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Found 11 papers, 2 papers with code
Learning Tracking Representations from Single Point Annotations
In this paper, we propose to learn tracking representations from single point annotations (i. e., 4. 5x faster to annotate than the traditional bounding box) in a weakly supervised manner.
Robust Unsupervised Crowd Counting and Localization with Adaptive Resolution SAM
The existing crowd counting models require extensive training data, which is time-consuming to annotate.
Scalable Video Object Segmentation with Simplified Framework
The current popular methods for video object segmentation (VOS) implement feature matching through several hand-crafted modules that separately perform feature extraction and matching.
DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks
However, we find that this simple baseline heavily relies on spatial cues while ignoring temporal relations for frame reconstruction, thus leading to sub-optimal temporal matching representations for VOT and VOS.
Ranked #1 on
Visual Object Tracking
on TrackingNet
(AUC metric)
TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer
We further demonstrate the generalizability of our method on hand mesh recovery.
A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds
Recent works on 3D single object tracking treat the task as a target-specific 3D detection task, where an off-the-shelf 3D detector is commonly employed for the tracking.
Progressive Unsupervised Learning for Visual Object Tracking
In this paper, we propose a progressive unsupervised learning (PUL) framework, which entirely removes the need for annotated training videos in visual tracking.
End-to-end Learning of Object Motion Estimation from Retinal Events for Event-based Object Tracking
We feed the sequence of TSLTD frames to a novel Retinal Motion Regression Network (RMRNet) to perform an end-to-end 5-DoF object motion regression.
Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking
To achieve this goal, we present an Adaptive Time-Surface with Linear Time Decay (ATSLTD) event-to-frame conversion algorithm, which asynchronously and effectively warps the spatio-temporal information of asynchronous retinal events to a sequence of ATSLTD frames with clear object contours.
Hallucinated Adversarial Learning for Robust Visual Tracking
Incorporating such an ability to hallucinate diverse new samples of the tracked instance can help the trackers alleviate the over-fitting problem in the low-data tracking regime.
DSNet: Deep and Shallow Feature Learning for Efficient Visual Tracking
In recent years, Discriminative Correlation Filter (DCF) based tracking methods have achieved great success in visual tracking.
