Concerns with enabling computers to interpret and understand visual inputs, such as images and videos.
The paper presents ProLab, a unique approach that uses property-level label space to generate robust and interpretable segmentation models. Rather than solely relying on category-specific annotations, ProLab uses descriptive properties…
Continue readingThe paper addresses the issue of unfaithfulness in the explanations provided by visual question answering (VQA) systems. Current post-hoc methods, which provide natural language explanations (NLE), often fail to align…
Continue readingThe article introduces ProvFL, a mechanism that aids in interpreting and explaining global model predictions in Federated Learning (FL). It’s designed to track the information flow between individual clients in…
Continue readingDiffusion Reward is a novel framework that learns rewards from expert videos for reinforcement learning tasks. The system leverages conditional video diffusion models to solve complex visual reinforcement learning problems….
Continue readingThe article discusses the challenges in collecting cross-age facial images, which limits the versatility of supervised methods for age-invariant face recognition, a critical task in applications such as security and…
Continue readingThe article presents a study on the issue of model collapse in peer collaborative learning (PCL). The authors analyze the high homogenization between the student and teacher as the cause…
Continue readingThis paper presents the Global Feature Pyramid Network (GFPNet), an improved version of PAFPN that uses global data to enhance target detection. The GFPNet addresses the issue of increased false…
Continue readingThis study introduces a dataset of 140 surgical videos (MultiBypass140) of laparoscopic Roux-en-Y gastric bypass surgeries from two medical centers. The dataset has been fully annotated with phases and steps…
Continue readingThe article introduces a new framework that leverages the normalization layer in adapters with Progressive Learning and Adaptive Distillation (ProLAD) for Cross-Domain Few-Shot Learning (CD-FSL). The framework uses two separate…
Continue readingThe article introduces a novel coarse-to-fine approach for 3D instance segmentation, named Spherical Mask. It is based on spherical representation, which overcomes limitations of existing methods, such as instance size…
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