This paper addresses low-light video super-resolution (LVSR), aiming to restore high-resolution videos from low-light, low-resolution (LR) inputs. Existing LVSR methods often struggle to recover fine details due to limited contrast and insufficient high-frequency information. To overcome these challenges, we present RetinexEVSR, the first event-driven LVSR framework that leverages high-contrast event signals and Retinex-inspired priors to enhance video quality under low-light scenarios. Unlike previous approaches that directly fuse degraded signals, RetinexEVSR introduces a novel bidirectional cross-modal fusion strategy to extract and integrate meaningful cues from noisy event data and degraded RGB frames. Specifically, an illumination-guided event enhancement module is designed to progressively refine event features using illumination maps derived from the Retinex model, thereby suppressing low-light artifacts while preserving high-contrast details. Furthermore, we propose an event-guided reflectance enhancement module that utilizes the enhanced event features to dynamically recover reflectance details via a multi-scale fusion mechanism. Experimental results show that our RetinexEVSR achieves state-of-the-art performance on three datasets. Notably, on the SDSD benchmark, our method can get up to 2.95 dB gain while reducing runtime by 65% compared to prior event-based methods.

Tiny object detection plays a vital role in drone surveillance, remote sensing, and autonomous systems, enabling the identification of small targets across vast landscapes. However, existing methods suffer from inefficient feature leverage and high computational costs due to redundant feature processing and rigid query allocation. To address these challenges, we propose Dome-DETR, a novel framework with Density-Oriented Feature-Query Manipulation for Efficient Tiny Object Detection. To reduce feature redundancies, we introduce a lightweight Density-Focal Extractor (DeFE) to produce clustered compact foreground masks. Leveraging these masks, we incorporate Masked Window Attention Sparsification (MWAS) to focus computational resources on the most informative regions via sparse attention. Besides, we propose Progressive Adaptive Query Initialization (PAQI), which adaptively modulates query density across spatial areas for better query allocation. Extensive experiments demonstrate that Dome-DETR achieves state-of-the-art performance (+3.3 AP on AI-TOD-V2 and +2.5 AP on VisDrone) while maintaining low computational complexity and a compact model size. Code is available at https://github.com/RicePasteM/Dome-DETR.

The rapid evolution of vision-language models (VLMs), such as CLIP, has demonstrated remarkable zero-shot capabilities in downstream tasks. Prompt learning paradigms like Context Optimization (CoOp) refine learnable prompts for efficient adaptation. However, their application in the biomedical domain remains limited due to the insufficient utilization of specialized biomedical knowledge and cross-modality structural relationships. To address these, we introduce MeDKCoOp, a Medical Dual Knowledge-guided graph adaptation method that leverages systematic integration of knowledge through three aspects: exploit domain-specific knowledge from both textual and visual branches, formalize it into graph-structured representations, and leverage knowledge-guided relation transfer for learning cross-modality fusion. By dynamically optimizing learnable prompts through relation learning process, our method achieves disentangled visual representation and enhances transferability to downstream tasks. Evaluations across 8 biomedical datasets spanning 7 imaging modalities demonstrate state-of-the-art cross-domain generalization, with an average 15.12% accuracy improvement over baselines. Our work establishes a new paradigm through graph prompt learning in medical vision-language models, advancing robust diagnostic AI in data-scarce clinical scenarios. Our code is available at: https://github.com/WangYijun-OUC/MeDKCoOp.