Source-Free Object Detection (SFOD) aims to adapt a source-pretrained object detector to a target domain without access to source data. However, existing SFOD methods predominantly rely on internal knowledge from the source model, which limits their capacity to generalize across domains and often results in biased pseudo-labels, thereby hindering both transferability and discriminability. In contrast, Vision Foundation Models (VFMs), pretrained on massive and diverse data, exhibit strong perception capabilities and broad generalization, yet their potential remains largely untapped in the SFOD setting. In this paper, we propose a novel SFOD framework that leverages VFMs as external knowledge sources to jointly enhance feature alignment and label quality. Specifically, we design three VFM-based modules: (1) Patch-weighted Global Feature Alignment (PGFA) distills global features from VFMs using patch-similarity–based weighting to enhance global feature transferability; (2) Prototype-based Instance Feature Alignment (PIFA) performs instance-level contrastive learning guided by momentum-updated VFM prototypes; and (3) Dual-source Enhanced Pseudo-label Fusion (DEPF) fuses predictions from detection VFMs and teacher models via an entropy-aware strategy to yield more reliable supervision. Extensive experiments on six benchmarks demonstrate that our method achieves state-of-the-art SFOD performance, validating the effectiveness of integrating VFMs to simultaneously improve transferability and discriminability.

Graph Neural Networks (GNNs) have shown efficacy in graph node classification, but face computational challenges on large-scale graphs. Although existing graph reduction methods address these issues, they still require high computational resources and fail to prioritize robust performance on out-of-distribution data. To tackle these challenges, we introduce the subgraph invariant learning paradigm, inspired by the small-world phenomenon. This approach enables models trained on specific subgraphs to generalize across diverse subgraphs, reducing computational demands, and enhancing scalability. To promote generalization, we maximize the invariance log-likelihood by deriving a theoretical lower bound of it and formulating the InVar loss. This loss minimizes the discrepancy between node representations and their corresponding invariance representations while maximizing the entropy of the node representation. In response to InVar loss, we propose the Invariance Facilitation Model (IFM), comprising the Invariance Representation Encoder (IRE) and Node Representation Encoder (NRE). IRE, capturing the invariance representations, utilizes Invariance ATTention (InvarATT) to compress long-range dependencies, while NRE learns the node representation, by integrating invariance representations via Telematic ATTention (TeleATT) and exchanging local information within each subgraph through GNNs. Evaluations on four large-scale graph datasets demonstrate the effectiveness, computational efficiency, and interpretability of IFM for large-scale graph node classification.

We introduce See&Trek, the first training-free prompting framework tailored to enhance the spatial understanding of Multimodal Large Language Models (MLLMs) under vision-only constraints. While prior efforts have incorporated modalities like depth or point clouds to improve spatial reasoning, purely visual-spatial understanding remains underexplored. See&Trek addresses this gap by focusing on two core principles: increasing visual diversity and motion reconstruction. For visual diversity, we conduct Maximum Semantic Richness Sampling, which employs an off-the-shell perception model to extract semantically rich keyframes that capture scene structure. For motion reconstruction, we simulate visual trajectories and encode relative spatial positions into keyframes to preserve both spatial relations and temporal coherence. Our method is training&GPU-free, requiring only a single forward pass, and can be seamlessly integrated into existing MLLMs. Extensive experiments on the VSI-Bench and STI-Bench show that See&Trek consistently boosts various MLLMs performance across diverse spatial reasoning tasks with the most +3.5% improvement, offering a promising path toward stronger spatial intelligence.

Deep learning techniques have driven significant progress in various analytical tasks within 3D genomics in computational biology. However, a holistic understanding of 3D genomics knowledge remains underexplored. Here, we propose ***MIX-HIC***, the first multimodal foundation model of 3D genome that integrates both 3D genome structure and epigenomic tracks, which obtains unified and comprehensive semantics. For accurate heterogeneous semantic fusion, we design the cross-modal interaction and mapping blocks for robust unified representation, yielding the accurate aggregation of 3D genome knowledge. Besides, we introduce the first large-scale dataset comprising over ***1 million*** pairwise samples of Hi-C contact maps and epigenomic tracks for high-quality pre-training, enabling the exploration of functional implications in 3D genomics. Extensive experiments show that MIX-HIC significantly surpasses existing state-of-the-art methods in diverse downstream tasks. This work provides a valuable resource for advancing 3D genomics research.

We address the problem of (a) predicting the trajectory of an arm reaching motion, based on a few seconds of the motion’s onset, and (b) leveraging this predictor to facilitate shared-control manipulation tasks, by reducing the operator’s cognitive load through assistance in their anticipated direction of motion. Our novel intent estimator, dubbed the Robot Trajectron (RT), produces a probabilistic representation of the robot’s anticipated trajectory based on its recent position, velocity and acceleration history. By taking arm dynamics into account, RT can capture the operator’s intent better than other SOTA models that only use the arm’s position, making it particularly well-suited to assist in tasks where the operator’s intent is susceptible to change. We derive a novel shared-control solution that combines RT’s predictive capacity to a representation of the locations of potential reaching targets. Our experiments demonstrate RT’s effectiveness in both intent estimation and shared-control tasks. We will make the code and data supporting our experiments publicly available at https://gitlab.kuleuven.be/detry-lab/public/robot-trajectron

In 3D object manipulation, collecting expert data for end-to-end imitation learning becomes a mainstream method. Though successful, previous works neglect the guiding role of language in action execution. These methods lack the understanding of action semantics, in which multiple action sequences are guided by a category of instructions, resulting in overlearned object semantics and vague action semantics. To address the above limitation, we introduce a novel framework named LLaKey, which breaks down skill commands into more detailed action instructions based on key states for fine-grained action control. Specifically, LLaKey first leverages the knowledge encoded in pre-trained large-scale models to fine-tune an action instruction conductor. Then, these instructions are executed by a downstream action model. Comprehensive experiments show that LLaKey significantly surpasses baselines with a relative improvement of 15% in nine complex and varied skill tasks, demonstrating the superiority of our method.

Accurate perception of semantic and spatial information is crucial for robots performing language-driven navigation tasks. Existing approaches utilize visual-language models to extract semantic information from the environment and construct maps. However, constrained by the generalization and accuracy of these models themselves, the constructed maps may not be accurate and comprehensive, thereby affecting the accuracy of navigation tasks. Inspired by foundational models’ outstanding classification and segmentation capabilities, this study introduces a semantic map constructed using foundational models. We leverage a foundational model to semantically segment objects in the robot’s video stream and fuse semantics onto the map. Furthermore, this map is used in conjunction with large language models (LLMs) that receive natural language instructions to complete the navigation task. A substantial number of experiments in a simulated environment demonstrate that our method outperforms existing ones in language-driven navigation tasks.

Generic object detection methods have achieved preferable results, but it is still challenging to detect objects from complicated traffic scenes like extreme illumination and adverse weather. The existing methods are not robust enough to be extended to new complex traffic scenes. To address this issue, we leverage the idea of ensemble learning for strong robustness and propose a novel Bagging R-CNN framework. Specially, we design a bagging classification branch that uses adaptive sampling to train base learners and make them different from each other. The final predictions are the ensemble of the base learners, achieving strong robustness to the challenging objects. For localizing more accurately, a progressive regression branch is proposed in which bounding boxes are continuously optimized for high quality. Extensive experiment results on TJU-DHD-traffic and Pascal VOC datasets show that our Bagging R-CNN achieves superior detection accuracy over state-of-the-art methods. The source code can be found at https://github.com/PungTeng/BaggingRCNN.

Domain Adaptive Object Detection (DAOD) transfers an object detector from a labeled source domain to a novel unlabeled target domain. Recent works bridge the domain gap by aligning cross-domain pixel-pairs in the non-euclidean graphical space and minimizing the domain discrepancy for adapting semantic distribution. Though great successes, these methods model graphs roughly with coarse semantic sampling due to ignoring the non-informative noises and failing to concentrate on precise semantics alignment. Besides, the coarse graph generation inevitably contains abnormal nodes. These challenges result in biased domain adaptation. Therefore, we propose an Improved Graph Generation (IGG) framework which conducts high-quality graph generation for DAOD. Specifically, we design an Intensive Node Refinement (INR) module that reconstructs the noisy sampled nodes with a memory bank, and contrastively regularizes the noisy features. For better semantics alignment, we decouple the domain-specific style and category-invariant content encoded in graph covariance and selectively eliminate only the domain-specific style. Then, a Precision Graph Optimization (PGO) adaptor is proposed which utilizes the variational inference to down-weight abnormal nodes. Comprehensive experiments on three adaptation benchmarks demonstrate that IGG achieves state-of-the-art results in unsupervised domain adaptation.