Search Results for author:
Found 10 papers, 3 papers with code
Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence
In this paper, to overcome the limitation, we propose CS-Aligner, a novel and straightforward framework that performs distributional vision-language alignment by integrating Cauchy-Schwarz (CS) divergence with mutual information.
Geometric Neural Process Fields
This paper addresses the challenge of Neural Field (NeF) generalization, where models must efficiently adapt to new signals given only a few observations.
Domain Adaptation with Cauchy-Schwarz Divergence
Furthermore, we illustrate that the CS divergence enables a simple estimator on the discrepancy of both marginal and conditional distributions between source and target domains in the representation space, without requiring any distributional assumptions.
Dynamic Prototype Adaptation with Distillation for Few-shot Point Cloud Segmentation
Existing prototype-based methods rely on support prototypes to guide the segmentation of query point clouds, but they encounter challenges when significant object variations exist between the support prototypes and query features.
Motion Flow Matching for Human Motion Synthesis and Editing
In this paper, we propose \emph{Motion Flow Matching}, a novel generative model designed for human motion generation featuring efficient sampling and effectiveness in motion editing applications.
Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network
However, the appearance variations between objects from the same category could be extremely large, leading to unreliable feature matching and query mask prediction.
Ranked #45 on
Few-Shot Semantic Segmentation
on PASCAL-5i (1-Shot)
Principle of Relevant Information for Graph Sparsification
Graph sparsification aims to reduce the number of edges of a graph while maintaining its structural properties.
Bilevel Continual Learning
This paper presents Bilevel Continual Learning (BiCL), a general framework for continual learning that fuses bilevel optimization and recent advances in meta-learning for deep neural networks.
Learning an Interpretable Graph Structure in Multi-Task Learning
We present a novel methodology to jointly perform multi-task learning and infer intrinsic relationship among tasks by an interpretable and sparse graph.
Towards Interpretable Multi-Task Learning Using Bilevel Programming
Interpretable Multi-Task Learning can be expressed as learning a sparse graph of the task relationship based on the prediction performance of the learned models.
