Cross-modal retrieval has been a compelling topic in the multimodal community. Recently,
to mitigate the high cost of data collection, the co-occurred pairs (eg, image and text) could …

Abstract Domain Generalizable (DG) person ReID is a challenging task which trains a model
on source domains yet generalizes well on target domains. Existing methods use source …

Defense models against adversarial attacks have grown significantly, but the lack of
practical evaluation methods has hindered progress. Evaluation can be defined as looking …

Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic
model for capturing task-specific knowledge of the test task, rely only on few-labeled support …

Meta-learning offers an effective solution to learn new concepts under scarce supervision
through an episodic-training scheme: a series of target-like tasks sampled from base classes …

Aerial-view geo-localization tends to determine an unknown position through matching the
drone-view image with the geo-tagged satellite-view image. This task is mostly regarded as …

Collecting a substantial number of labeled samples is infeasible in many real-world
scenarios, thereby bringing out challenges for supervised classification. The research on …

The goal of data-free meta-learning is to learn useful prior knowledge from a collection of
pre-trained models without accessing their training data. However, existing works only solve …

Few-shot learning (FSL) is essential in practical applications. However, the limited training
examples make the models more vulnerable to label noise, which can lead to poor …

Multi-object tracking (MOT) has become a hot task in multi-media analysis. It not only locates
the objects but also maintains their unique identities. However, previous methods encounter …