Papers with Code 아카이브
Generative Visual Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 1
- 보존된 평가 기록
- 3
Generating answers in free form to questions posed about images.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/gregor-ge/mblip
https://github.com/happen2me/cross-gnn
https://github.com/huggingface/transformers
https://github.com/jiwanchung/vlis
https://github.com/junshutang/Make-It-3D
https://github.com/kdr/videorag-mrr2024
https://github.com/lastmile-ai/aiconfig
LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models
https://github.com/linzhiqiu/clip-flant5
https://github.com/lucidrains/flamingo-pytorch
https://github.com/mlfoundations/open_flamingo
https://github.com/opengvlab/llama-adapter
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
https://github.com/pku-yuangroup/video-bench
https://github.com/pwc-1/Paper-9/tree/main/2/minigpt4
https://github.com/qiujihao19/artemis
Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
https://github.com/rabiulcste/vqazero
https://github.com/skyworkai/moe-plus-plus
https://github.com/skyworkai/moh
https://github.com/thudm/visualglm-6b
https://github.com/tlacombe/topt
PLLay: Efficient Topological Layer based on Persistence Landscapes
https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models
https://github.com/xinding-sys/StreamMind
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/yangyucheng000/University/tree/main/model-2/blip_2
https://github.com/yukw777/videoblip
https://github.com/zihanzhaosjtu/librisqa
PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.