Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i. e., samples from open-set anomaly classes), while effectively identifying the seen anomalies.
To address this, we propose a novel architecture called Darkness Clue-Prompted Tracking (DCPT) that achieves robust UAV tracking at night by efficiently learning to generate darkness clue prompts.
To further improve the quality of tracking masks, a pretrained MR model is employed to refine the tracking results.
Ranked #5 on Semi-Supervised Video Object Segmentation on YouTube-VOS 2019 (using extra training data)
In this paper, we introduce 3rd place solution for PVUW2023 VSS track.
We consider the challenges associated with causal inference in settings where data from a randomized trial is augmented with control data from an external source to improve efficiency in estimating the average treatment effect (ATE).
Anomaly detection (AD) is a crucial machine learning task that aims to learn patterns from a set of normal training samples to identify abnormal samples in test data.
To inherit the powerful representations of the foundation model, a natural modus operandi for multi-modal tracking is full fine-tuning on the RGB-based parameters.
Trackers tend to lose the target object due to the limited search region or be interfered with by distractors due to the excessive search region.
First, we present a transformer tracking (named TransT) method based on the Siamese-like feature extraction backbone, the designed attention-based fusion mechanism, and the classification and regression head.
The analytical description of charts is an exciting and important research area with many applications in academia and industry.
The correlation operation is a simple fusion manner to consider the similarity between the template and the search region.
Ranked #5 on Visual Tracking on TNL2K