113 citations · 282 across the 27 of their papers we have counts for
27 papers
MMDG-Bench: A Benchmark for Multimodal Domain Generalization
Qianshan Zhan, Qian Wang, Da Li +2
Multi-modal Domain Generalization (MMDG) seeks to leverage complementary modalities to enhance model robustness on unseen domains. Despite extensive progress in Multi-modal Learnin…
Sketch-based Video Object Segmentation: Benchmark and Analysis
Ruolin Yang, Da Li, Conghui Hu +3
Reference-based video object segmentation is an emerging topic which aims to segment the corresponding target object in each video frame referred by a given reference, such as a la…
Better Practices for Domain Adaptation
Linus Ericsson, Da Li, Timothy M. Hospedales
Distribution shifts are all too common in real-world applications of machine learning. Domain adaptation (DA) aims to address this by providing various frameworks for adapting mode…
Label Calibration for Semantic Segmentation Under Domain Shift
Ondrej Bohdal, Da Li, Timothy Hospedales
Performance of a pre-trained semantic segmentation model is likely to substantially decrease on data from a new domain. We show a pre-trained model can be adapted to unlabelled tar…
Feed-Forward Source-Free Domain Adaptation via Class Prototypes
Ondrej Bohdal, Da Li, Timothy Hospedales
Source-free domain adaptation has become popular because of its practical usefulness and no need to access source data. However, the adaptation process still takes a considerable a…
Neural Fine-Tuning Search for Few-Shot Learning
Panagiotis Eustratiadis, Łukasz Dudziak, Da Li +1
In few-shot recognition, a classifier that has been trained on one set of classes is required to rapidly adapt and generalize to a disjoint, novel set of classes. To that end, rece…