arXiv (All)
open access
Exploiting Stability-Plasticity Asymmetry in Pretrained Detectors for Incremental Object Detection
Pretrained model-based incremental object detection (PTMIOD) leverages the rich detection priors of pretrained detectors to learn new categories incrementally while preserving detection ability on previously learned ones. Existing methods mainly exploit pretrained detectors as a whole, without expli
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Achieving Near-Zero-Overhead Multi-Model Hierarchical Classification in Real-Time Detection Pipelines#447913
- PiVoT: A Variational Solution for Real-time Large-scale Multi-object Detection and Tracking under Heavy Clutter#370333
- SPK: Eliciting Structured Prior Knowledge for Interpretable Out-of-Distribution Detection in Real-Time Object Detection#467121
- Multi-Scale Fruit Capsules: Dilated Convolutions and Dynamic Routing for In-the-Wild Explainable Fruit Recognition#1048789
- VLM4Cluster: Benchmarking Deep Clustering In the Era of Vision-Language Pre-training#1131849
Record · ID 820196
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive