Fine-grained agricultural pest image-text retrieval demands robust cross-modal matching capable of handling subtle appearance differences and structured multi-sentence descriptions. Yet, small-scale domain data introduces two key challenges in supervision and representation. First, existing corpora typically pair each image with a single coarse paragraph, providing incomplete supervision that omits life-stage appearances and subtle morphological traits, while high-frequency redundant fields shared across pest categories further cause semantic confounding. Second, prevailing retrieval models rely on global pooled alignment, which averages out low-occupancy but discriminative local cues such as stripe layouts, body segmentation, and wing venation, entangling near-neighbor categories. To address these issues, we propose StruMorAlign, a parameter-efficient cross-attention CLIP framework for fine-grained pest image-text retrieval. We first construct GjPest4CMR, an English goji pest image-text retrieval dataset created via LLMs-assisted re-writing with expert-in-the-loop verification, providing multi-sentence structured descriptions. On the modeling side, we design StruPD (Structured Semantic Deconfounding) to provide multi-granularity structured supervision and strengthen coarse-to-fine consistency learning on morphology-relevant local regions, addressing the first challenge; and introduce GateA2 (Gated Adaptive Alignment) self-distillation with high-momentum EMA teacher for parameter-efficient, morphology-aware cross-modal alignment, addressing the second challenge. On the GjPest4CMR dataset, StruMorAlign achieves mR 78.72% for both image-to-text and text-to-image retrieval, with I2T R@1=53.11% and T2I R@1=51.53%, outperforming the strongest baseline HarMA by +9.72 mR. Ablation studies confirm the complementary benefits of StruPD and GateA2, and qualitative analyses including t-SNE visualizations and attention heatmaps further validate the effectiveness of our approach.

Restoring and colorizing old films is a comprehensive video enhancement task, marked by the presence of heterogeneous and structured degradations. Our DeepEnhancer addresses this challenge through a unified pipeline that combines restoration and colorization. In this workflow, we implement a bidirectional propagation strategy. Specifically, we incorporate second-order feature alignment to reduce the accumulation of inaccuracies in optical flow estimation. Simultaneously, we utilize cross-scale long-term attention mechanisms to model correlations within hidden states, thereby ensuring spatial and temporal consistency. To address the notable content loss in aging films, we introduce a temporally consistent focal transformer guided by global information. This transformer utilizes various window levels with distinct sub-window sizes to seamlessly integrate fine-grained and coarse-grained features. Comprehensive experimental results conclusively demonstrate the superiority of our model in both quantitative and qualitative comparisons when compared to existing approaches.