7 accepted papers
Cross-modal alignment is a crucial task in multimodal learning aimed at achieving semantic consistency between vision and language. This requires that image-text pairs exhibit similar semantics. Traditional algorithms pursue embedding consistency to achieve semantic consistency, ignoring the non-sem
Time series forecasting is crucial for applications in various domains. Conventional methods often rely on global decomposition into trend, seasonal, and residual components, which become ineffective for real-world series dominated by local, complex, and highly dynamic patterns. Moreover, the high m
Deep learning has achieved significant advancements in medical image segmentation, but existing models still face challenges in accurately segmenting lesion regions. The main reason is that some lesion regions in medical images have unclear boundaries, irregular shapes, and small tissue density diff…
Time series forecasting is a crucial task in various domains. Caused by factors such as trends, seasonality, or irregular fluctuations, time series often exhibits non-stationary. It obstructs stable feature propagation through deep layers, disrupts feature distributions, and complicates learning dat…
Total variation (TV) and non-local patch similarity have been used successfully to enhance the performance of compressive sensing (CS) approaches. However, such techniques can often remove important details in the image or introduce reconstruction artifacts. This paper presents a novel CS method, wh…
We present a new approach for constructing the 3D panorama for an indoor environment by joining together aligned submaps. For each submap, the trajectory of the moving camera is estimated based on the Kanade-Lucas-Tomasi (KLT) features. Our method can update the feature set status by adding new feat…
This paper proposes a novel super-resolution method that exploits the sparse representation and non-local similarity of patches for the effective reconstruction of images. Highresolution images are reconstructed from low resolution observations with an efficient technique based on the alternating di…