https://github.com/togethercomputer/dragonfly
공식 코드Dragonfly: Multi-Resolution Zoom-In Encoding Enhances Vision-Language Models
Papers with Code 아카이브
Visual Question Answering
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/toloka/wsdmcup2023
공식 코드https://github.com/tomohiro-sawada/cs7643-final-project
공식 코드https://github.com/tong-zeng/dvbench
공식 코드https://github.com/tossowski/multimodalpromptretrieval
공식 코드Multimodal Prompt Retrieval for Generative Visual Question Answering
https://github.com/tree985/treble-counterfactual-vlms
공식 코드Treble Counterfactual VLMs: A Causal Approach to Hallucination
https://github.com/tri-ml/prismatic-vlms
공식 코드Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
https://github.com/tri-ml/vlm-evaluation
공식 코드Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models
https://github.com/triumph123aaa/grid-augmented-vision
공식 코드https://github.com/tsinghua-fib-lab/CityEQA
공식 코드CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space
https://github.com/tsinghua-fib-lab/citygpt
공식 코드CityGPT: Empowering Urban Spatial Cognition of Large Language Models
https://github.com/tsinghua-msiip/uni-med
공식 코드Uni-Med: A Unified Medical Generalist Foundation Model For Multi-Task Learning Via Connector-MoE
https://github.com/tttamaki/vqd
공식 코드An Entropy Clustering Approach for Assessing Visual Question Difficulty
https://github.com/tttyuntian/vlm_lexical_grounding
공식 코드Does Vision-and-Language Pretraining Improve Lexical Grounding?
https://github.com/ttumyche/cxreasonbench
공식 코드CXReasonBench: A Benchmark for Evaluating Structured Diagnostic Reasoning in Chest X-rays
https://github.com/tuhinjubcse/faithfuledits_emnlp2023
공식 코드Learning to Follow Object-Centric Image Editing Instructions Faithfully
https://github.com/tulerfeng/video-r1
공식 코드https://github.com/turingmotors/nuscenes-mqa
공식 코드https://github.com/tutujingyugang1/ChatVLA_public
공식 코드ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model
https://github.com/tyler-hayes/REMIND
공식 코드REMIND Your Neural Network to Prevent Catastrophic Forgetting
https://github.com/uds-lsv/premise
공식 코드Label-Descriptive Patterns and Their Application to Characterizing Classification Errors
https://github.com/ukplab/acl2024-sparc-and-sparp
공식 코드https://github.com/ukplab/eacl2023-xlingvqa
공식 코드https://github.com/umd-huang-lab/sima
공식 코드Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
https://github.com/uni-medical/GMAI-MMBench
공식 코드GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI
https://github.com/uni-medical/gmai-vl
공식 코드https://github.com/uni-medical/gmai-vl-r1
공식 코드GMAI-VL-R1: Harnessing Reinforcement Learning for Multimodal Medical Reasoning
https://github.com/upper9527/gerea
공식 코드GeReA: Question-Aware Prompt Captions for Knowledge-based Visual Question Answering
https://github.com/ustc-hyin/ClearSight
공식 코드https://github.com/usydnlp/REXUP
공식 코드https://github.com/uta-smile/TCL
공식 코드Vision-Language Pre-Training with Triple Contrastive Learning
https://github.com/vacancy/NSCL-PyTorch-Release
공식 코드https://github.com/vaidehi99/unlok-vqa
공식 코드Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation
https://github.com/vaishwarya96/GroVE
공식 코드https://github.com/val-iisc/sketchguess
공식 코드Game of Sketches: Deep Recurrent Models of Pictionary-style Word Guessing
https://github.com/vanessadamario/understanding_reasoning
공식 코드How Modular Should Neural Module Networks Be for Systematic Generalization?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.