GenGA: Editable and Data-Grounded Graphical Abstract Generation for Academic Papers

arXiv:2608.05478 · cs.GR, cs.CL, cs.CV, cs.HC, cs.LG, cs.MM · Submitted 2026-08-05 · Read on arXiv

Takuro Kawada, Shunsuke Kitada, Hitoshi Iyatomi

cs.GR, cs.CL, cs.CV, cs.HC, cs.LG, cs.MM

Submitted: 2026-08-05

Comments: 20 pages, 11 figures, 4 tables

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Papers — 31× in this paper · explained in 3 episodes
• Faithfulness — 9× in this paper · explained in 2 episodes
• Semantic Alignment — 8× in this paper · explained in 6 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Clarity — 6× in this paper · explained in 3 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Structural Independence Coefficient — 6× in this paper
• Composition — 4× in this paper · explained in 2 episodes
• Core — 4× in this paper · explained in 2 episodes
• Generator — 4× in this paper · explained in 2 episodes
• Hierarchical Structure — 4× in this paper · explained in 3 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Diffusion Model — 3× in this paper · explained in 10 episodes
• Diffusion Models — 3× in this paper · explained in 9 episodes
• Structural Consistency — 3× in this paper · explained in 2 episodes
• Ablation Study — 2× in this paper · explained in 3 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Iterative Refinement — 2× in this paper · explained in 5 episodes
• Latent Diffusion Model — 2× in this paper · explained in 3 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Pairwise Comparisons — 2× in this paper · explained in 2 episodes
• Structural Constraints — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Vector Representations — 2× in this paper · explained in 2 episodes
• Vision-language models — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 3 episodes
• Coherence — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Evaluation Protocol — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Vision-Language Model (VLM) — 1× in this paper · explained in 3 episodes

Sources

• DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editing
• Progressive Co-Attention Network for Fine-grained Visual Classification
• Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects
• A Competitive Method to VIPriors Object Detection Challenge
• SciFig: Towards Automating Editable Figure Generation for Scientific Papers
• The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
• OverleafCopilot: Empowering Academic Writing in Overleaf with Large Language Models
• PaperBanana: Automating Academic Illustration for AI Scientists
• Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation
• X$^3$KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection
• Test-Time Amendment with a Coarse Classifier for Fine-Grained Classification
• MixSiam: A Mixture-based Approach to Self-supervised Representation Learning

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