Search Results for author:
Found 16 papers, 5 papers with code
LoopGaussian: Creating 3D Cinemagraph with Multi-view Images via Eulerian Motion Field
In this paper, inspired by significant progress in the field of novel view synthesis (NVS) achieved by 3D Gaussian Splatting (3D-GS), we propose LoopGaussian to elevate cinemagraph from 2D image space to 3D space using 3D Gaussian modeling.
Scalable Lipschitz Estimation for CNNs
We prove an upper-bound on the Lipschitz constant of the larger block in terms of the Lipschitz constants of the smaller blocks.
Progressive Feature Self-reinforcement for Weakly Supervised Semantic Segmentation
Building upon this, we introduce a complementary self-enhancement method that constrains the semantic consistency between these confident regions and an augmented image with the same class labels.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Bi-directional Distribution Alignment for Transductive Zero-Shot Learning
It is well-known that zero-shot learning (ZSL) can suffer severely from the problem of domain shift, where the true and learned data distributions for the unseen classes do not match.
Faster Riemannian Newton-type Optimization by Subsampling and Cubic Regularization
The proposed algorithm exhibits improved computational speed and convergence behavior compared to a large set of state-of-the-art Riemannian optimization algorithms.
A Unified Theory of Diversity in Ensemble Learning
We present a theory of ensemble diversity, explaining the nature of diversity for a wide range of supervised learning scenarios.
Data-driven Approaches to Surrogate Machine Learning Model Development
Each of these methods have seen widespread use in the field of machine learning, however, here we apply them specifically to surrogate machine learning model development.
Parameterization of Cross-Token Relations with Relative Positional Encoding for Vision MLP
They use token-mixing layers to capture cross-token interactions, as opposed to the multi-head self-attention mechanism used by Transformers.
Bias-Variance Decompositions for Margin Losses
We introduce a novel bias-variance decomposition for a range of strictly convex margin losses, including the logistic loss (minimized by the classic LogitBoost algorithm), as well as the squared margin loss and canonical boosting loss.
Ontology-based n-ball Concept Embeddings Informing Few-shot Image Classification
The approach consists of two components - converting symbolic knowledge of an ontology into continuous space by learning n-ball embeddings that capture properties of subsumption and disjointness, and guiding the training and inference of a vision model using the learnt embeddings.
Visual-Semantic Embedding Model Informed by Structured Knowledge
With respect to both standard and zero-shot image classification, our approach shows superior performance compared with the original approach, which uses word embeddings.
Zero-Shot Human-Object Interaction Recognition via Affordance Graphs
We propose a loss function with the aim of distilling the knowledge contained in the graph into the model, while also using the graph to regularise learnt representations by imposing a local structure on the latent space.
Modelling Instance-Level Annotator Reliability for Natural Language Labelling Tasks
It can automatically estimate the per-instance reliability of each annotator and the correct label for each instance.
On Class Imbalance and Background Filtering in Visual Relationship Detection
In this paper we investigate the problems of class imbalance and irrelevant relationships in Visual Relationship Detection (VRD).
VommaNet: an End-to-End Network for Disparity Estimation from Reflective and Texture-less Light Field Images
The precise combination of image sensor and micro-lens array enables lenslet light field cameras to record both angular and spatial information of incoming light, therefore, one can calculate disparity and depth from light field images.
Distributed Document and Phrase Co-embeddings for Descriptive Clustering
Descriptive document clustering aims to automatically discover groups of semantically related documents and to assign a meaningful label to characterise the content of each cluster.
