Recent advances in 3D scene representation, particularly 3D Gaussian Splatting (3DGS), have demonstrated remarkable photorealistic rendering capabilities. However, the heavy reliance on dense and precisely calibrated camera configurations limits effectiveness in sparse view and unposed scenarios. In this paper, we present Tri-consistency 3D Gaussian Splatting (dubbed TriGS), a novel framework that jointly optimizes 3DGS parameters and camera poses only from sparse and unposed images via triple consistency supervisions coupled with the adaptive regularization strategy. We first estimate coarse camera poses by exploiting 3DGS's anisotropic properties through iterative relative pose optimization. Building upon this foundation, we introduce cross-view consistency enforcement through synchronized photometric color, geometric structure, and deep feature, effectively resolving rendering ambiguities with auxiliary supervisions. A unified rendering paradigm is also proposed to jointly refine Gaussian primitives and camera poses by transforming positions, covariances, and spherical harmonics. To combat overfitting inherent in joint optimization, we devise an adaptive regularization mechanism that strategically samples hard viewpoints based on baseline distances and training dynamics, enforcing projection consistency through deep feature priors. Extensive experiments on multiple challenging real-world datasets validate the effectiveness of TriGS, which achieves satisfactory results to set a new state-of-the-art without the reliance on external pose priors only under sparse and unposed view inputs.

Audio-visual Generalized Zero-Shot Learning ((G)ZSL) has attracted significant attention for its ability to identify unseen classes in general video classification tasks. However, modality imbalance in (G)ZSL leads to over-reliance on the optimal modality, reducing discriminative capabilities for unseen classes. Though recent studies have attempted to address this issue, two challenges still remain unsolved: (a) Quality discrepancies, where modalities offer differing quantities and qualities of information for the same concept. (b) Content discrepancies, where the contributions of different samples within the same modality exhibit significant differences. To address these challenges, we propose a Discrepancy-Aware Attention Network (DAAN) for Enhanced Audio-Visual (G)ZSL. Our approach introduces a Redundant-Noise Mitigation Attention (RNMA) unit to minimize content discrepancies by mitigating redundant information in modalities and a Contrastive Sample Gradient Modulation (CSGM) mechanism to adjust gradient magnitudes and balance quality discrepancies. We quantify modality contributions by integrating optimization and convergence rate for more precise gradient modulation in CSGM. Experiments demonstrate DAAN achieves state-of-the-art performance on benchmark datasets, with ablation studies validating the effectiveness of individual modules. Code is available at https://github.com/xiaoxinning/DAAN-GZSL.