Search Results for author:
Found 19 papers, 6 papers with code
Generating Editable Head Avatars with 3D Gaussian GANs
We propose a novel approach that enhances the editability and animation control of 3D head avatars by incorporating 3D Gaussian Splatting (3DGS) as an explicit 3D representation.
Consistent Diffusion: Denoising Diffusion Model with Data-Consistent Training for Image Restoration
In this work, we address the limitations of denoising diffusion models (DDMs) in image restoration tasks, particularly the shape and color distortions that can compromise image quality.
Leveraging Predicate and Triplet Learning for Scene Graph Generation
We observe that visual variations within the identical triplet are relatively small and certain relation cues are shared in the same type of triplet, which can potentially facilitate the relation learning in SGG.
ReWiTe: Realistic Wide-angle and Telephoto Dual Camera Fusion Dataset via Beam Splitter Camera Rig
The fusion of images from dual camera systems featuring a wide-angle and a telephoto camera has become a hotspot problem recently.
BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation
In real recommendation scenarios, users often have different types of behaviors, such as clicking and buying.
View Transition based Dual Camera Image Fusion
Our insight is to minimize the occlusion area and thus maximize the use of pixels from $\bf{T}$ images.
Zero-Shot Scene Graph Generation via Triplet Calibration and Reduction
In our framework, a triplet calibration loss is first presented to regularize the representations of diverse triplets and to simultaneously excavate the unseen triplets in incompletely annotated training scene graphs.
CUR Transformer: A Convolutional Unbiased Regional Transformer for Image Denoising
Based on the prior that, for each pixel, its similar pixels are usually spatially close, our insights are that (1) we partition the image into non-overlapped windows and perform regional self-attention to reduce the search range of each pixel, and (2) we encourage pixels across different windows to communicate with each other.
MIEHDR CNN: Main Image Enhancement based Ghost-Free High Dynamic Range Imaging using Dual-Lens Systems
In most of the related HDR imaging methods, the problem is usually solved by Multiple Images Merging, i. e. the final HDR image is fused from pixels of all the input LDR images.
Cycle-CNN for Colorization towards Real Monochrome-Color Camera Systems
We present a new CNN model, named cycle CNN, which can directly use the real data from monochrome-color camera systems for training.
Learning a Deep Convolutional Network for Colorization in Monochrome-Color Dual-Lens System
To get high-quality color images, it is desired to colorize the gray image with the color image as reference.
VLAD3: Encoding Dynamics of Deep Features for Action Recognition
To account for long-range inhomogeneous dynamics, a VLAD descriptor is derived for the LDS and pooled over the whole video, to arrive at the final VLAD^3 representation.
Joint Image-Text News Topic Detection and Tracking with And-Or Graph Representation
The AOG embeds a context sensitive grammar that can describe the hierarchical composition of news topics by semantic elements about people involved, related places and what happened, and model contextual relationships between elements in the hierarchy.
Fidelity-Naturalness Evaluation of Single Image Super Resolution
In this paper, we proposed to use both fidelity (the difference with original images) and naturalness (human visual perception of super resolved images) for evaluation.
Ground-truth dataset and baseline evaluations for image base-detail separation algorithms
Base-detail separation is a fundamental computer vision problem consisting of modeling a smooth base layer with the coarse structures, and a detail layer containing the texture-like structures.
Multiple Instance Learning for Soft Bags via Top Instances
Under this formulation, both positive and negative bags are soft, in the sense that negative bags can also contain positive instances.
Visual Persuasion: Inferring Communicative Intents of Images
In this paper we introduce the novel problem of understanding visual persuasion.
Recognizing Activities via Bag of Words for Attribute Dynamics
A video sequence is decomposed into short-term segments, which are characterized by the dynamics of their attributes.
Recognizing Activities by Attribute Dynamics
The proposed method is shown to outperform similar classifiers derived from the kernel dynamic system (KDS) and state-of-the-art approaches for dynamics-based or attribute-based action recognition.
