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FS-MEVQA
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The Few-Shot Multimodal Explanation for Visual Question Answering (FS-MEVQA) task aims to learn MEVQA from few training samples.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/qwenlm/qwen-vl
공식 코드Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
https://github.com/szzexpoi/rex
공식 코드https://github.com/thudm/cogvlm
공식 코드https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/cogvlm
https://github.com/AUCOHL/RTL-Repo
https://github.com/LivXue/FS-MEVQA
https://github.com/LivXue/VCIN
https://github.com/MS-P3/code5/tree/main/cogvlm
https://github.com/THUDM/CogAgent
https://github.com/brandon3964/multimodal-task-vector
Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
https://github.com/dlvuldet/primevul
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
https://github.com/emrgnt-cmplxty/zero-shot-replication
https://github.com/eternityyw/tram-benchmark
https://github.com/ethz-privsec/superhuman-ai-consistency
https://github.com/ethz-spylab/superhuman-ai-consistency
https://github.com/gpt4life/alpagasus
https://github.com/lflage/openfactscore
https://github.com/shmsw25/factscore
https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models
https://github.com/zach-zhiling-zheng/reticular_chemist
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.