FeedbackTrack: Visual-Cortex-Inspired Cross-Frame Feedback for Transformer Tracking
Visual object tracking requires effective temporal integration, yet most Transformer trackers still rely on predominantly feed-forward feature extraction. Existing temporal mechanisms typically update templates, prompts, queries, or prediction states, while intermediate representations are rarely reused to modulate corresponding processing stages. We propose \textbf{FeedbackTrack}, a visual-cortex-inspired framework that introduces sparse, group-level layer-aligned cross-frame feedback into pret
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
FedTVD: Balancing Data Quality and Quantity for Robust Federated Learning
arXiv · 10 August 2026
CIDER: A Dataset of Contextual Disclosure Boundaries for Privacy Preference Alignment
arXiv · 10 August 2026
TradeVerse: A Longitudinal Benchmark of Political Negotiation in International Trade
arXiv cs.CY · 10 August 2026
Label-Free Parkinson's Disease Screening from Face and Voice through Mechanistic Interpretability
arXiv cs.LG · 10 August 2026
Generative Proxy: Synthesizing Proxy-Based Interfaces for Real-World Interaction Across AR Glasses
arXiv cs.HC · 10 August 2026
GA-AFedOD: gradient-aligned active federated learning for resource-aware object detection in edge industrial IoT
Frontiers in Artificial Intelligence · 10 August 2026
How to cite this record
ethics.ai (10 August 2026), “FeedbackTrack: Visual-Cortex-Inspired Cross-Frame Feedback for Transformer Tracking,” evidence record 18014, https://ethics.ai/record/18014 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.