Tomato is one of the most important economic crops in the world, but it is prone to diseases during the growth process, so the detection of tomato diseases is very important. However, when detecting tomato diseases in natural environments, existing models are easily affected by environmental factors such as occlusion and illumination, as well as the small size of lesions. In response to these challenges, this paper proposes a tomato leaf disease detection framework LDW-DETR based on multi-scale fusion. First, the local-global feature fusion (LGFF) module is designed by referring to the idea of the PPA module, which can effectively capture local and global features, thereby enhancing the detection ability of small lesions in complex backgrounds. Second, the CSPDarknet architecture is introduced as the backbone network of LDW-DETR to improve the efficiency of feature extraction. In addition, the bottleneck layer of the C2f component is improved by integrating Strip Block and Contextualized Gated Linear Unit (CGLU) to enhance the perception ability of lesion edges and textures. Finally, the WIoU v3 loss function is used to optimize the bounding box regression process. The experimental results show that compared with RT-DETR, the LDW-DETR model improves mAP@0.5 and mAP@0.5–0.95 by 2.6% and 3.7%, respectively, while the number of parameters is reduced by 17.9%. In addition, it still maintains high robustness and generalization ability in cross-dataset experiments. These results show that LDW-DETR has good detection performance and generalization ability in the tomato leaf disease detection task.

Head pose estimation is a fundamental task in facial analysis and behavior understanding. To address the limitations of 6DRepNet in single-RGB scenarios, particularly in high-level spatial discriminative region modeling, global feature-to-6D rotation representation mapping, and the optimization of large-pose challenging samples, this paper proposes a geometry-aware enhanced framework for head pose estimation. While preserving the 6D continuous rotation representation and its SO(3)-based geometric supervision mechanism, the proposed method improves the baseline model through the joint design of a Spatial Recalibration Module, a Residual Pose Mapping Head, and a Pose-Aware Weighted Geodesic Loss. Experiments are conducted using 300W-LP for training and AFLW2000 and BIWI for evaluation. The results show that the proposed method consistently outperforms the baseline 6DRepNet on both datasets, reducing the overall MAE from 3.97 to 3.72 on AFLW2000 and from 3.54 to 3.26 on BIWI. Ablation studies further verify the effectiveness and complementarity of the proposed components. These results demonstrate that the proposed method can effectively improve the accuracy and robustness of single-RGB head pose estimation without introducing additional modalities.

Multimodal sentiment analysis must combine textual, visual, and acoustic evidence whose reliability varies across utterances. We propose PGMCR, which uses a predicted three-way polarity distribution to compose sample-level modality-reliability weights and applies a training-only consistency constraint to text-preserving visual and acoustic perturbations. Experiments on CMU-MOSI and CMU-MOSEI show that polarity-guided fusion is the principal architectural contribution, while the text-guided refinement and consistency branch have metric-dependent effects. The learned weights vary across polarity groups, but proxy-corruption diagnostics also reveal strong dependence on textual evidence and non-negligible polarity-estimation errors. The results support polarity as a useful conditioning signal for multimodal fusion while delimiting the scope of the nonverbal robustness claim.