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Found 6 papers, 6 papers with code
Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models
Recent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt.
Optimizing Relevance Maps of Vision Transformers Improves Robustness
It has been observed that visual classification models often rely mostly on the image background, neglecting the foreground, which hurts their robustness to distribution changes.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
No Token Left Behind: Explainability-Aided Image Classification and Generation
To mitigate it, we present a novel explainability-based approach, which adds a loss term to ensure that CLIP focuses on all relevant semantic parts of the input, in addition to employing the CLIP similarity loss used in previous works.
Image-Based CLIP-Guided Essence Transfer
We make the distinction between (i) style transfer, in which a source image is manipulated to match the textures and colors of a target image, and (ii) essence transfer, in which one edits the source image to include high-level semantic attributes from the target.
Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers
Transformers are increasingly dominating multi-modal reasoning tasks, such as visual question answering, achieving state-of-the-art results thanks to their ability to contextualize information using the self-attention and co-attention mechanisms.
Transformer Interpretability Beyond Attention Visualization
Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks.
