3 papers
cs.CV2026
Learning Where to Embed: Noise-Aware Positional Embedding for Query Retrieval in Small-Object Detection
Yangchen Zeng, Zhenyu Yu, Dongming Jiang +5
Transformer-based detectors have advanced small-object detection, but they often remain inefficient and vulnerable to background-induced query noise, which motivates deep decoders…
cs.CV2025
PrePrompt: Predictive prompting for class incremental learning
Libo Huang, Zhulin An, Chuanguang Yang +5
Class Incremental Learning (CIL) based on pre-trained models offers a promising direction for open-world continual learning. Existing methods typically rely on correlation-based st…
cs.CV2025
HMPE:HeatMap Embedding for Efficient Transformer-Based Small Object Detection
YangChen Zeng
Current Transformer-based methods for small object detection continue emerging, yet they have still exhibited significant shortcomings. This paper introduces HeatMap Position Embed…