https://github.com/pengfeiliheu/m2i2
공식 코드Self-supervised vision-language pretraining for Medical visual question answering
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/penghao-wu/vstar
공식 코드V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs
https://github.com/pengts/vw-lmm
공식 코드https://github.com/peteanderson80/Matterport3DSimulator
공식 코드https://github.com/peteanderson80/bottom-up-attention
공식 코드Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/pfZhu/duma_code
공식 코드https://github.com/phdabel/robin
공식 코드RoBIn: A Transformer-Based Model For Risk Of Bias Inference With Machine Reading Comprehension
https://github.com/philippds/sphyr
공식 코드SPhyR: Spatial-Physical Reasoning Benchmark on Material Distribution
https://github.com/phoebussi/compress-robust-vqa
공식 코드Compressing And Debiasing Vision-Language Pre-Trained Models for Visual Question Answering
https://github.com/phoebussi/mmbs
공식 코드Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning
https://github.com/phoebussi/thinking-while-observing
공식 코드https://github.com/phoebussi/vqa-vs
공식 코드Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA
https://github.com/phoenixz810/mg-llava
공식 코드MG-LLaVA: Towards Multi-Granularity Visual Instruction Tuning
https://github.com/phoenixz810/omnialign-v
공식 코드OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human Preference
https://github.com/pickxiguapi/embodied-fsd
공식 코드From Seeing to Doing: Bridging Reasoning and Decision for Robotic Manipulation
https://github.com/pjlab-adg/gpt4v-ad-exploration
공식 코드On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving
https://github.com/pku-icst-mipl/finedefics_iclr2025
공식 코드https://github.com/pku-yuangroup/chat-univi
공식 코드https://github.com/pliang279/LG-FedAvg
공식 코드Think Locally, Act Globally: Federated Learning with Local and Global Representations
https://github.com/pluslabnlp/econet
공식 코드ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning
https://github.com/pluslabnlp/ester
공식 코드ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning
https://github.com/polymathbenchmark/PolyMATH
공식 코드Polymath: A Challenging Multi-modal Mathematical Reasoning Benchmark
https://github.com/pramodkaushik/acl18_results
공식 코드https://github.com/prdwb/okvqa-release
공식 코드Passage Retrieval for Outside-Knowledge Visual Question Answering
https://github.com/premsrit/SPARTUNQChain
공식 코드https://github.com/princeton-nlp/CharXiv
공식 코드CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs
https://github.com/princeton-nlp/carets
공식 코드CARETS: A Consistency And Robustness Evaluative Test Suite for VQA
https://github.com/princeton-nlp/ptp
공식 코드https://github.com/princeton-pli/vlm_s2h
공식 코드Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?
https://github.com/princeton-vl/SpatialSense
공식 코드SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition
https://github.com/princeton-vl/uniqa-3d
공식 코드https://github.com/princetonvisualai/pointingqa
공식 코드Point and Ask: Incorporating Pointing into Visual Question Answering
https://github.com/prithv1/cvpr2017_counting
공식 코드https://github.com/promptslab/rosettaeval
공식 코드https://github.com/protagolabs/SpatialEval
공식 코드https://github.com/psudslab/FEDMEKI
공식 코드FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.