07/11/2025
DLSU Center for Computational Imaging & Visual Innovations (CIVI)
CIVI 2526T1 Academic Lecture Series
📆 November 12, 2025 (W)
⏰ 1:00 PM
📹 Online via Zoom
📱 Register here: https://forms.gle/W9LxYpgpKrSn39HK9
Talk 1: An Introduction to Camouflaged Object Detection
Talk 2: Introduction to Single-Stage Object Detection
Talk 3: Pruning in Computer Vision: Making Object Detection Lighter
Talk 4: Curriculum Learning for Image Classification
Talk 1: An Introduction to Camouflaged Object Detection
ABSTRACT
This lecture will cover Camouflaged Object Detection (COD), a field of Computer Vision that focuses on segmenting camouflaged objects hidden in images. Designed for advanced learners, this lecture will cover the history and prominent trends in COD, as well as the notable networks and metrics used in the field.
The outline of the lecture is as follows:
1. Definition of COD
2. History and Creation of COD
3. COD Network Architectures
4. COD Performance Metrics
5. Current Applications for COD
Prerequisite knowledge
Attendees should be knowledgeable about these topics:
- Understanding of Neural Network Concepts (e.g., weights, loss function, backpropagation, etc.)
- Understanding of Convolutional Neural Networks (CNN)
- Basic Knowledge of Computer Vision
- Understanding of Residual Learning
- Understanding of Object Segmentation
ABOUT THE SPEAKER
Aaron Gabrielle C. Dichoso is currently a 4th-year Computer Science student at De La Salle University (DLSU) under the BSMSCS Honors program. As a member of CIVI, his laboratory experiments focus on Camouflaged Crop Detection (CCD), with an interest in pursuing the improvement of CCD Network Architecture for his thesis research in the pursuit of accelerating agricultural infrastructure in the country.
Talk 2: Introduction to Single-Stage Object Detection
ABSTRACT
Single-stage object detectors have become a cornerstone in modern computer vision due to their balance of speed and accuracy. Unlike traditional two-stage detectors (e.g., Faster R-CNN) that separate region proposal and classification, single-stage detectors perform object localization and classification in one pass, making them highly efficient for real-time applications. This lecture provides a comprehensive introduction to single-stage object detection, covering the fundamental principles, popular architectures such as SSD, RetinaNet, and YOLO, as well as recent improvements in anchor-free methods. Attendees will gain insights into the design trade-offs between speed and accuracy, evaluation metrics, and practical use cases in industry and research.
The outline of the lecture is as follows:
1. Introduction to Object Detection
2. Single-Stage Object Detection Architectures
3. Two-stage vs single-stage approaches
4. Advantages and Limitations of Single-Stage Detectors
5. Applications of Single-Stage Object Detectors
Prerequisite knowledge
Attendees should be knowledgeable about these topics:
- Basic understanding of machine learning concepts
- Familiarity with convolutional neural networks (CNNs)
- Basic knowledge of object detection terminology (bounding boxes, IoU, precision/recall)
ABOUT THE SPEAKER
Matthew Ryan Carandang is an undergraduate student at De La Salle University, pursuing a degree in Computer Science as part of the honors program BSMSCS. Currently, he is conducting his thesis research on single-stage object detection, specifically developing a module to enhance a model's performance for vehicle detection.
Talk 3: Pruning in Computer Vision: Making Object Detection Lighter
ABSTRACT
As deep learning models grow increasingly complex, their deployment for real-world tasks faces challenges in computation and memory. Model optimization techniques like pruning offer practical solutions to reduce model size and improve efficiency while preserving accuracy. This lecture explores the theory of pruning, distinguishing structured and unstructured methods. While applicable to a wide range of models, this lecture highlights case studies involving YOLO object detection models, which are often optimized for mobile and edge deployment.
The outline of the lecture is as follows:
1. Motivation for optimizing object detection models
2. Overview of model compression methods
3. Pruning theory and approaches
4. Trade-offs in pruning: accuracy vs. speed vs. size
Prerequisite knowledge
Attendees should be knowledgeable about these topics:
- Basic understanding of deep learning models
- General knowledge of object detection
ABOUT THE SPEAKER
Rafael Subo A. Yap is currently a 3rd-year Computer Science student at De La Salle University (DLSU) under the BSMSCS Honors program. He is a tutor with the Peer Tutors Society (PTS) and a member of the Center for Computational Imaging & Visual Innovations (CIVI). His current research focuses on the optimization of deep learning models for counting small objects, with a particular interest in mosquito egg detection as a tool for vector surveillance and public health.
Talk 4: Curriculum Learning for Image Classification
ABSTRACT
Curriculum learning (CL) is a learning paradigm that trains machine learning models the same way that humans learn, by starting with easier problems and gradually increasing the difficulty. This strategy has shown to improve generalization and convergence rate for a variety of models in computer vision and other fields. This lecture will cover the core concepts behind CL, including a review of its history, variations, and applications.
The outline of the lecture is as follows:
1. Introduction and motivations
2. Definitions of CL
3. Theoretical basis
4. General framework
5. Potential Applications
6. Conclusion
Prerequisite knowledge
Attendees should be knowledgeable about these topics:
- Basic understanding of machine learning concepts
- Have an understanding of CNN architectures
ABOUT THE SPEAKER
Christian V. Tia is an undergraduate student in the BSMS Computer Science program at De La Salle University (DLSU). His research focuses on automated diabetic retinopathy classification and how it can be optimized in low-resource settings using smartphone-based fundus imaging.
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