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Found 14 papers, 11 papers with code
Learning to Prompt for Vision-Language Models
It shifts from the tradition of using images and discrete labels for learning a fixed set of weights, seen as visual concepts, to aligning images and raw text for two separate encoders.
Energy-Based Open-World Uncertainty Modeling for Confidence Calibration
Confidence calibration is of great importance to the reliability of decisions made by machine learning systems.
MixStyle Neural Networks for Domain Generalization and Adaptation
In this work, we address domain generalization with MixStyle, a plug-and-play, parameter-free module that is simply inserted to shallow CNN layers and requires no modification to training objectives.
Semi-Supervised Domain Generalization with Stochastic StyleMatch
Our proposed approach, StyleMatch, is inspired by FixMatch, a state-of-the-art semi-supervised learning method based on pseudo-labeling, with several new ingredients tailored to solve SSDG.
Domain Generalization with MixStyle
Our method, termed MixStyle, is motivated by the observation that visual domain is closely related to image style (e. g., photo vs.~sketch images).
Domain Generalization in Vision: A Survey
In particular, intensive research in this topic has led to a broad spectrum of methodologies, e. g., those based on domain alignment, meta-learning, data augmentation, or ensemble learning, just to name a few; and has covered various vision applications such as object recognition, segmentation, action recognition, and person re-identification.
Learning to Generate Novel Domains for Domain Generalization
This explicitly increases the diversity of available training domains and leads to a more generalizable model.
Domain Adaptive Ensemble Learning
Each such classifier is an expert to its own domain and a non-expert to others.
Deep Domain-Adversarial Image Generation for Domain Generalisation
This is achieved by having a learning objective formulated to ensure that the generated data can be correctly classified by the label classifier while fooling the domain classifier.
Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch
Person re-identification (re-ID), which aims to re-identify people across different camera views, has been significantly advanced by deep learning in recent years, particularly with convolutional neural networks (CNNs).
Learning Generalisable Omni-Scale Representations for Person Re-Identification
An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation.
Unsupervised Domain Adaptation
Unsupervised Person Re-Identification
Omni-Scale Feature Learning for Person Re-Identification
As an instance-level recognition problem, person re-identification (ReID) relies on discriminative features, which not only capture different spatial scales but also encapsulate an arbitrary combination of multiple scales.
Ranked #7 on
Person Re-Identification
on CUHK03
Video Summarisation by Classification with Deep Reinforcement Learning
Most existing video summarisation methods are based on either supervised or unsupervised learning.
Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward
Video summarization aims to facilitate large-scale video browsing by producing short, concise summaries that are diverse and representative of original videos.
Ranked #3 on
Supervised Video Summarization
on SumMe