Topic: visual-question-answering Goto Github
Some thing interesting about visual-question-answering
Some thing interesting about visual-question-answering
visual-question-answering,Creating multimodal multitask models
Organization: ai-forever
Home Page: https://dsworks.ru/champs/fb5778a8-94e9-46de-8bad-aa2c83a755fb
visual-question-answering,Compact Trilinear Interaction for Visual Question Answering (ICCV 2019)
Organization: aioz-ai
Home Page: https://blog.ai.aioz.io/research/vqa-cti/
visual-question-answering,AIOZ AI - Overcoming Data Limitation in Medical Visual Question Answering (MICCAI 2019)
Organization: aioz-ai
Home Page: https://blog.ai.aioz.io/research/vqa-mevf/
visual-question-answering,Official repository for the A-OKVQA dataset
Organization: allenai
visual-question-answering,Document Visual Question Answering
User: anisha2102
visual-question-answering,[NeurIPS 2022] Zero-Shot Video Question Answering via Frozen Bidirectional Language Models
User: antoyang
Home Page: https://arxiv.org/abs/2206.08155
visual-question-answering,[ICCV 2021 Oral + TPAMI] Just Ask: Learning to Answer Questions from Millions of Narrated Videos
User: antoyang
Home Page: https://arxiv.org/abs/2012.00451
visual-question-answering,Code for paper title "Learning Semantic Sentence Embeddings using Pair-wise Discriminator" COLING-2018
User: badripatro
visual-question-answering,PyTorch implementation of FiLM: Visual Reasoning with a General Conditioning Layer
User: caffeinism
visual-question-answering,[Paper][ISWC 2021] Zero-shot Visual Question Answering using Knowledge Graph
Organization: china-uk-zsl
Home Page: https://arxiv.org/abs/2107.05348
visual-question-answering,:eyes: :speaking_head: :memo:12-in-1: Multi-Task Vision and Language Representation Learning Web Demo
Organization: cloud-cv
Home Page: https://vilbert.cloudcv.org/
visual-question-answering,Strong baseline for visual question answering
User: cyanogenoid
visual-question-answering,PyTorch implementation of "Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning"
User: davidmascharka
Home Page: https://arxiv.org/abs/1803.05268
visual-question-answering,PyTorch VQA implementation that achieved top performances in the (ECCV18) VizWiz Grand Challenge: Answering Visual Questions from Blind People
User: denisdsh
visual-question-answering,PyTorch implementation of paper "Visual Concept-Metaconcept Learner", NeruIPS 2019
User: glaciohound
Home Page: http://vcml.csail.mit.edu
visual-question-answering,a collection of computer vision projects&tools. ่ฎก็ฎๆบ่ง่งๆนๅ้กน็ฎๅๅทฅๅ ท้ๅใ
User: hanxinzi-ai
visual-question-answering,Official code repository for "Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning" (published at ICLR 2023).
User: ivonajdenkoska
visual-question-answering,Code for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''
User: jialinwu17
visual-question-answering,Bilinear attention networks for visual question answering
User: jnhwkim
visual-question-answering,A Pytorch implementation of Attention on Attention module (both self and guided variants), for Visual Question Answering
User: lucidrains
visual-question-answering,Implementation of ๐ฆฉ Flamingo, state-of-the-art few-shot visual question answering attention net out of Deepmind, in Pytorch
User: lucidrains
visual-question-answering,Co-attending Regions and Detections for VQA.
User: lupantech
visual-question-answering,MathVista: data, code, and evaluation for Mathematical Reasoning in Visual Contexts
User: lupantech
Home Page: https://mathvista.github.io/
visual-question-answering,LLaVA inference with multiple images at once for cross-image analysis.
User: mapluisch
visual-question-answering,Pytorch implementation of winner from VQA Chllange Workshop in CVPR'17
User: markdtw
visual-question-answering,A pytorch implementation for "A simple neural network module for relational reasoning", working on the CLEVR dataset
User: mesnico
visual-question-answering,Deep Modular Co-Attention Networks for Visual Question Answering
Organization: milvlg
visual-question-answering,A lightweight, scalable, and general framework for visual question answering research
Organization: milvlg
visual-question-answering,Implementation of CVPR 2023 paper "Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering".
Organization: milvlg
Home Page: https://arxiv.org/abs/2303.01903
visual-question-answering,Large Language Models are Temporal and Causal Reasoners for Video Question Answering (EMNLP 2023)
Organization: mlvlab
Home Page: https://ikodoh.github.io/flipped_vqa_demo.html
visual-question-answering,This repo contains evaluation code for the paper "MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI"
Organization: mmmu-benchmark
Home Page: https://mmmu-benchmark.github.io/
visual-question-answering,This repo contains evaluation code for the paper "Are We on the Right Way for Evaluating Large Vision-Language Models"
Organization: mmstar-benchmark
Home Page: https://mmstar-benchmark.github.io
visual-question-answering,Official repository of OFA (ICML 2022). Paper: OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
Organization: ofa-sys
visual-question-answering,
User: paarthneekhara
visual-question-answering,Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome
User: peteanderson80
Home Page: http://panderson.me/up-down-attention/
visual-question-answering,Code to reproduce results in our ACL 2018 paper "Did the Model Understand the Question?"
User: pramodkaushik
visual-question-answering,[AAAI 2024] NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario.
User: qiantianwen
visual-question-answering,[ICCV 2021] Official implementation of the paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering"
User: rentainhe
visual-question-answering,A collection of resources on applications of multi-modal learning in medical imaging.
User: richard-peng-xia
visual-question-answering,PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Organization: salesforce
visual-question-answering,[ICML 2023] UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers.
User: sdc17
Home Page: https://dachuanshi.com/UPop-Project/
visual-question-answering,CNN+LSTM, Attention based, and MUTAN-based models for Visual Question Answering
User: shivanshu-gupta
visual-question-answering,Korean Visual Question Answering
Organization: sktbrain
Home Page: https://sktbrain.github.io/KVQA/
visual-question-answering,Bottom-up features extractor implemented in PyTorch.
User: violetteshev
visual-question-answering,Evaluation framework for paper "VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?"
Organization: visualwebbench
Home Page: https://visualwebbench.github.io/
visual-question-answering,TensorFlow implementation of the CNN-LSTM, Relation Network and text-only baselines for the paper "FigureQA: An Annotated Figure Dataset for Visual Reasoning"
User: vmichals
visual-question-answering,X-modaler is a versatile and high-performance codebase for cross-modal analytics(e.g., image captioning, video captioning, vision-language pre-training, visual question answering, visual commonsense reasoning, and cross-modal retrieval).
User: yehli
visual-question-answering,TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
User: yushi-hu
Home Page: https://tifa-benchmark.github.io/
visual-question-answering,Research Code for NeurIPS 2020 Spotlight paper "Large-Scale Adversarial Training for Vision-and-Language Representation Learning": UNITER adversarial training part
User: zhegan27
Home Page: https://arxiv.org/pdf/2006.06195.pdf
visual-question-answering,Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Organization: zjukg
Home Page: http://arxiv.org/abs/2402.05391
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