7 accepted papers
Over-aligning image generation models to a generalized aesthetic preference conflicts with user intent, particularly when "anti-aesthetic" outputs are requested for artistic or critical purposes. This adherence prioritizes developer-centered values, compromising user autonomy and aesthetic pluralism…
In remote sensing (RS) cross-modal retrieval, most existing methods employ contrastive learning as their primary optimization objective, aligning anchors with positive counterparts and distinguishing them from negative samples. To improve negative sampling, these approaches typically set thresholds
We introduce Value Sign Flip (VSF), a simple and efficient method for incorporating negative prompt guidance in few-step (1-8 steps) diffusion and flow-matching image generation models. Unlike existing approaches such as classifier-free guidance (CFG), NASA, and NAG, VSF dynamically suppresses undes…
In recent years, many fusion algorithms based on multi-scale transform or neural networks have been proposed to improve medical image fusion (MIF) performance. However, there is still enormous potential to explore the combination of different fusion theories. In this paper, we propose a novel MIF fr…
Marine mammals and their ecosystem face significant threats from, for example, military active sonar and marine transportation. To mitigate this harm, early detection and classification of marine mammals are essential. While recent efforts have utilized spectrogram analysis and machine learning tech…
Depth estimation attracts great interest in various sectors such as robotics, human computer interfaces, intelligent visual surveillance, and wearable augmented reality gear. Monocular depth estimation is of particular interest due to its low complexity and cost. Research in recent years was shifted…
Depth estimation is of growing interest in many sectors, from robotics to wearable augmented reality gears. Monocular depth estimation attracts more attention due to its cost efficiency and low complexity. Most recent research has developed very large and resource intensive networks which are not su…