Visual Question Answering

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

29 leaderboard tables shown for this task, 29 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 29 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

32 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 32 until expanded.

Subtasks archive 2025-07-28

5 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 1,042 papers with code (2,177 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
  7 Oct 2016 126 repositories listed Syntology ran 79 of 141 samples · 62 unverified · 68 pointer-only (licence)
  For captioning and VQA, we show that even non-attention based models can localize inputs.
• Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
  25 Jul 2017 65 repositories listed Syntology ran 9 of 9 samples · 0 unverified · 6 pointer-only (licence)
  Top-down visual attention mechanisms have been used extensively in image captioning and visual question answering (VQA) to enable deeper image understanding through fine-grained analysis and even multiple steps of…
• VQA: Visual Question Answering
  3 May 2015 21 repositories listed Syntology ran 6 of 7 samples · 1 unverified · 6 pointer-only (licence)
  Given an image and a natural language question about the image, the task is to provide an accurate natural language answer.
• A simple neural network module for relational reasoning
  5 Jun 2017 20 repositories listed Syntology ran 3 of 7 samples · 4 unverified · 1 pointer-only (licence)
  Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn.
• BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
  30 Jan 2023 17 repositories listed Syntology ran 4 of 8 samples · 4 unverified · 1 pointer-only (licence)
  The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models.
• Visual Instruction Tuning
  17 Apr 2023 13 repositories listed Syntology ran 16 of 51 samples · 35 unverified
  Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field.
• Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering
  11 Apr 2017 13 repositories listed Syntology ran 9 of 9 samples · 0 unverified · 9 pointer-only (licence)
  This paper presents a new baseline for visual question answering task.
• GPT-4 Technical Report
  15 Mar 2023 11 repositories listed Syntology ran 2 of 5 samples · 3 unverified · 1 pointer-only (licence)
  We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
• ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
  6 Aug 2019 11 repositories listed Syntology ran 10 of 34 samples · 24 unverified · 34 pointer-only (licence)
  We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language.
• Hadamard Product for Low-rank Bilinear Pooling
  14 Oct 2016 11 repositories listed
  Bilinear models provide rich representations compared with linear models.
• Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
  9 Aug 2017 10 repositories listed
  This paper presents a state-of-the-art model for visual question answering (VQA), which won the first place in the 2017 VQA Challenge.
• Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
  6 Jun 2016 10 repositories listed
  Approaches to multimodal pooling include element-wise product or sum, as well as concatenation of the visual and textual representations.
• Dynamic Memory Networks for Visual and Textual Question Answering
  4 Mar 2016 10 repositories listed Syntology ran 7 of 7 samples · 0 unverified · 7 pointer-only (licence)
  Neural network architectures with memory and attention mechanisms exhibit certain reasoning capabilities required for question answering.
• Improved Baselines with Visual Instruction Tuning
  5 Oct 2023 9 repositories listed Syntology ran 6 of 9 samples · 3 unverified · 8 pointer-only (licence)
  Large multimodal models (LMM) have recently shown encouraging progress with visual instruction tuning.
• LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
  29 Dec 2020 9 repositories listed
  Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born…
• LXMERT: Learning Cross-Modality Encoder Representations from Transformers
  20 Aug 2019 9 repositories listed Syntology ran 4 of 15 samples · 11 unverified · 3 pointer-only (licence)
  In LXMERT, we build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language encoder, and a cross-modality encoder.
• Hierarchical Question-Image Co-Attention for Visual Question Answering
  31 May 2016 9 repositories listed Syntology ran 1 of 7 samples · 6 unverified · 1 pointer-only (licence)
  In addition, our model reasons about the question (and consequently the image via the co-attention mechanism) in a hierarchical fashion via a novel 1-dimensional convolution neural networks (CNN).
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
  18 Sep 2024 8 repositories listed Syntology ran 8 of 12 samples · 4 unverified
  We present the Qwen2-VL Series, an advanced upgrade of the previous Qwen-VL models that redefines the conventional predetermined-resolution approach in visual processing.
• Bilinear Attention Networks
  21 May 2018 8 repositories listed Syntology ran 4 of 13 samples · 9 unverified
  In this paper, we propose bilinear attention networks (BAN) that find bilinear attention distributions to utilize given vision-language information seamlessly.
• Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments
  20 Nov 2017 8 repositories listed
  This is significant because a robot interpreting a natural-language navigation instruction on the basis of what it sees is carrying out a vision and language process that is similar to Visual Question Answering.
• FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
  5 Dec 2020 7 repositories listed
  This dataset demonstrates the post flooded damages of the affected areas.
• UNITER: UNiversal Image-TExt Representation Learning
  25 Sep 2019 7 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 2 pointer-only (licence)
  Different from previous work that applies joint random masking to both modalities, we use conditional masking on pre-training tasks (i.
• Deep Modular Co-Attention Networks for Visual Question Answering
  25 Jun 2019 7 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  In this paper, we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA) layers cascaded in depth.
• Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
  20 Mar 2017 7 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 1 pointer-only (licence)
  Specifically, we pose a cooperative 'image guessing' game between two agents -- Qbot and Abot -- who communicate in natural language dialog so that Qbot can select an unseen image from a lineup of images.
• Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
  2 Dec 2016 7 repositories listed
  We propose to counter these language priors for the task of Visual Question Answering (VQA) and make vision (the V in VQA) matter!
• Simple Baseline for Visual Question Answering
  7 Dec 2015 7 repositories listed
  We describe a very simple bag-of-words baseline for visual question answering.
• Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
  8 Feb 2024 6 repositories listed Syntology ran 9 of 18 samples · 9 unverified
  In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph…
• Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
  16 Nov 2023 6 repositories listed Syntology ran 4 of 7 samples · 3 unverified · 1 pointer-only (licence)
  In this work, we unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM.
• MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
  20 Apr 2023 6 repositories listed
  Our work, for the first time, uncovers that properly aligning the visual features with an advanced large language model can possess numerous advanced multi-modal abilities demonstrated by GPT-4, such as detailed image…
• CoCa: Contrastive Captioners are Image-Text Foundation Models
  4 May 2022 6 repositories listed Syntology ran 9 of 17 samples · 8 unverified
  We apply a contrastive loss between unimodal image and text embeddings, in addition to a captioning loss on the multimodal decoder outputs which predicts text tokens autoregressively.

Syntology lines on 21 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.