Computer Vision
Computer vision, a field focused on enabling computers to "see" and interpret images and videos, aims to develop algorithms that can perform tasks such as object detection, image classification, and scene understanding. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and vision transformers (ViTs), often combined with techniques like multi-modal fusion (integrating data from different sensors) and transfer learning to improve efficiency and accuracy. These advancements are driving significant progress in diverse applications, including precision agriculture, robotics, medical imaging analysis, and autonomous systems, by providing automated, efficient, and objective solutions to complex visual tasks.
Papers
CHOSEN: Compilation to Hardware Optimization Stack for Efficient Vision Transformer Inference
Mohammad Erfan Sadeghi, Arash Fayyazi, Suhas Somashekar, Massoud Pedram
CerberusDet: Unified Multi-Dataset Object Detection
Irina Tolstykh, Mikhail Chernyshov, Maksim Kuprashevich
Motion and Structure from Event-based Normal Flow
Zhongyang Ren, Bangyan Liao, Delei Kong, Jinghang Li, Peidong Liu, Laurent Kneip, Guillermo Gallego, Yi Zhou
Hybrid Primal Sketch: Combining Analogy, Qualitative Representations, and Computer Vision for Scene Understanding
Kenneth D. Forbus, Kezhen Chen, Wangcheng Xu, Madeline Usher
TF-SASM: Training-free Spatial-aware Sparse Memory for Multi-object Tracking
Thuc Nguyen-Quang, Minh-Triet Tran
Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video Dataset
Rahm Ranjan, David Ahmedt-Aristizabal, Mohammad Ali Armin, Juno Kim