Search Results for author:
Found 16 papers, 11 papers with code
Exploring Dynamic Transformer for Efficient Object Tracking
For instance, DyTrack obtains 64. 9% AUC on LaSOT with a speed of 256 fps.
Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts
In this work, we propose to train a GAD model with few-shot normal images as sample prompts for AD on diverse datasets on the fly.
EmoWear: Exploring Emotional Teasers for Voice Message Interaction on Smartwatches
We introduce EmoWear, a smartwatch voice messaging system enabling users to apply 30 animation teasers on message bubbles to reflect emotions.
Tracking with Human-Intent Reasoning
The perception component then generates the tracking results based on the embeddings.
Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection
Extensive experiments on nine real-world anomaly detection datasets show that AHL can 1) substantially enhance different state-of-the-art OSAD models in detecting seen and unseen anomalies, and 2) effectively generalize to unseen anomalies in new domains.
DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs
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.
Tracking Anything in High Quality
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)
3rd Place Solution for PVUW2023 VSS Track: A Large Model for Semantic Segmentation on VSPW
In this paper, we introduce 3rd place solution for PVUW2023 VSS track.
A Causal Inference Framework for Leveraging External Controls in Hybrid Trials
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).
Unified Sequence-to-Sequence Learning for Single- and Multi-Modal Visual Object Tracking
In this paper, we introduce a new sequence-to-sequence learning framework for RGB-based and multi-modal object tracking.
Ranked #3 on
Visual Object Tracking
on NeedForSpeed
Anomaly Detection under Distribution Shift
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.
Visual Prompt Multi-Modal Tracking
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.
Ranked #6 on
Rgb-T Tracking
on LasHeR
SRRT: Search Region Regulation Tracking
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.
High-Performance Transformer Tracking
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.
AutoChart: A Dataset for Chart-to-Text Generation Task
The analytical description of charts is an exciting and important research area with many applications in academia and industry.
Transformer Tracking
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
