Minghao Lai

Runhao Zeng, Jiaqi Mao, Minghao Lai et al.

Video grounding (VG) task focuses on locating specific moments in a video based on a query, usually in text form. However, traditional VG struggles with some scenarios like streaming video or queries using visual cues. To fill this gap, we present a new task named Online Video Grounding with Hybrid-modal Queries (OVG-HQ), which enables online segment localization using text, images, video segments, and their combinations. This task poses two new challenges: limited context in online settings and modality imbalance during training, where dominant modalities overshadow weaker ones. To address these, we propose OVG-HQ-Unify, a unified framework featuring a Parametric Memory Block (PMB) that retain previously learned knowledge to enhance current decision and a cross-modal distillation strategy that guides the learning of non-dominant modalities. This design enables a single model to effectively handle hybrid-modal queries. Due to the lack of suitable datasets, we construct QVHighlights-Unify, an expanded dataset with multi-modal queries. Besides, since offline metrics overlook prediction timeliness, we adapt them to the online setting, introducing oR@n, IoU=m, and online mean Average Precision (omAP) to evaluate both accuracy and efficiency. Experiments show that our OVG-HQ-Unify outperforms existing models, offering a robust solution for online, hybrid-modal video grounding. Source code and datasets are available at https://github.com/maojiaqi2324/OVG-HQ.

Qiwei Liang, Boyang Cai, Minghao Lai et al.

Despite strong results on recognition and segmentation, current 3D visual pre-training methods often underperform on robotic manipulation. We attribute this gap to two factors: the lack of state-action-state dynamics modeling and the unnecessary redundancy of explicit geometric reconstruction. We introduce AFRO, a self-supervised framework that learns dynamics-aware 3D representations without action or reconstruction supervision. AFRO casts state prediction as a generative diffusion process and jointly models forward and inverse dynamics in a shared latent space to capture causal transition structure. To prevent feature leakage in action learning, we employ feature differencing and inverse-consistency supervision, improving the quality and stability of visual features. When combined with Diffusion Policy, AFRO substantially increases manipulation success rates across 16 simulated and 4 real-world tasks, outperforming existing pre-training approaches. The framework also scales favorably with data volume and task complexity. Qualitative visualizations indicate that AFRO learns semantically rich, discriminative features, offering an effective pre-training solution for 3D representation learning in robotics. Project page: https://kolakivy.github.io/AFRO/