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Visual Question Answering
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/deep-spin/mcan-vqa-continuous-attention
공식 코드https://github.com/deep-spin/sparse_continuous_distributions
공식 코드https://github.com/deepmind/slim-dataset
공식 코드https://github.com/deepmind/svo_probes
공식 코드https://github.com/deepseek-ai/deepseek-vl
공식 코드DeepSeek-VL: Towards Real-World Vision-Language Understanding
https://github.com/deepseek-ai/deepseek-vl2
공식 코드DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding
https://github.com/deepseek-ai/janus
공식 코드Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation
https://github.com/digitalphonetics/intrinsic-subgraph-generation-for-vqa
공식 코드Intrinsic Subgraph Generation for Interpretable Graph based Visual Question Answering
https://github.com/divamgupta/sbevnet-stereo-layout-estimation
공식 코드https://github.com/divelab/vqa-text
공식 코드Learning Convolutional Text Representations for Visual Question Answering
https://github.com/djl11/ESM
공식 코드https://github.com/dlcv-buaa/tinyllavabench
공식 코드TinyLLaVA: A Framework of Small-scale Large Multimodal Models
https://github.com/dlyuangod/tinygpt-v
공식 코드TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones
https://github.com/dongyh20/chain-of-spot
공식 코드Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models
https://github.com/double125/Graph-Matching-Attention
공식 코드Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question Answering
https://github.com/dpclip/dpclip
공식 코드Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training
https://github.com/dragonlzm/pave
공식 코드https://github.com/dreichCSL/GQA_generalization_splits
공식 코드https://github.com/dreichcsl/fpvg
공식 코드https://github.com/dreichcsl/truevg
공식 코드Uncovering the Full Potential of Visual Grounding Methods in VQA
https://github.com/dvlab-research/LLMGA
공식 코드LLMGA: Multimodal Large Language Model based Generation Assistant
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/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/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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.