Computer Vision
Creating intelligent systems that can perceive, interpret, and understand visual information from the world around us, enabling machines to see and comprehend like never before.
Overview
Computer vision aims to enable machines to gain high-level understanding from digital images and videos. Our research spans from low-level image processing to high-level scene understanding, combining classical computer vision techniques with modern deep learning approaches.
We tackle fundamental challenges including object recognition, scene understanding, visual reasoning, 3D reconstruction, and video analysis across diverse real-world scenarios and conditions.
Current Research Focus
Object Detection and Recognition
We develop systems that can accurately identify and localize objects in images and videos. Our work includes detection in challenging conditions, fine-grained recognition, open-vocabulary detection, and real-time processing for practical applications.
Semantic Segmentation and Scene Understanding
Going beyond bounding boxes, we create models that understand images at the pixel level, parsing scenes into meaningful regions. This includes instance segmentation, panoptic segmentation, and holistic scene understanding that captures relationships between objects.
3D Vision and Reconstruction
Understanding the three-dimensional structure of the world is crucial for many applications. Our research includes depth estimation, 3D object reconstruction, simultaneous localization and mapping (SLAM), and neural rendering techniques like NeRFs.
Video Understanding and Action Recognition
Videos add the temporal dimension to visual understanding. We work on action recognition, video captioning, temporal segmentation, and understanding complex activities and events in video sequences.
Key Insight
The integration of vision and language models has opened new possibilities for zero-shot and few-shot visual understanding. Systems like CLIP demonstrate that learning visual representations through language supervision can lead to remarkably flexible vision systems.
Breakthrough Applications
- Autonomous Vehicles: Enabling self-driving cars to perceive and navigate complex environments safely
- Medical Imaging: Assisting healthcare professionals in diagnosis through automated image analysis
- Augmented Reality: Understanding physical spaces to overlay digital information seamlessly
- Industrial Inspection: Automated quality control and defect detection in manufacturing
- Wildlife Monitoring: Tracking and studying animal populations through automated image analysis
Current Challenges
Despite significant advances, key challenges remain including robustness to viewpoint, lighting, and occlusion variations, understanding fine-grained details and subtle differences, handling long-tail distributions and rare objects, reducing data requirements through better transfer learning, and ensuring fairness and reducing biases in visual recognition systems.
Recommended Resources
Dive deeper into computer vision with these foundational resources:
Computer Vision: Algorithms and Applications
Richard Szeliski's comprehensive textbook covering fundamentals through modern deep learning approaches.
Read Online →Deep Residual Learning for Image Recognition
The 2015 ResNet paper that revolutionized deep learning for vision with residual connections.
arXiv →Stanford CS231n
Convolutional Neural Networks for Visual Recognition course with comprehensive lecture notes and assignments.
Course Website →PyTorch Vision Tutorials
Practical tutorials for implementing computer vision models using PyTorch's torchvision library.
Documentation →CVPR Open Access
Access to cutting-edge computer vision research papers from the premier conference in the field.
Browse Papers →NeRF: Neural Radiance Fields
Groundbreaking 2020 work on representing scenes as continuous volumetric radiance fields.
Project Page →Impact and Future Directions
Computer vision has become integral to countless applications, from smartphones that can recognize faces and objects, to medical systems that can detect diseases, to autonomous systems that navigate the world. The field continues to evolve rapidly.
Looking forward, we see exciting opportunities in unified vision-language models that bridge visual and textual understanding, more efficient architectures for edge deployment, better handling of 3D understanding and spatial reasoning, improved temporal modeling for video understanding, and enhanced robustness and reliability for safety-critical applications.
Join Our Research
Are you passionate about advancing computer vision? We're looking for talented researchers to contribute to groundbreaking work in this field.
Apply to Research ProgramQuestions about our computer vision research? Get in touch