Xiaohui Chen

Hao-Ran Yang, Xiaohui Chen, Chuan-Xian Ren

Aiming to generalize the well-trained gaze estimation model to new target domains, Cross-domain Gaze Estimation (CDGE) is developed for real-world application scenarios. Existing CDGE methods typically extract the domain-invariant features to mitigate domain shift in feature space, which is proved insufficient by Generalized Label Shift (GLS) theory. In this paper, we introduce a novel GLS perspective to CDGE and modelize the cross-domain problem by label and conditional shift problem. A GLS correction framework is presented and a feasible realization is proposed, in which a importance reweighting strategy based on truncated Gaussian distribution is introduced to overcome the continuity challenges in label shift correction. To embed the reweighted source distribution to conditional invariant learning, we further derive a probability-aware estimation of conditional operator discrepancy. Extensive experiments on standard CDGE tasks with different backbone models validate the superior generalization capability across domain and applicability on various models of proposed method.

Xiaohui Chen, Ramtin Hosseini, Karen Panetta et al.

Predicting future sensory states is crucial for learning agents such as robots, drones, and autonomous vehicles. In this paper, we couple multiple sensory modalities with exploratory actions and propose a predictive neural network architecture to address this problem. Most existing approaches rely on large, manually annotated datasets, or only use visual data as a single modality. In contrast, the unsupervised method presented here uses multi-modal perceptions for predicting future visual frames. As a result, the proposed model is more comprehensive and can better capture the spatio-temporal dynamics of the environment, leading to more accurate visual frame prediction. The other novelty of our framework is the use of sub-networks dedicated to anticipating future haptic, audio, and tactile signals. The framework was tested and validated with a dataset containing 4 sensory modalities (vision, haptic, audio, and tactile) on a humanoid robot performing 9 behaviors multiple times on a large set of objects. While the visual information is the dominant modality, utilizing the additional non-visual modalities improves the accuracy of predictions.