Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks182 from public data
- A Comprehensive Survey on Test-Time Adaptation Under Distribution Shifts653
This survey categorizes Test-time adaptation into several distinct groups based on the form of test data, namely, test-time domain adaptation, test-time batch adaptation, and online test-time adaptation.
- NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion373
Compared to several strong baselines, N\"UWA achieves state-of-the-art results on text-to-image generation, text- to-video generation, video prediction, etc, and it also shows surprisingly good zero-shot capabilities onText-guided image and video manipulation tasks.
- Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer370
A novel approach called Source HypOthesis Transfer (SHOT), which learns the feature extraction module for the target domain by fitting the target data features to the frozen source classification module (representing classification hypothesis), and which achieves results surpassing or comparable to the state-of-the-arts.
- Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-free Approach341
The proposed method leverages Fully Convolutional Network (FCN) to generate fix-sized spatial feature maps such that pixel-level features are consistent and can decrease the similarity of coupled images from different persons and increase that from the same person.
- Causality Inspired Representation Learning for Domain Generalization301
A general structural causal model is introduced and a Causality Inspired Representation Learning (CIRL) algorithm is proposed that enforces the representations to satisfy the above properties and then uses them to simulate the causal factors, which yields improved generalization ability.
- Domain Adaptation with Auxiliary Target Domain-Oriented Classifier190
A new pseudo-labeling framework called Auxiliary Target Domain-Oriented Classifier (ATDOC) is proposed, which alleviates the classifier bias by introducing an auxiliary classifier for target data only, to improve the quality of pseudo labels.
- 176
- TALL: Thumbnail Layout for Deepfake Video Detection153
A simple yet effective strategy named Thumbnail Layout (TALL), which transforms a video clip into a pre-defined layout to realize the preservation of spatial and temporal dependencies, which is incorporated into Swin Transformer, forming an efficient and effective method TALL-Swin.
- 143
- 136
- 134
- Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation133
The proposed Bi-Classifier Determinacy Maximization (BCDM) is designed with a novel classifier determinacy disparity (CDD) metric, which formulates classifier discrepancy as the class relevance of distinct target predictions and implicitly introduces constraint on the target feature discriminability.
- A Balanced and Uncertainty-Aware Approach for Partial Domain Adaptation126
A novel domain adaptation method BA$3$US is proposed that emphasizes uncertain samples and exploits an adaptive weighted complement entropy objective to encourage incorrect classes to have uniform and low prediction scores.
- 124
- DINE: Domain Adaptation from Single and Multiple Black-box Predictors120
A new two-step knowledge adaptation framework called DIstill and fine-tuNE (DINE) is proposed, which distills the knowledge from the source predictor to a customized target model, then fine-tunes the distilled model to further fit the target domain.
- Exploring uncertainty in pseudo-label guided unsupervised domain adaptation120
This paper proposes to progressively increase the number of target training samples and incorporate the uncertainties to accurately characterize both cross-domain distribution discrepancy and other intra-domain relations, and significantly outperforms recent state-of-the-art approaches.
- Semantic Concentration for Domain Adaptation114
Semantic Concentration for Domain Adaptation is proposed, which encourages the model to concentrate on the most principal features via the pair-wise adversarial alignment of prediction distributions, and can be easily integrated into various DA methods as a regularizer to further boost their performance.
- NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis105
NUWA-Infinity is presented, a generative model for infinite visual synthesis, which is defined as the task of generating arbitrarily-sized high-resolution images or long-duration videos and has superior visual synthesis capabilities in terms of resolution and variable-size generation.
- Learning Feature Recovery Transformer for Occluded Person Re-Identification101
A new approach called Feature Recovery Transformer (FRT) to address the two challenges simultaneously, which mainly consists of visibility graph matching and feature recovery transformer, which significantly outperforms state-of-the-art results.
- Why Attentions May Not Be Interpretable?95
It is demonstrated that one root cause of this phenomenon is the combinatorial shortcuts, which means that, in addition to the highlighted parts, the attention weights themselves may carry extra information that could be utilized by downstream models after attention layers.
- 95
- 91
- Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning89
This work proposes {\sc FedDecorr], a novel method that can effectively mitigate dimensional collapse in federated learning that applies a regularization term during local training that encourages different dimensions of representations to be uncorrelated.
- Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning89
Class-wise Decorrelation (CwD) is proposed that regularizes representations of each class to scatter more uniformly, thus mimicking the model jointly trained with all classes (i.e., the oracle model), and is simple to implement and easy to plug into existing methods.
- Improving Zero-Shot Generalization for CLIP with Synthesized Prompts85
A plug-and-play generative approach to improve existing fine-tuning methods in pretrained vision-language models by introducing a generator that reconstructs the visual features by inputting the synthesized prompts and the corresponding class names to the textual encoder of CLIP.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.