Search Results for author:
Found 7 papers, 7 papers with code
Confusing Pair Correction Based on Category Prototype for Domain Adaptation under Noisy Environments
In this paper, we address unsupervised domain adaptation under noisy environments, which is more challenging and practical than traditional domain adaptation.
Orthogonal Temporal Interpolation for Zero-Shot Video Recognition
We propose a model called OTI for ZSVR by employing orthogonal temporal interpolation and the matching loss based on VLMs.
Ranked #1 on
Zero-Shot Action Recognition
on UCF101
Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain Adaptation
Due to the domain discrepancy in visual domain adaptation, the performance of source model degrades when bumping into the high data density near decision boundary in target domain.
Learning Invariant Representation with Consistency and Diversity for Semi-supervised Source Hypothesis Transfer
Semi-supervised domain adaptation (SSDA) aims to solve tasks in target domain by utilizing transferable information learned from the available source domain and a few labeled target data.
Gradually Vanishing Bridge for Adversarial Domain Adaptation
On the discriminator, GVB contributes to enhance the discriminating ability, and balance the adversarial training process.
Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations
We find by theoretical analysis that the prediction discriminability and diversity could be separately measured by the Frobenius-norm and rank of the batch output matrix.
Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization
We address the unsupervised open domain recognition (UODR) problem, where categories in labeled source domain S is only a subset of those in unlabeled target domain T. The task is to correctly classify all samples in T including known and unknown categories.
