Search Results for author:
Found 12 papers, 3 papers with code
SmartMask: Context Aware High-Fidelity Mask Generation for Fine-grained Object Insertion and Layout Control
To overcome these limitations, we introduce SmartMask, which allows any novice user to create detailed masks for precise object insertion.
IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Models
We present a diffusion-based image morphing approach with perceptually-uniform sampling (IMPUS) that produces smooth, direct and realistic interpolations given an image pair.
Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback
Furthermore, we also find that the assertion level alignment scores provide a useful feedback which can then be used in a simple iterative procedure to gradually increase the expression of different assertions in the final image outputs.
Probabilistic and Semantic Descriptions of Image Manifolds and Their Applications
This paper begins with a description of methods for estimating image probability density functions that reflects the observation that such data is usually constrained to lie in restricted regions of the high-dimensional image space-not every pattern of pixels is an image.
High-Fidelity Guided Image Synthesis with Latent Diffusion Models
The user scribbles control the color composition while the text prompt provides control over the overall image semantics.
UAV-based Visual Remote Sensing for Automated Building Inspection
Unmanned Aerial Vehicle (UAV) based remote sensing system incorporated with computer vision has demonstrated potential for assisting building construction and in disaster management like damage assessment during earthquakes.
Paint2Pix: Interactive Painting based Progressive Image Synthesis and Editing
In particular, we propose a novel approach paint2pix, which learns to predict (and adapt) "what a user wants to draw" from rudimentary brushstroke inputs, by learning a mapping from the manifold of incomplete human paintings to their realistic renderings.
Intelli-Paint: Towards Developing Human-like Painting Agents
However, current research in this direction is often reliant on a progressive grid-based division strategy wherein the agent divides the overall image into successively finer grids, and then proceeds to paint each of them in parallel.
Sparse Attention Guided Dynamic Value Estimation for Single-Task Multi-Scene Reinforcement Learning
However, we argue that the sample variance for a multi-scene environment is best minimized by treating each scene as a distinct MDP, and then learning a joint value function V(s, M) dependent on both state s and MDP M. We further demonstrate that the true joint value function for a multi-scene environment, follows a multi-modal distribution which is not captured by traditional CNN / LSTM based critic networks.
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings
2) We also introduce invariance to the position and scale of the foreground object through a neural alignment model, which combines object localization and spatial transformer networks in an end to end manner, to zoom into a particular semantic instance.
Enhanced Scene Specificity with Sparse Dynamic Value Estimation
Recently, Singh et al. [1] tried to address this by proposing a dynamic value estimation approach that models the true joint value function distribution as a Gaussian mixture model (GMM).
Dynamic Value Estimation for Single-Task Multi-Scene Reinforcement Learning
Training deep reinforcement learning agents on environments with multiple levels / scenes / conditions from the same task, has become essential for many applications aiming to achieve generalization and domain transfer from simulation to the real world.
