文件名称：vqa-paper.zip
文件大小：163.55MB
文件格式：ZIP
更新时间：2024-05-07 11:17:11
VQA
主要是看过的20年之前的相关论文整理
【文件预览】：
vqa-paper
----MUCKO.PDF(2.91MB)
----Say As You Wish-Fine-grained-Control-of-Image-Caption-Generation-with-Abstract-Scene-Graphs.pdf(227KB)
----ViLBERT.pptx(965KB)
----Wu_Ask_Me_Anything_CVPR_2016_paper.pdf(1.34MB)
----Behind the Scene-Revealing the Secrets of.pdf(1.63MB)
----7531-out-of-the-box-reasoning-with-graph-convolution-nets-for-factual-visual-question-answering.pdf(2.45MB)
----conceptbert.pdf(2.7MB)
----Co-Attending Free-Form Regions and Detections with Multi-Modal Multiplicative Feature Embedding for Visual Question Answering.pdf(2.35MB)
----From Recognition to Cognition Visual Commonsense Reasoning.pptx(961KB)
----Singh_Towards_VQA_Models_That_Can_Read_CVPR_2019_paper.pdf(3.22MB)
----Enforcing Reasoning in Visual Commonsense Reasoning - 副本.pdf(6.9MB)
----KnowIT VQA- Answering Knowledge-Based Questions about Videos - 副本.pdf(1.4MB)
----Bottom-Up_and_Top-Down_CVPR_2018_paper.pdf(727KB)
----CQ-VQA-Visual Question Answering on Categorized-arxiv - 副本.pdf(457KB)
----7429-bilinear-attention-networks - 副本.pdf(1.63MB)
----A Comprehensive Survey on Graph Neural.PDF(1.6MB)
----Marino_OK-VQA_A_Visual_Question_Answering_Benchmark_Requiring_External_Knowledge_CVPR_2019_paper.pdf(2.69MB)
----8054-learning-conditioned-graph-structures-for-interpretable-visual-question-answering.pdf(3.86MB)
----Heterogeneous Graph Learning for Visual.pptx(1015KB)
----Generating Question Relevant Captions to Aid Visual Question Answering - 副本.pdf(5.29MB)
----Heterogeneous Graph Attention Network.pdf(2.76MB)
----Xiong_TA-Student_VQA_Multi-Agents_Training_by_Self-Questioning_CVPR_2020_paper.pdf(3.69MB)
----heterogeneous-graph-learning-for-visual-commonsense-reasoning.pdf(1.6MB)
----Zhu_ActBERT_Learning_Global-Local_Video-Text_Representations_CVPR_2020_paper.pdf(1.1MB)
----Shah_Cycle-Consistency_for_Robust_Visual_Question_Answering_CVPR_2019_paper.pdf(1.54MB)
----Li_Visual_Question_Answering_as_Reading_Comprehension_CVPR_2019_paper - 副本.pdf(667KB)
----VQA_Visual_Question_ICCV_2015_paper.pdf(1.78MB)
----2019-Challenges and Prospects in Vision and - 副本.pdf(3.74MB)
----Divide and Conquer-Question-Guided Spatio-Temporal Contextual Attention for.pdf(2.79MB)
----straight to fact.pdf(2.77MB)
----Reasoning on the Relation-Enhancing Visual Representation for Visual Question Answering and Cross-modal Retrieval.pdf(16.59MB)
----Are we pretraining it right.pdf(956KB)
----Dynamic Capsule Attention for Visual Question Answering.pdf(766KB)
----Tips_and_Tricks_CVPR_2018_paper (1).pdf(664KB)
----Biten_Scene_Text_Visual_Question_Answering_ICCV_2019_paper.pdf(1.92MB)
----Fusion of Detected Objects in Text for Visual Question Answering - 副本.pdf(3.95MB)
----Li_Visual_Question_Answering_as_Reading_Comprehension_CVPR_2019_paper.pdf(667KB)
----Hu_Iterative_Answer_Prediction_With_Pointer-Augmented_Multimodal_Transformers_for_TextVQA_CVPR_2020_paper.pdf(1.67MB)
----SQU.pdf(3.94MB)
----Do_Compact_Trilinear_Interaction_for_Visual_Question_Answering_ICCV_2019_paper.pdf(719KB)
----Singh_From_Strings_to_Things_Knowledge-Enabled_VQA_Model_That_Can_Read_ICCV_2019_paper.pdf(3.35MB)
----Feng_Self-Supervised_Representation_Learning_From_Multi-Domain_Data_ICCV_2019_paper.pdf(817KB)
----multi-encoder-decoder-atention-network-IEEEacess.pdf(5.13MB)
----CQ-VQA.pdf(457KB)
----Zellers_From_Recognition_to_Cognition_Visual_Commonsense_Reasoning_CVPR_2019_paper - 副本.pdf(2.13MB)
----Bhattacharya_Why_Does_a_Visual_Question_Have_Different_Answers_ICCV_2019_paper.pdf(1.1MB)
----Compact Trilinear Interaction for Visual Question Answering.pptx(906KB)
----pre.pdf(2.75MB)
----Antol_VQA_Visual_Question_ICCV_2015_paper (1) - 副本.pdf(1.61MB)
----FVQA Fact-based Visual Question Answering.pptx(1.36MB)
----Unified Vision-Language Pre-Training for Image Captioning and VQA.pdf(657KB)
----From Two Graphs to N Questions - 副本.pdf(2.2MB)
----Gao_Multi-Modality_Latent_Interaction_Network_for_Visual_Question_Answering_ICCV_2019_paper - 副本.pdf(987KB)
----2017-Visual Question Answering算法未来挑战.pdf(9.18MB)
----Cross-ModalityRelevanceforReasoningonLanguageandVision - 副本.pdf(1.35MB)
----Image Captioning and Visual Question.pdf(3.55MB)
----KBQA-2019.PDF(646KB)
----Visual Question Answering综述.pdf(9.18MB)
----SQuINTing at VQA Models.pdf(3.94MB)
----Knowledge-Based Visual Question Answering in Videos.pdf(528KB)
----ask me anything.pdf(1.25MB)
----Diverse Visuo-Lingustic Question Answering (DVLQA) Challenge.pdf(1.13MB)
----Kvqa Knowledge-aware visual question answering - 副本.pdf(856KB)
----Noh_Transfer_Learning_via_Unsupervised_Task_Discovery_for_Visual_Question_Answering_CVPR_2019_paper.pdf(1.24MB)
----KnowIT VQA- Answering Knowledge-Based Questions about Videos.pdf(1.4MB)
----Improving Visual Question Answering by Referring to - 副本.pdf(2.04MB)
----Re-Attention for Visual Question Answering - 副本.pdf(1.17MB)
----12-in-1--Multi-task vision.pdf(7.13MB)
----DVLQA.pptx(1.16MB)
----（2）-Deep Modular Co-Attention Networks for Visual Question Answering.pdf(7.91MB)
----KVQA-AAAI2019.pdf(3.99MB)