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Ronghua Luo

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

OW-DAR: Dual-Granularity Adaptive Reconstruction-Error Modeling for Open-World Object Detection

Linhua Ye
Xing Xi
RongHua Luo

Open-world object detection (OWOD) aims to detect known and unknown objects in dynamic environments. However, only known classes are labeled during training, making it challenging for detectors to recognize unknown objects during inference. Existing methods typically rely on supervision from known categories, leading models to overconfidently misclassify visually similar unknowns as known, and dissimilar ones as background. This known-class prior bias limits the model’s ability to detect unknown objects. In this paper, we propose a novel method, OW-DAR, which enhances foreground-background separability through collaborative fine-grained and coarse-grained modeling. At the fine-grained level, we propose Fine-grained Masked Reconstruction (FMR), which randomly masks regions of the feature map to guide the reconstruction toward semantic structures, rather than memorizing low-level patterns. At the coarse-grained level, we propose Adaptive Region-based Error Aggregation (AREA), which operates on object proposals to aggregate reconstruction errors. This enables the model to attend to semantically ambiguous foreground-background boundaries while suppressing the influence of local outliers during optimization. Finally, we leverage robust reconstruction errors to perform unsupervised foreground-background modeling, enabling probabilistic estimation for potential unknown objects. We validate the effectiveness of OW-DAR on standard OWOD benchmark. Experimental results demonstrate that OW-DAR consistently outperforms existing state-of-the-art methods, achieving a +18.8 improvement in unknown object recall (U-Recall).

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ICML Conference 2025 Conference Paper

OW-VAP: Visual Attribute Parsing for Open World Object Detection

Xing Xi
Xing Fu
Weiqiang Wang 0002
Ronghua Luo

Open World Object Detection (OWOD) requires the detector to continuously identify and learn new categories. Existing methods rely on the large language model (LLM) to describe the visual attributes of known categories and use these attributes to mark potential objects. The performance of such methods is influenced by the accuracy of LLM descriptions, and selecting appropriate attributes during incremental learning remains a challenge. In this paper, we propose a novel OWOD framework, termed OW-VAP, which operates independently of LLM and requires only minimal object descriptions to detect unknown objects. Specifically, we propose a Visual Attribute Parser (VAP) that parses the attributes of visual regions and assesses object potential based on the similarity between these attributes and the object descriptions. To enable the VAP to recognize objects in unlabeled areas, we exploit potential objects within background regions. Finally, we propose Probabilistic Soft Label Assignment (PSLA) to prevent optimization conflicts from misidentifying background as foreground. Comparative results on the OWOD benchmark demonstrate that our approach surpasses existing state-of-the-art methods with a +13 improvement in U-Recall and a +8 increase in U-AP for unknown detection capabilities. Furthermore, OW-VAP approaches the unknown recall upper limit of the detector.

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IJCAI Conference 2024 Conference Paper

KTCN: Enhancing Open-World Object Detection with Knowledge Transfer and Class-Awareness Neutralization

Xing Xi
Yangyang Huang
Jinhao Lin
RongHua Luo

Open-World Object Detection (OWOD) has garnered widespread attention due to its ability to recall unannotated objects. Existing works generate pseudo-labels for the model using heuristic priors, which limits the model’s performance. In this paper, we leverage the knowledge of the large-scale visual model to provide supervision for unknown categories. Specifically, we use the Segment Anything Model (SAM) to generate raw pseudo-labels for potential objects and refine them through Intersection over Union (IOU) and the shortest bounding box side length. Nevertheless, the abundance of pseudo-labels still exacerbates the competition issue in the one-to-many label assignment. To address this, we propose the Dual Matching Label Assignment (DMLA) strategy. Furthermore, we propose the Class-Awareness Neutralizer (CAN) to reduce the model’s bias towards known categories. Evaluation results on open-world object detection benchmarks, including MS COCO and Pascal VOC, show that our method achieves nearly 200% the unknown recall rate of previous state-of-the-art (SOTA) methods, reaching 41. 5 U-Recall. Additionally, our approach does not add any extra parameters, maintaining the inference speed advantage of Faster R-CNN, leading the SOTA methods based on deformable DETR at a speed of over 10 FPS. Our code is available at https: //github. com/xxyzll/KTCN.

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AAAI Conference 2024 Conference Paper

M2SD:Multiple Mixing Self-Distillation for Few-Shot Class-Incremental Learning

Jinhao Lin
Ziheng Wu
Weifeng Lin
Jun Huang
RongHua Luo

Few-shot Class-incremental learning (FSCIL) is a challenging task in machine learning that aims to recognize new classes from a limited number of instances while preserving the ability to classify previously learned classes without retraining the entire model. This presents challenges in updating the model with new classes using limited training data, particularly in balancing acquiring new knowledge while retaining the old. We propose a novel method named Multiple Mxing Self-Distillation (M2SD) during the training phase to address these issues. Specifically, we propose a dual-branch structure that facilitates the expansion of the entire feature space to accommodate new classes. Furthermore, we introduce a feature enhancement component that can pass additional enhanced information back to the base network by self-distillation, resulting in improved classification performance upon adding new classes. After training, we discard both structures, leaving only the primary network to classify new class instances. Extensive experiments demonstrate that our approach achieves superior performance over previous state-of-the-art methods.

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NeurIPS Conference 2024 Conference Paper

UMB: Understanding Model Behavior for Open-World Object Detection

Xing Xi
Yangyang Huang
Zhijie Zhong
RongHua Luo

Open-World Object Detection (OWOD) is a challenging task that requires the detector to identify unlabeled objects and continuously demands the detector to learn new knowledge based on existing ones. Existing methods primarily focus on recalling unknown objects, neglecting to explore the reasons behind them. This paper aims to understand the model's behavior in predicting the unknown category. First, we model the text attribute and the positive sample probability, obtaining their empirical probability, which can be seen as the detector's estimation of the likelihood of the target with certain known attributes being predicted as the foreground. Then, we jointly decide whether the current object should be categorized in the unknown category based on the empirical, the in-distribution, and the out-of-distribution probability. Finally, based on the decision-making process, we can infer the similarity of an unknown object to known classes and identify the attribute with the most significant impact on the decision-making process. This additional information can help us understand the behavior of the model's prediction in the unknown class. The evaluation results on the Real-World Object Detection (RWD) benchmark, which consists of five real-world application datasets, show that we surpassed the previous state-of-the-art (SOTA) with an absolute gain of 5. 3 mAP for unknown classes, reaching 20. 5 mAP. Our code is available at https: //github. com/xxyzll/UMB.

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ECAI Conference 2023 Conference Paper

Data-Free Class-Incremental Learning with Implicit Representation of Prototypes

Tianwen Yang
Leixiong Huang
Ronghua Luo

Class-incremental learning (CIL) has attracted much attention in deep learning due to the challenge problem of catastrophic forgetting. Various methods have been proposed for CIL, including exemplar-based class-incremental learning (EBCIL), non-exemplar class-incremental learning (NECIL) and data-free class-incremental learning (DFCIL). Without storing any information (such as examples and prototypes) about the old classes, DFCIL is obviously the most challenging one. To address the problem of lacking information in DFCIL and with the assumption that the learned representations are not linearly separable, we propose a method called IRP. We use the L2-similarity classifier instead of the FC classifier, where each weight vector represents a prototype that implicitly records information about the classes. We use representation-prototype distance minimization (RPDM) to solve the problem of loose representation caused by overfitting. To alleviate the excessive deviation of old prototypes under long-term CIL, we add prototype changing limitation (PCL) and prototype momentum updating (PMU) in incremental stages. In addition, we design a method for resampling around old prototypes (RAOP) to maintain the decision boundary of the old classes. Numerous experiments on three benchmarks have shown that IRP is significantly superior to other DFCIL methods and performs comparably to NECIL and partial EBCIL methods.

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ICRA Conference 2011 Conference Paper

Simultaneous place and object recognition with mobile robot using pose encoded contextual information

Ronghua Luo
Songhao Piao
Huaqing Min

Place and object recognition are two fundamental problems for mobile robot to understand its surroundings. In the field of computer vision it has been acknowledged that context plays an important role in image parsing, but in most of the researches contextual information is only used in one direction and little attention is paid to the relative pose context between objects and local features. We observe, however, place and object can serve as context to each other, that is the recognition of one facilitates the recognition of the other. In this paper, a new hierarchical random field which can encode multiple kinds of context including co-occurrence context, temporal context and relative pose context is proposed for simultaneous place and object recognition with a mobile platform. And a new kind of relative pose context, which is scale and rotation invariant, is defined to improve the stability of pose-encoded context. Experimental results with a mobile robot prove that the proposed method significantly improve the precision of the place and object recognition in familiar and unfamiliar environments.

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