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Found 24 papers, 10 papers with code
SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model
By contrast, multi-modal inpainting provides more flexible and useful controls on the inpainted content, \eg, a text prompt can be used to describe an object with richer attributes, and a mask can be used to constrain the shape of the inpainted object rather than being only considered as a missing area.
ObjectStitch: Generative Object Compositing
Object compositing based on 2D images is a challenging problem since it typically involves multiple processing stages such as color harmonization, geometry correction and shadow generation to generate realistic results.
Toward Understanding WordArt: Corner-Guided Transformer for Scene Text Recognition
Thirdly, we utilize Transformer to learn the global feature on image-level and model the global relationship of the corner points, with the assistance of a corner-query cross-attention mechanism.
SImProv: Scalable Image Provenance Framework for Robust Content Attribution
We present SImProv - a scalable image provenance framework to match a query image back to a trusted database of originals and identify possible manipulations on the query.
Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training
However, most existing works are still trapped in the dilemma between higher accuracy and stronger robustness since they tend to fit a model towards robust features (not easily tampered with by adversaries) while ignoring those non-robust but highly predictive features.
GALA: Toward Geometry-and-Lighting-Aware Object Search for Compositing
To move a step further, this paper proposes GALA (Geometry-and-Lighting-Aware), a generic foreground object search method with discriminative modeling on geometry and lighting compatibility for open-world image compositing.
Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models
Prioritizing fairness is of central importance in artificial intelligence (AI) systems, especially for those societal applications, e. g., hiring systems should recommend applicants equally from different demographic groups, and risk assessment systems must eliminate racism in criminal justice.
OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical settings
In the real-world clinical setting, OpenClinicalAI significantly outperforms the state-of-the-art AI system.
Font Completion and Manipulation by Cycling Between Multi-Modality Representations
The novel graph constructor maps a glyph's latent code to its graph representation that matches expert knowledge, which is trained to help the translation task.
Feature Importance-aware Transferable Adversarial Attacks
More specifically, we obtain feature importance by introducing the aggregate gradient, which averages the gradients with respect to feature maps of the source model, computed on a batch of random transforms of the original clean image.
A Multi-Implicit Neural Representation for Fonts
Fonts are ubiquitous across documents and come in a variety of styles.
Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach
We also introduce Text Refinement Network (TexRNet), a novel text segmentation approach that adapts to the unique properties of text, e. g. non-convex boundary, diverse texture, etc., which often impose burdens on traditional segmentation models.
Texture Hallucination for Large-Factor Painting Super-Resolution
We aim to super-resolve digital paintings, synthesizing realistic details from high-resolution reference painting materials for very large scaling factors (e. g., 8X, 16X).
A Semantic-based Medical Image Fusion Approach
Medical image fusion is a promising approach to providing overall information from medical images of different modalities.
A new direction to promote the implementation of artificial intelligence in natural clinical settings
Artificial intelligence (AI) researchers claim that they have made great `achievements' in clinical realms.
Image Super-Resolution by Neural Texture Transfer
Reference-based super-resolution (RefSR), on the other hand, has proven to be promising in recovering high-resolution (HR) details when a reference (Ref) image with similar content as that of the LR input is given.
Ranked #1 on
Image Super-Resolution
on CUFED5 - 4x upscaling
Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning
Although the state-of-the-art attacking techniques that incorporated the advance of Generative adversarial networks (GANs) could construct class representatives of the global data distribution among all clients, it is still challenging to distinguishably attack a specific client (i. e., user-level privacy leakage), which is a stronger privacy threat to precisely recover the private data from a specific client.
Reference-Conditioned Super-Resolution by Neural Texture Transfer
We focus on transferring the high-resolution texture from reference images to the super-resolution process without the constraint of content similarity between reference and target images, which is a key difference from previous example-based methods.
Decoupled Learning for Conditional Adversarial Networks
Incorporating encoding-decoding nets with adversarial nets has been widely adopted in image generation tasks.
r-BTN: Cross-domain Face Composite and Synthesis from Limited Facial Patches
A more generalized question is that if a large proportion (e. g., more than 50%) of the face/sketch is missing, can a realistic whole face sketch/image still be estimated.
Age Progression/Regression by Conditional Adversarial Autoencoder
In CAAE, the face is first mapped to a latent vector through a convolutional encoder, and then the vector is projected to the face manifold conditional on age through a deconvolutional generator.
Derivative Delay Embedding: Online Modeling of Streaming Time Series
The staggering amount of streaming time series coming from the real world calls for more efficient and effective online modeling solution.
