5 accepted papers
Abstract visual reasoning remains challenging as existing methods often prioritize either global context or local row-wise relations, failing to integrate both, and lack intermediate feature constraints, leading to incomplete rule capture and entangled representations. To address these issues, we pr…
Attention mechanisms have become a key module in modern vision backbones due to their ability to model long-range dependencies. However, their quadratic complexity in sequence length and the difficulty of interpreting attention weights limit both scalability and clarity. Recent attention-free archit…
Bounding box regression (BBR) is a critical component in object detection, significantly influencing the accuracy of object localization. However, existing Intersection over Union (IoU)-based loss functions encounter two primary challenges: (i) The penalty factor configuration often results in the e…
Fully comprehending scientific papers by machines reflects a high level of Artificial General Intelligence, requiring the ability to reason across fragmented and heterogeneous sources of information, presenting a complex and practically significant challenge. While Vision-Language Models (VLMs) have…
Deep learning has achieved great success in face recognition, however deep-learned features still have limited invariance to strong intra-personal variations such as large pose. It is observed that some facial attributes (e.g. eyebrow thickness, gender) are invariant to such variations. We present t…