https://github.com/opengvlab/internvideo
공식 코드InternVideo: General Video Foundation Models via Generative and Discriminative Learning
Papers with Code 아카이브
Visual Question Answering (VQA)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 9
- 평가표
- 76
- 보존된 평가 기록
- 725
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/opengvlab/internvl
공식 코드InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
https://github.com/opengvlab/llama-adapter
공식 코드LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
https://github.com/opengvlab/multi-modality-arena
공식 코드LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models
https://github.com/opengvlab/multitask-model-selector
공식 코드Foundation Model is Efficient Multimodal Multitask Model Selector
https://github.com/opengvlab/piip
공식 코드Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding
https://github.com/opengvlab/unmasked_teacher
공식 코드Unmasked Teacher: Towards Training-Efficient Video Foundation Models
https://github.com/opengvlab/visionllm
공식 코드https://github.com/openrobotlab/pointllm
공식 코드PointLLM: Empowering Large Language Models to Understand Point Clouds
https://github.com/optimization-for-data-driven-science/dair
공식 코드https://github.com/orionbench/orionbench
공식 코드OrionBench: A Benchmark for Chart and Human-Recognizable Object Detection in Infographics
https://github.com/osilly/dynamic_llava
공식 코드https://github.com/osu-nlp-group/seeact
공식 코드https://github.com/osu-slatelab/mapqa
공식 코드https://github.com/ouyangkun10/spacer
공식 코드https://github.com/ouyangkun10/spatial-r1
공식 코드https://github.com/overloadedhenry/clinkd
공식 코드ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical Images
https://github.com/ovguyo/captions-in-vqa
공식 코드Enhancing Visual Question Answering through Question-Driven Image Captions as Prompts
https://github.com/ovshake/cobra
공식 코드https://github.com/ozyyshr/DGM
공식 코드Fact-driven Logical Reasoning for Machine Reading Comprehension
https://github.com/ozyyshr/focalreasoner
공식 코드Fact-driven Logical Reasoning for Machine Reading Comprehension
https://github.com/pairlab/slotformer
공식 코드SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models
https://github.com/palaashagrawal/stupd
공식 코드STUPD: A Synthetic Dataset for Spatial and Temporal Relation Reasoning
https://github.com/panasonicconnect/videomultiagents
공식 코드VideoMultiAgents: A Multi-Agent Framework for Video Question Answering
https://github.com/para-lost/rvp
공식 코드https://github.com/park-jungin/vlap
공식 코드Bridging Vision and Language Spaces with Assignment Prediction
https://github.com/patilli/vqa_benchmarking
공식 코드Beyond Accuracy: A Consolidated Tool for Visual Question Answering Benchmarking
https://github.com/patrickamadeus/vqa-nle-llava
공식 코드Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language Models
https://github.com/pauamargant/hsr-graspsynth
공식 코드https://github.com/paulgavrikov/vlm_shapebias
공식 코드https://github.com/paullerner/viquae
공식 코드Multimodal Inverse Cloze Task for Knowledge-based Visual Question Answering
https://github.com/paulsubarna/core-tokenset
공식 코드Core Tokensets for Data-efficient Sequential Training of Transformers
https://github.com/pavancm/GREED
공식 코드https://github.com/pds-dpo/pds-dpo
공식 코드Multimodal Preference Data Synthetic Alignment with Reward Model
https://github.com/pefimov/cross-lingual-adjustment
공식 코드The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot Transfer
https://github.com/peijin360/vsre
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.