LoopTrack: A Simple Baseline for Parameter-Efficient Transformer Tracking

arXiv:2609.33306 · cs.CV · Submitted 2026-09-27 · Read on arXiv

cs.CV

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• Transformer — 85× in this paper · explained in 2 episodes
• Accuracy — 19× in this paper · explained in 1 episode
• Ablation — 18× in this paper · explained in 1 episode
• Aggregation — 9× in this paper · explained in 1 episode
• Distillation — 9× in this paper · explained in 2 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Gated Target Memory — 8× in this paper
• Pruning — 7× in this paper · explained in 4 episodes
• Target-Aware Looping — 7× in this paper
• Appearance Pooling — 5× in this paper
• Feature Extraction — 4× in this paper · explained in 2 episodes
• Gated Update — 4× in this paper
• Ablation Studies — 3× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Investigating Single-Block Recurrence in Vision Transformers for Image Recognition
• UTPTrack: Towards Simple and Unified Token Pruning for Visual Tracking

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models