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Found 16 papers, 8 papers with code
KAFA: Rethinking Image Ad Understanding with Knowledge-Augmented Feature Adaptation of Vision-Language Models
Image ad understanding is a crucial task with wide real-world applications.
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.
MetaCLUE: Towards Comprehensive Visual Metaphors Research
Towards this goal, we introduce MetaCLUE, a set of vision tasks on visual metaphor.
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.
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.
Diagnosing Vision-and-Language Navigation: What Really Matters
Results show that indoor navigation agents refer to both object and direction tokens when making decisions.
A Framework for Deep Constrained Clustering
A fundamental strength of deep learning is its flexibility, and here we explore a deep learning framework for constrained clustering and in particular explore how it can extend the field of constrained clustering.
Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations
A major challenge in visually grounded language generation is to build robust benchmark datasets and models that can generalize well in real-world settings.
Leveraging Organizational Resources to Adapt Models to New Data Modalities
As applications in large organizations evolve, the machine learning (ML) models that power them must adapt the same predictive tasks to newly arising data modalities (e. g., a new video content launch in a social media application requires existing text or image models to extend to video).
Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation
Outdoor vision-and-language navigation (VLN) is such a task where an agent follows natural language instructions and navigates a real-life urban environment.
Ranked #4 on
Vision and Language Navigation
on Touchdown Dataset
(using extra training data)
HUSE: Hierarchical Universal Semantic Embeddings
The works in the domain of visual semantic embeddings address this problem by first constructing a semantic embedding space based on some external knowledge and projecting image embeddings onto this fixed semantic embedding space.
A Framework for Deep Constrained Clustering -- Algorithms and Advances
The area of constrained clustering has been extensively explored by researchers and used by practitioners.
Interpretable Neural Architectures for Attributing an Ad's Performance to its Writing Style
How much does {``}free shipping!
Micro-Browsing Models for Search Snippets
CTR of a result has two core components: (a) the probability of examination of a result by a user, and (b) the perceived relevance of the result given that it has been examined by the user.
Graphical RNN Models
Many time series are generated by a set of entities that interact with one another over time.
