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/tianaidong/2021splu-robonlp-vispa
GuessWhat?! Visual object discovery through multi-modal dialogue
https://github.com/tianhai123/tf-faster-rcnn
https://github.com/tidedra/vl-rlhf
https://github.com/tigerofmurder/tf-faster-rcnn
https://github.com/timothyhtimothy/fast-vqa
Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment
https://github.com/timothyhtimothy/fast-vqa-and-fastervqa
Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment
https://github.com/tinyllava/tinyllava_factory
TinyLLaVA: A Framework of Small-scale Large Multimodal Models
https://github.com/tlacombe/topt
PLLay: Efficient Topological Layer based on Persistence Landscapes
https://github.com/tohinz/pytorch-mac-network
https://github.com/tomerwolgithub/Break
https://github.com/tongjingqi/code2logic
https://github.com/towhee-io/towhee
https://github.com/triarts/ECG-classification
ECG Heartbeat Classification: A Deep Transferable Representation
https://github.com/triarts/ECG-classification-OLD_VERISON
ECG Heartbeat Classification: A Deep Transferable Representation
https://github.com/trisha-c-a/Visual-Question-Answering-with-Keras
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
https://github.com/tsurumeso/chainer-grad-cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/ttok0s7u2n5/ML2_proj
https://github.com/tu184044109/BVQA_Benchmark
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
https://github.com/turningpoint-ai/visualthinker-r1-zero
SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models
https://github.com/uakarsh/TiLT-Implementation
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer
https://github.com/uakarsh/docformerv2
https://github.com/uakarsh/latr
https://github.com/uclanlp/visualbert
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/uds-lsv/pypremise
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors
https://github.com/ukyh/switch_disc_caption
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/umass-foundation-model/3d-llm
https://github.com/umass-foundation-model/genome
GENOME: GenerativE Neuro-symbOlic visual reasoning by growing and reusing ModulEs
https://github.com/unispac/visual-adversarial-examples-jailbreak-large-language-models
https://github.com/valdecy/pybibx
https://github.com/vargpt-family/vargpt-v1.1
https://github.com/vbbphc/GradCAM.jl
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/vchudinov/dynamic_memory_networks_with_keras
Dynamic Memory Networks for Visual and Textual Question Answering
https://github.com/vietlinhtspt/NewFasterRCNN
https://github.com/vijayakuruba/IFT6010_Applying_Answer-Clue-Style_Approach_to_VQG
Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
https://github.com/vikrantmane7781/detectroon2
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.