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Found 9 papers, 3 papers with code
Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis
In this work, we improve the compositional skills of T2I models, specifically more accurate attribute binding and better image compositions.
LayoutGPT: Compositional Visual Planning and Generation with Large Language Models
When combined with a downstream image generation model, LayoutGPT outperforms text-to-image models/systems by 20-40% and achieves comparable performance as human users in designing visual layouts for numerical and spatial correctness.
Discriminative Diffusion Models as Few-shot Vision and Language Learners
Diffusion models, such as Stable Diffusion, have shown incredible performance on text-to-image generation.
CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization
One challenge in evaluating visual question answering (VQA) models in the cross-dataset adaptation setting is that the distribution shifts are multi-modal, making it difficult to identify if it is the shifts in visual or language features that play a key role.
Mind the Context: The Impact of Contextualization in Neural Module Networks for Grounding Visual Referring Expressions
Our model outperforms the state-of-the-art NMN model on CLEVR-Ref+ dataset with +8. 1% improvement in accuracy on the single-referent test set and +4. 3% on the full test set.
Robust Visual Reasoning via Language Guided Neural Module Networks
Neural module networks (NMN) are a popular approach for solving multi-modal tasks such as visual question answering (VQA) and visual referring expression recognition (REF).
CPL: Counterfactual Prompt Learning for Vision and Language Models
Prompt tuning is a new few-shot transfer learning technique that only tunes the learnable prompt for pre-trained vision and language models such as CLIP.
