On the public citation graph
Inner Knowledge-based Img2Doc Scheme for Visual Question Answering
0 signed · 65 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-030-58589-1_23openalex-snapshot
VQA-LOL: Visual Question Answering Under the Lens of Logic
inferredcites →https://doi.org/10.1007/978-3-319-46478-7_28openalex-snapshot
Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering
inferredcites →https://doi.org/10.1007/978-3-319-46484-8_44openalex-snapshot
Revisiting Visual Question Answering Baselines
inferredcites →https://doi.org/10.1007/s11263-017-1038-2openalex-snapshot
Ask Your Neurons: A Deep Learning Approach to Visual Question Answering
inferredcites →https://doi.org/10.1007/s11263-020-01331-0openalex-snapshot
Weakly-supervised Semantic Guided Hashing for Social Image Retrieval
inferredcites →https://doi.org/10.1016/j.cviu.2017.05.001openalex-snapshot
Visual question answering: A survey of methods and datasets
inferredcites →https://doi.org/10.1016/j.cviu.2017.06.005openalex-snapshot
Visual question answering: Datasets, algorithms, and future challenges
inferredcites →https://doi.org/10.1016/j.patcog.2020.107538openalex-snapshot
DecomVQANet: Decomposing visual question answering deep network via tensor decomposition and regression
inferredcites →https://doi.org/10.1029/2018gl077787openalex-snapshot
CloudNet: Ground‐Based Cloud Classification With Deep Convolutional Neural Network
inferredcites →https://doi.org/10.1038/srep00113openalex-snapshot
An Activation Force-based Affinity Measure for Analyzing Complex Networks
inferredcites →https://doi.org/10.1109/cvpr.1993.341010openalex-snapshot
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/cvpr.2016.10openalex-snapshot
Stacked Attention Networks for Image Question Answering
inferredcites →https://doi.org/10.1109/cvpr.2016.11openalex-snapshot
Image Question Answering Using Convolutional Neural Network with Dynamic Parameter Prediction
inferredcites →https://doi.org/10.1109/cvpr.2016.29openalex-snapshot
What Value Do Explicit High Level Concepts Have in Vision to Language Problems?
inferredcites →https://doi.org/10.1109/cvpr.2016.494openalex-snapshot
DenseCap: Fully Convolutional Localization Networks for Dense Captioning
inferredcites →https://doi.org/10.1109/cvpr.2016.500openalex-snapshot
Ask Me Anything: Free-Form Visual Question Answering Based on Knowledge from External Sources
inferredcites →https://doi.org/10.1109/cvpr.2016.538openalex-snapshot
Answer-Type Prediction for Visual Question Answering
inferredcites →https://doi.org/10.1109/cvpr.2016.540openalex-snapshot
Visual7W: Grounded Question Answering in Images
inferredcites →https://doi.org/10.1109/cvpr.2017.670openalex-snapshot
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
inferredcites →https://doi.org/10.1109/cvpr.2018.00522openalex-snapshot
Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering
inferredcites →https://doi.org/10.1109/cvpr.2019.00644openalex-snapshot
Deep Modular Co-Attention Networks for Visual Question Answering
inferredcites →https://doi.org/10.1109/cvpr.2019.00851openalex-snapshot
Towards VQA Models That Can Read
inferredcites →https://doi.org/10.1109/cvpr.2019.01072openalex-snapshot
Answer Them All! Toward Universal Visual Question Answering Models
inferredcites →https://doi.org/10.1109/cvpr31182.2015openalex-snapshot
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00971openalex-snapshot
Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing
inferredcites →https://doi.org/10.1109/iccv.2017.202openalex-snapshot
Multi-modal Factorized Bilinear Pooling with Co-attention Learning for Visual Question Answering
inferredcites →https://doi.org/10.1109/iccv.2019.00439openalex-snapshot
Scene Text Visual Question Answering
inferredcites →https://doi.org/10.1109/tnnls.2018.2817340openalex-snapshot
Beyond Bilinear: Generalized Multimodal Factorized High-Order Pooling for Visual Question Answering
inferredcites →https://doi.org/10.1109/tpami.2016.2642953openalex-snapshot
Aligning Where to See and What to Tell: Image Captioning with Region-Based Attention and Scene-Specific Contexts
inferredcites →https://doi.org/10.1109/tpami.2017.2708709openalex-snapshot
Image Captioning and Visual Question Answering Based on Attributes and External Knowledge
inferredcites →https://doi.org/10.1109/tpami.2017.2723400openalex-snapshot
Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition
inferredcites →https://doi.org/10.1109/tpami.2017.2754246openalex-snapshot
FVQA: Fact-Based Visual Question Answering
inferredcites →https://doi.org/10.1109/tpami.2018.2852750openalex-snapshot
Deep Collaborative Embedding for Social Image Understanding
inferredcites →https://doi.org/10.1145/3300938openalex-snapshot
Semantic Concept Network and Deep Walk-based Visual Question Answering
inferredcites →https://doi.org/10.1145/3338533openalex-snapshot
Proceedings of the ACM Multimedia Asia
inferredcites →https://doi.org/10.1609/aaai.v33i01.33018076openalex-snapshot
TallyQA: Answering Complex Counting Questions
inferredcites →https://doi.org/10.1609/aaai.v34i07.7005openalex-snapshot
Unified Vision-Language Pre-Training for Image Captioning and VQA
inferredcites →https://doi.org/10.1609/aimag.v31i3.2303openalex-snapshot
Building Watson: An Overview of the DeepQA Project
inferredcites →https://doi.org/10.18653/v1/d16-1044openalex-snapshot
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
inferredcites →https://doi.org/10.18653/v1/d18-1164openalex-snapshot
Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions
inferredcites →https://doi.org/10.18653/v1/d19-1514openalex-snapshot
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
inferredcites →https://doi.org/10.2307/2709688openalex-snapshot
Newton's Third Law and Universal Gravity
inferredcites →https://doi.org/10.48550/arxiv.cmp-lg/9406033openalex-snapshot
Verb Semantics and Lexical Selection
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-032-06253-6_24openalex-snapshot
Hybrid CNN-LSTM with Attention Mechanism for Medical Visual Question Answering
inferred← citeshttps://doi.org/10.1007/s00530-023-01240-5openalex-snapshot
Video question answering via traffic knowledge database and question classification
inferred← citeshttps://doi.org/10.1007/s11063-023-11403-0openalex-snapshot
Multimodal Bi-direction Guided Attention Networks for Visual Question Answering
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.105923openalex-snapshot
Cross-modal information balance-aware reasoning network for image-text retrieval
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.123092openalex-snapshot
DSAMR: Dual-Stream Attention Multi-hop Reasoning for knowledge-based visual question answering
inferred← citeshttps://doi.org/10.1016/j.knosys.2023.110879openalex-snapshot
Question-conditioned debiasing with focal visual context fusion for visual question answering
inferred← citeshttps://doi.org/10.1109/icassp49660.2025.10890839openalex-snapshot
KMG-LL: Knowledge-enhanced Multimodal Graph for Dialogue Generation
inferred← citeshttps://doi.org/10.1109/sitis57111.2022.00048openalex-snapshot
Multimodal Knowledge Reasoning for Enhanced Visual Question Answering
inferred← citeshttps://doi.org/10.1109/tpami.2024.3402143openalex-snapshot
RepSGG: Novel Representations of Entities and Relationships for Scene Graph Generation
inferred← citeshttps://doi.org/10.1145/3585010openalex-snapshot
Visual Paraphrase Generation with Key Information Retained
inferred← citeshttps://doi.org/10.1145/3596947.3596951openalex-snapshot
Dynamic Self-Attention with Guided-Attention Network for Visual Question Answering using Capsules Dynamic Routing
inferred← citeshttps://doi.org/10.1145/3604284openalex-snapshot
Zero-shot Scene Graph Generation via Triplet Calibration and Reduction
inferred← citeshttps://doi.org/10.1145/3604557openalex-snapshot
Language-guided Residual Graph Attention Network and Data Augmentation for Visual Grounding
inferred← citeshttps://doi.org/10.1145/3660346openalex-snapshot
Multi-Grained Representation Aggregating Transformer with Gating Cycle for Change Captioning
inferred← citeshttps://doi.org/10.1145/3711680openalex-snapshot
Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey
inferred← citeshttps://doi.org/10.1145/3715141openalex-snapshot
Language-guided Bias Generation Contrastive Strategy for Visual Question Answering
inferred← citeshttps://doi.org/10.1145/3734220openalex-snapshot
SCAG: Semantic Co-occurring Attention Guided Alignment for Knowledge-based Visual Question Answering
inferred← citeshttps://doi.org/10.1145/3833389openalex-snapshot
Intelligent Prompt Construction for Large Language Models in Knowledge-based Visual Question Answering
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.