# CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection

arXiv: https://arxiv.org/abs/2301.01970 · HTML: https://mlsanity.com/paper/2301.01970/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 50 references that are in the map.
- First posted on arXiv 5 Jan 2023.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Exploring Plain Vision Transformer Backbones for Object Detection](https://mlsanity.com/paper/2203.16527/) (2022)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [An End-to-End Transformer Model for 3D Object Detection](https://mlsanity.com/paper/2109.08141/) (2021)
- [Towards Open World Object Detection](https://mlsanity.com/paper/2103.02603/) (2021)
- [Few-shot Object Detection via Feature Reweighting](https://mlsanity.com/paper/1812.01866/) (2018)
- [Incremental Learning of Object Detectors without Catastrophic Forgetting](https://mlsanity.com/paper/1708.06977/) (2017)
- [Zero-Shot Object Detection](https://mlsanity.com/paper/1804.04340/) (2018)
- [OW-DETR: Open-world Detection Transformer](https://mlsanity.com/paper/2112.01513/) (2021)
- [Dropout Sampling for Robust Object Detection in Open-Set Conditions](https://mlsanity.com/paper/1710.06677/) (2017)
- [Learning Open-World Object Proposals without Learning to Classify](https://mlsanity.com/paper/2108.06753/) (2021)
- [Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation](https://mlsanity.com/paper/1503.00848/) (2015)
- [Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation](https://mlsanity.com/paper/2210.07506/) (2022)
- [Toward Transformer-Based Object Detection](https://mlsanity.com/paper/2012.09958/) (2020)
- [Learning to Segment Object Candidates](https://mlsanity.com/paper/1506.06204/) (2015)
- [Faster ILOD: Incremental Learning for Object Detectors based on Faster RCNN](https://mlsanity.com/paper/2003.03901/) (2020)
- [UC-OWOD: Unknown-Classified Open World Object Detection](https://mlsanity.com/paper/2207.11455/) (2022)
- [Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization](https://mlsanity.com/paper/1606.04446/) (2016)
- [Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection](https://mlsanity.com/paper/1709.04347/) (2017)

## Built on by (most cited first, 28 of 28)

- [Learning Vision-and-Language Navigation from YouTube Videos](https://mlsanity.com/paper/2307.11984/) (2023)
- [Open World Object Detection: A Survey](https://mlsanity.com/paper/2410.11301/) (2024)
- [Open World Object Detection in the Era of Foundation Models](https://mlsanity.com/paper/2312.05745/) (2023)
- [YOLO-UniOW: Efficient Universal Open-World Object Detection](https://mlsanity.com/paper/2412.20645/) (2024)
- [SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Driving](https://mlsanity.com/paper/2305.06940/) (2023)
- [3D Indoor Instance Segmentation in an Open-World](https://mlsanity.com/paper/2309.14338/) (2023)
- [Unsupervised Recognition of Unknown Objects for Open-World Object Detection](https://mlsanity.com/paper/2308.16527/) (2023)
- [From Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel Objects](https://mlsanity.com/paper/2411.18207/) (2024)
- [USD: Unknown Sensitive Detector Empowered by Decoupled Objectness and Segment Anything Model](https://mlsanity.com/paper/2306.02275/) (2023)
- [SKDF: A Simple Knowledge Distillation Framework for Distilling Open-Vocabulary Knowledge to Open-world Object Detector](https://mlsanity.com/paper/2312.08653/) (2023)
- [Looking Beyond the Known: Towards a Data Discovery Guided Open-World Object Detection](https://mlsanity.com/paper/2510.00303/) (2025)
- [Addressing the Challenges of Open-World Object Detection](https://mlsanity.com/paper/2303.14930/) (2023)
- [UADet: A Remarkably Simple Yet Effective Uncertainty-Aware Open-Set Object Detection Framework](https://mlsanity.com/paper/2412.09229/) (2024)
- [Detecting Unknown Objects via Energy-based Separation for Open World Object Detection](https://mlsanity.com/paper/2603.29954/) (2026)
- [O1O: Grouping of Known Classes to Identify Unknown Objects as Odd-One-Out](https://mlsanity.com/paper/2410.07514/) (2024)
- [Knowing the Unknown: Interpretable Open-World Object Detection via Concept Decomposition Model](https://mlsanity.com/paper/2602.20616/) (2026)
- [EW-DETR: Evolving World Object Detection via Incremental Low-Rank DEtection TRansformer](https://mlsanity.com/paper/2602.20985/) (2026)
- [REAL-OW: Rehearsal-free Open World Object Detection with Low-Rank Adaptation and Dual-Stage Objectness Modeling](https://mlsanity.com/paper/2607.03004/) (2026)
- [Open-CRB: Towards Open World Active Learning for 3D Object Detection](https://mlsanity.com/paper/2310.10391/) (2023)
- [OW-Rep: Open World Object Detection with Instance Representation Learning](https://mlsanity.com/paper/2409.16073/) (2024)
- [Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection](https://mlsanity.com/paper/2506.22161/) (2025)
- [Decoupled PROB: Decoupled Query Initialization Tasks and Objectness-Class Learning for Open World Object Detection](https://mlsanity.com/paper/2507.13085/) (2025)
- [Towards Open World Detection: A Survey](https://mlsanity.com/paper/2508.16527/) (2025)
- [Beyond Known Objects: A Novel Framework for Open-Set Object Detection using Negative-Aware Norm](https://mlsanity.com/paper/2605.02284/) (2026)
- [Towards Open World Sound Event Detection](https://mlsanity.com/paper/2605.03934/) (2026)
- [DualMem: Bypassing the Objectness Bottleneck for Calibrated Unknown-Stream Filtering in Open-World Object Detection](https://mlsanity.com/paper/2605.23634/) (2026)
- [Towards Sparsely Annotated Open-World Object Detection](https://mlsanity.com/paper/2608.12714/) (2026)
- [Hyperbolic Geometry for Open-World Object Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2609.09626/) (2026)

## Abstract (arXiv)

Open-world object detection (OWOD), as a more general and challenging goal, requires the model trained from data on known objects to detect both known and unknown objects and incrementally learn to identify these unknown objects. The existing works which employ standard detection framework and fixed pseudo-labelling mechanism (PLM) have the following problems: (i) The inclusion of detecting unknown objects substantially reduces the model's ability to detect known ones. (ii) The PLM does not adequately utilize the priori knowledge of inputs. (iii) The fixed selection manner of PLM cannot guarantee that the model is trained in the right direction. We observe that humans subconsciously prefer to focus on all foreground objects and then identify each one in detail, rather than localize and identify a single object simultaneously, for alleviating the confusion. This motivates us to propose a novel solution called CAT: LoCalization and IdentificAtion Cascade Detection Transformer which decouples the detection process via the shared decoder in the cascade decoding way. In the meanwhile, we propose the self-adaptive pseudo-labelling mechanism which combines the model-driven with input-driven PLM and self-adaptively generates robust pseudo-labels for unknown objects, significantly improving the ability of CAT to retrieve unknown objects. Comprehensive experiments on two benchmark datasets, i.e., MS-COCO and PASCAL VOC, show that our model outperforms the state-of-the-art in terms of all metrics in the task of OWOD, incremental object detection (IOD) and open-set detection.