Concerns with enabling computers to interpret and understand visual inputs, such as images and videos.
The article introduces DeepShaRM, a novel multi-view method for 3D reconstruction of textureless, non-Lambertian objects under unknown natural illumination. DeepShaRM achieves state-of-the-art accuracy by estimating reflectance and illumination as a…
Continue readingThe research article presents a new framework for unsupervised video anomaly detection (VAD) called Coarse-to-Fine Pseudo-Labeling (C2FPL). Traditional VAD methods rely heavily on manually annotated anomaly examples, which is a…
Continue readingThis article introduces EmoCLIP, a new vision-language model that enhances learning of rich latent representations for zero-shot classification. The model is tested using zero-shot classification on four popular dynamic FER…
Continue readingThis research explores the benefits of combining 3D scene information with blind audio recordings for novel-view acoustic synthesis. The main challenges identified are sound source localization, separation, and dereverberation. The…
Continue readingThe article discusses a proposed system for sound design that extracts repetitive actions from a video, which are used in conjunction with audio or textual embeddings to condition a diffusion…
Continue readingThe article investigates the basic language capabilities of pre-trained multimodal models, questioning their understanding of image-text interaction. It introduces the BLA Benchmark, a tool designed to evaluate these models based…
Continue readingThis research presents a new annotation strategy, Drag&Drop, for tumor detection and localization. This strategy simplifies the annotation process making it more efficient, particularly for temporal and volumetric imaging. The…
Continue readingThe study focuses on using deep learning to predict breast cancer subtypes and biomarker levels. Four biomarkers – Estrogen Receptor (ER), Progesterone Receptor (PR), HER2, and Ki67 – are used…
Continue readingThe article presents an extensive review of Federated Learning (FL) as applied to visual recognition, emphasizing the importance of thoughtful architectural design in achieving optimal performance. The authors argue that…
Continue readingThe authors of the article investigate ‘Eureka-moments’ in transformers during multi-step tasks, where transformers quickly improve after training and validation loss have stagnated for a long time. They found that…
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