https://github.com/yangdsh/VQA-BUTD-demo
Bottom-Up and Top-Down Attention for Image Captioning and 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/yangyucheng000/Paper-4/tree/main/mm_MindSpore
MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning
https://github.com/yangyucheng000/University/tree/main/model-1/pix2struct
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
https://github.com/yangyucheng000/University/tree/main/model-2/blip_2
https://github.com/yangyucheng000/University/tree/main/model-2/cogvlm
CogVLM2: Visual Language Models for Image and Video Understanding
https://github.com/yangyucheng000/University/tree/main/model-2/internlm
https://github.com/yangyucheng000/University/tree/main/model-3/qwen2_vl
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/yangyucheng000/papercode-2/tree/main/LRSL-main
https://github.com/yangyucheng000/papercode-2/tree/main/mm_MindSpore-main
MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning
https://github.com/yanxinyan1/yxy
https://github.com/yaolinli/capenrich
VinVL: Revisiting Visual Representations in Vision-Language Models
https://github.com/yifanh1/gqa_pytorch
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/yikang-li/iqan
https://github.com/yingsen1/unimd
InternVideo: General Video Foundation Models via Generative and Discriminative Learning
https://github.com/yiskw713/ClassActivationMapping
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/yiskw713/RISE
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/yiskw713/SmoothGradCAMplusplus
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/yiskw713/scorecam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/ylingfeng/FGVP
ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension
https://github.com/yly-revive/chainer-mreader
Reinforced Mnemonic Reader for Machine Reading Comprehension
https://github.com/yonatanbitton/automatic_generation_of_contrast_sets_from_scene_graphs
https://github.com/yongbowin/MSMARCO-Question-Answering_annotation
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/yongbowin/OpenKP_annotation
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/yongbowin/san_mrc_annotation
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/yooli23/ParlAI
https://github.com/yukw777/videoblip
https://github.com/yulong-XJTU/SimVLM
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
https://github.com/yzgrfsy/tf-fastrcnn-crop
https://github.com/zach-zhiling-zheng/reticular_chemist
https://github.com/zacharie12/zacharie
Learning to Compose Dynamic Tree Structures for Visual Contexts
https://github.com/zcfinal/loveu-cvpr23-aqtc
AssistQ: Affordance-centric Question-driven Task Completion for Egocentric Assistant
https://github.com/zcyang/imageqa-san
https://github.com/zebangcheng/emotion-llama
MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning
https://github.com/zhangwenyao1/open6dor_v2_execution
SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation
https://github.com/zhaochen0110/openthinkimg
Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models
https://github.com/zhaoolee/garss
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.