The ability to understand and reason the 3D real world is a crucial milestone towards artificial general intelligence.
Existing domain generalization (DG) methods which are unable to exploit unlabeled data perform poorly compared to semi-supervised learning (SSL) methods under SSDG setting.
In this work, we adapt state-of-the-art techniques for counterfactual generation in the domain of XAI that are based on genetic algorithms to consider a series of temporal constraints at runtime.
Addressing this, we propose UAV-C, a large-scale benchmark for assessing robustness of UAV trackers under common corruptions.
Context information, such as road maps and surrounding agents' states, provides crucial geometric and semantic information for motion behavior prediction.
Continual learning can empower vision-language models to continuously acquire new knowledge, without the need for access to the entire historical dataset.
In response to this challenge, we propose a novel diffusion model reconstruction framework tailored for 3D seismic data.
We present a hybrid-view-based knowledge distillation framework, termed HVDistill, to guide the feature learning of a point cloud neural network with a pre-trained image network in an unsupervised man- ner.
To address this issue, we propose a novel Implicit Discriminative Knowledge Learning (IDKL) network to uncover and leverage the implicit discriminative information contained within the modality-specific.
We show that pre-training models for the detection of offensive content on HateCOT significantly boots open-sourced Language Models on three benchmark datasets in both zero and few-shot settings, despite differences in domain and task.}