Search Results for author:
Found 15 papers, 2 papers with code
EraseBench: Understanding The Ripple Effects of Concept Erasure Techniques
This highlights the gap in reliability of the concept erasure techniques.
Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation
Text-to-image (T2I) generation has made significant advances in recent years, but challenges still remain in the generation of perceptual artifacts, misalignment with complex prompts, and safety.
Offline Evaluation of Set-Based Text-to-Image Generation
Our proposed offline evaluation metrics for TTI not only capture how relevant generated images are with respect to the user's ideation need but also take into consideration the diversity and arrangement of the set of generated images.
Cropper: Vision-Language Model for Image Cropping through In-Context Learning
The goal of image cropping is to identify visually appealing crops within an image.
Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation
Human feedback plays a critical role in learning and refining reward models for text-to-image generation, but the optimal form the feedback should take for learning an accurate reward function has not been conclusively established.
Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation
We use the novel multi-reward optimization algorithm to jointly optimize the T2I model and a prompt expansion network, resulting in significant improvement of image quality and also allow to control the trade-off of different rewards using a reward related prompt during inference.
Rich Human Feedback for Text-to-Image Generation
We show that the predicted rich human feedback can be leveraged to improve image generation, for example, by selecting high-quality training data to finetune and improve the generative models, or by creating masks with predicted heatmaps to inpaint the problematic regions.
UniAR: A Unified model for predicting human Attention and Responses on visual content
Progress in human behavior modeling involves understanding both implicit, early-stage perceptual behavior, such as human attention, and explicit, later-stage behavior, such as subjective preferences or likes.
Learning From Unique Perspectives: User-Aware Saliency Modeling
Our work aims to advance attention research from three distinct perspectives: (1) We present a new model with the flexibility to capture attention patterns of various combinations of users, so that we can adaptively predict personalized attention, user group attention, and general saliency at the same time with one single model; (2) To augment models with knowledge about the composition of attention from different users, we further propose a principled learning method to understand visual attention in a progressive manner; and (3) We carry out extensive analyses on publicly available saliency datasets to shed light on the roles of visual preferences.
Teacher-Generated Spatial-Attention Labels Boost Robustness and Accuracy of Contrastive Models
We then traina model with a primary contrastive objective; to this stan-dard configuration, we add a simple output head trained topredict the attentional map for each image, guided by thepseudo labels from teacher model.
Deep Saliency Prior for Reducing Visual Distraction
Using only a model that was trained to predict where people look at images, and no additional training data, we can produce a range of powerful editing effects for reducing distraction in images.
Origin of the Electronic Structure in Single-Layer FeSe/SrTiO3 Films
These observations not only show the first direct evidence that the electronic structure of single-layer FeSe/SrTiO3 films originates from bulk FeSe through a combined effect of an electronic phase transition and an interfacial charge transfer, but also provide a quantitative basis for theoretical models in describing the electronic structure and understanding the superconducting mechanism in single-layer FeSe/SrTiO3 films.
Band Gap
Superconductivity
Strongly Correlated Electrons
GazeGAN - Unpaired Adversarial Image Generation for Gaze Estimation
Recent research has demonstrated the ability to estimate gaze on mobile devices by performing inference on the image from the phone's front-facing camera, and without requiring specialized hardware.
Hash Bit Selection: A Unified Solution for Selection Problems in Hashing
We represent the bit pool as a vertx- and edge-weighted graph with the candidate bits as vertices.
