https://github.com/ethanjperez/film
공식 코드https://github.com/eujhwang/meme-cap
공식 코드https://github.com/evansh666/evogen
공식 코드Progressive Compositionality In Text-to-Image Generative Models
https://github.com/evaportelance/vqa-function-word-learning
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.