# Background Learnable Cascade for Zero-Shot Object Detection

arXiv: https://arxiv.org/abs/2010.04502 · HTML: https://mlsanity.com/paper/2010.04502/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2020.

## Built on by (most cited first, 30 of 31)

- [Open-vocabulary Object Detection via Vision and Language Knowledge Distillation](https://mlsanity.com/paper/2104.13921/) (2021)
- [F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models](https://mlsanity.com/paper/2209.15639/) (2022)
- [Aligning Bag of Regions for Open-Vocabulary Object Detection](https://mlsanity.com/paper/2302.13996/) (2023)
- [FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation](https://mlsanity.com/paper/2303.17225/) (2023)
- [Vision-Language Models in Remote Sensing: Current Progress and Future Trends](https://mlsanity.com/paper/2305.05726/) (2023)
- [Region-Aware Pretraining for Open-Vocabulary Object Detection with Vision Transformers](https://mlsanity.com/paper/2305.07011/) (2023)
- [Open-Vocabulary One-Stage Detection with Hierarchical Visual-Language Knowledge Distillation](https://mlsanity.com/paper/2203.10593/) (2022)
- [A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future](https://mlsanity.com/paper/2307.09220/) (2023)
- [Zero-Shot Instance Segmentation](https://mlsanity.com/paper/2104.06601/) (2021)
- [A Survey of Self-Supervised and Few-Shot Object Detection](https://mlsanity.com/paper/2110.14711/) (2021)
- [Contrastive Feature Masking Open-Vocabulary Vision Transformer](https://mlsanity.com/paper/2309.00775/) (2023)
- [Semantics-Guided Contrastive Network for Zero-Shot Object detection](https://mlsanity.com/paper/2109.06062/) (2021)
- [Robust Region Feature Synthesizer for Zero-Shot Object Detection](https://mlsanity.com/paper/2201.00103/) (2022)
- [Semantic-Promoted Debiasing and Background Disambiguation for Zero-Shot Instance Segmentation](https://mlsanity.com/paper/2305.13173/) (2023)
- [Weakly Supervised Open-Vocabulary Object Detection](https://mlsanity.com/paper/2312.12437/) (2023)
- [Zero-shot Object Detection Through Vision-Language Embedding Alignment](https://mlsanity.com/paper/2109.12066/) (2021)
- [Fine-Grained Zero-Shot Object Detection](https://mlsanity.com/paper/2507.10358/) (2025)
- [A Survey of Deep Learning for Low-Shot Object Detection](https://mlsanity.com/paper/2112.02814/) (2021)
- [Contrastive Object Detection Using Knowledge Graph Embeddings](https://mlsanity.com/paper/2112.11366/) (2021)
- [SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection](https://mlsanity.com/paper/2310.04689/) (2023)
- [Frustratingly Simple but Effective Zero-shot Detection and Segmentation: Analysis and a Strong Baseline](https://mlsanity.com/paper/2302.07319/) (2023)
- [Exploiting Unlabeled Data with Multiple Expert Teachers for Open Vocabulary Aerial Object Detection and Its Orientation Adaptation](https://mlsanity.com/paper/2411.02057/) (2024)
- [Query-guided Attention in Vision Transformers for Localizing Objects Using a Single Sketch](https://mlsanity.com/paper/2303.08784/) (2023)
- [Synthesizing Knowledge-enhanced Features for Real-world Zero-shot Food Detection](https://mlsanity.com/paper/2402.09242/) (2024)
- [AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines](https://mlsanity.com/paper/2408.02181/) (2024)
- [Resolving Semantic Confusions for Improved Zero-Shot Detection](https://mlsanity.com/paper/2212.06097/) (2022)
- [Meta-ZSDETR: Zero-shot DETR with Meta-learning](https://mlsanity.com/paper/2308.09540/) (2023)
- [Review of Zero-Shot and Few-Shot AI Algorithms in The Medical Domain](https://mlsanity.com/paper/2406.16143/) (2024)
- [Caption Generation on Scenes with Seen and Unseen Object Categories](https://mlsanity.com/paper/2108.06165/) (2021)
- [Co-Occurrence Matters: Learning Action Relation for Temporal Action Localization](https://mlsanity.com/paper/2303.08463/) (2023)

## Abstract (arXiv)

Zero-shot detection (ZSD) is crucial to large-scale object detection with the aim of simultaneously localizing and recognizing unseen objects. There remain several challenges for ZSD, including reducing the ambiguity between background and unseen objects as well as improving the alignment between visual and semantic concept. In this work, we propose a novel framework named Background Learnable Cascade (BLC) to improve ZSD performance. The major contributions for BLC are as follows: (i) we propose a multi-stage cascade structure named Cascade Semantic R-CNN to progressively refine the alignment between visual and semantic of ZSD; (ii) we develop the semantic information flow structure and directly add it between each stage in Cascade Semantic RCNN to further improve the semantic feature learning; (iii) we propose the background learnable region proposal network (BLRPN) to learn an appropriate word vector for background class and use this learned vector in Cascade Semantic R CNN, this design makes Background Learnable" and reduces the confusion between background and unseen classes. Our extensive experiments show BLC obtains significantly performance improvements for MS-COCO over state-of-the-art methods.