Xiaoran Xu

Xiaoran Xu, Xiaoshan Yang, Jiangang Yang et al.

Open-Vocabulary Object Detection (OVOD) has achieved remarkable success in generalizing to novel categories. However, this success often rests on the implicit assumption of domain stationarity. In this work, we provide a principled revisit of the OVOD paradigm, uncovering a fundamental vulnerability: the fragile coupling between visual manifolds and textual embeddings when distribution shifts occur. We first systematically formalize Domain-Generalized Open-Vocabulary Object Detection (DG-OVOD). Through empirical analysis, we demonstrate that visual shifts do not merely add noise; they cause a collapse of the latent cross-modal space where novel category visual signals detach from their semantic anchors. Motivated by these insights, we propose Progressive Domain-invariant Cross-modal Alignment (PICA). PICA departs from uniform training by introducing a multi-level ambiguity and signal strength curriculum. It builds adaptive pseudo-word prototypes, refined via sample confidence and visual consistency, to enforce invariant cross-domain modality alignment. Our findings suggest that OVOD's robustness to domain shifts is intrinsically linked to the stability of the latent cross-modal alignment space. Our work provides both a challenging benchmark and a new perspective on building truly generalizable open-vocabulary systems that extend beyond static laboratory conditions.

Xiaoran Xu, Jiangang Yang, Wenyue Chong et al.

Single-Domain Generalized Object Detection~(S-DGOD) aims to train an object detector on a single source domain while generalizing well to diverse unseen target domains, making it suitable for multimedia applications that involve various domain shifts, such as intelligent video surveillance and VR/AR technologies. With the success of large-scale Vision-Language Models, recent S-DGOD approaches exploit pre-trained vision-language knowledge to guide invariant feature learning across visual domains. However, the utilized knowledge remains at a coarse-grained level~(e.g., the textual description of adverse weather paired with the image) and serves as an implicit regularization for guidance, struggling to learn accurate region- and object-level features in varying domains. In this work, we propose a new cross-modal feature learning method, which can capture generalized and discriminative regional features for S-DGOD tasks. The core of our method is the mechanism of Cross-modal and Region-aware Feature Interaction, which simultaneously learns both inter-modal and intra-modal regional invariance through dynamic interactions between fine-grained textual and visual features. Moreover, we design a simple but effective strategy called Cross-domain Proposal Refining and Mixing, which aligns the position of region proposals across multiple domains and diversifies them, enhancing the localization ability of detectors in unseen scenarios. Our method achieves new state-of-the-art results on S-DGOD benchmark datasets, with improvements of +8.8\%~mPC on Cityscapes-C and +7.9\%~mPC on DWD over baselines, demonstrating its efficacy.

Xiaoran Xu, Wei Feng, Yunsheng Jiang et al.

We propose Dynamically Pruned Message Passing Networks (DPMPN) for large-scale knowledge graph reasoning. In contrast to existing models, embedding-based or path-based, we learn an input-dependent subgraph to explicitly model reasoning process. Subgraphs are dynamically constructed and expanded by applying graphical attention mechanism conditioned on input queries. In this way, we not only construct graph-structured explanations but also enable message passing designed in Graph Neural Networks (GNNs) to scale with graph sizes. We take the inspiration from the consciousness prior proposed by and develop a two-GNN framework to simultaneously encode input-agnostic full graph representation and learn input-dependent local one coordinated by an attention module. Experiments demonstrate the reasoning capability of our model that is to provide clear graphical explanations as well as deliver accurate predictions, outperforming most state-of-the-art methods in knowledge base completion tasks.