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Visual Reasoning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Ability to understand actions and reasoning associated with any visual images
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ahmad-573/vurf
공식 코드VURF: A General-purpose Reasoning and Self-refinement Framework for Video Understanding
https://github.com/ahmed-abdulhuy/solving-tsp-and-mtsp-combinatorial-challenges-using-visual-reasoning-and-multi-agent-approach-mllms-
공식 코드https://github.com/ahmedssabir/belief-revision-score
공식 코드Belief Revision based Caption Re-ranker with Visual Semantic Information
https://github.com/aibee00/socraticquestioning
공식 코드Socratic Questioning: Learn to Self-guide Multimodal Reasoning in the Wild
https://github.com/aifeg/benchgpt
공식 코드BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models
https://github.com/aifeg/benchlmm
공식 코드BenchLMM: Benchmarking Cross-style Visual Capability of Large Multimodal Models
https://github.com/aim-uofa/SegAgent
공식 코드https://github.com/aiming-lab/eduvisagent
공식 코드From EduVisBench to EduVisAgent: A Benchmark and Multi-Agent Framework for Pedagogical Visualization
https://github.com/aiming-lab/eduvisbench
공식 코드From EduVisBench to EduVisAgent: A Benchmark and Multi-Agent Framework for Pedagogical Visualization
https://github.com/aimzer/cvr
공식 코드https://github.com/aipenguin/stopreasoning
공식 코드Stop Reasoning! When Multimodal LLM with Chain-of-Thought Reasoning Meets Adversarial Image
https://github.com/airsplay/lxmert
공식 코드LXMERT: Learning Cross-Modality Encoder Representations from Transformers
https://github.com/alexatartaglini/relational-circuits
공식 코드Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects
https://github.com/alirezazareian/vspnet
공식 코드https://github.com/allenai/visual-reasoning-rationalization
공식 코드https://github.com/alpha-innovator/omnicaptioner
공식 코드https://github.com/amazon-research/mix-generation
공식 코드https://github.com/amazon-science/vigor
공식 코드ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward Modeling
https://github.com/anish-lu-yihe/pySVRT
공식 코드https://github.com/apple/ml-rpm-bench
공식 코드https://github.com/applied-ai-research-lab/orchestrator-agent-trust
공식 코드https://github.com/arashsajjadi/TGraphX
공식 코드TGraphX: Tensor-Aware Graph Neural Network for Multi-Dimensional Feature Learning
https://github.com/arron2003/rgbd2full3d
공식 코드https://github.com/artemisp/lavis-xinstructblip
공식 코드https://github.com/asgsaeid/cad_vqa
공식 코드How to Determine the Preferred Image Distribution of a Black-Box Vision-Language Model?
https://github.com/attentionx/instructblip_peft
공식 코드Towards Efficient Visual-Language Alignment of the Q-Former for Visual Reasoning Tasks
https://github.com/axe--/viphy
공식 코드https://github.com/ayesha-ishaq/drivelmm-o1
공식 코드https://github.com/bethgelab/notorious_difficulty_of_comparing_human_and_machine_perception
공식 코드Five Points to Check when Comparing Visual Perception in Humans and Machines
https://github.com/biluyong/cityeqa
공식 코드CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space
https://github.com/bradyfu/awesome-multimodal-large-language-models
공식 코드https://github.com/cambridgeltl/visual-spatial-reasoning
공식 코드https://github.com/celinehocquette/ijcai24-stevie
공식 코드Learning logic programs by discovering higher-order abstractions
https://github.com/ceyzaguirre4/DACT-MAC
공식 코드Differentiable Adaptive Computation Time for Visual Reasoning
https://github.com/chancharikmitra/ccot
공식 코드Compositional Chain-of-Thought Prompting for Large Multimodal Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.