-
- |
-
-
- |
-
- 16-385 / Computer Vision
- This course provides a comprehensive introduction to computer vision. Major topics
- include image processing, detection
- and recognition, geometry-based and physics-based vision and video analysis.
- Students will learn basic concepts of
- computer vision as well as hands on experience to solve real-life vision problems.
- Course Website
- |
+
-
-
-
-
- |
+
+
+
+|
+
+
+ |
+
+ 16-889 : Learning for 3D Vision
+ Any autonomous agent we develop must perceive and act in a 3D world. The ability to infer, model, and utilize 3D representations is therefore of central importance in AI, with applications ranging from robotic manipulation and self-driving to virtual reality and image manipulation. While 3D understanding has been a longstanding goal in computer vision, it has witnessed several impressive advances due to the rapid recent progress in (deep) learning techniques. The goal of this course is to explore this confluence of 3D Vision and Learning-based methods.
+Course Website
+ |
+
+
+
-
- 16-889 / Learning for 3D Vision
- While 3D understanding has
- been a longstanding goal in computer
- vision, it has witnessed several impressive advances due to the rapid recent
- progress in (deep) learning techniques. The
- goal of this course is to explore this confluence of 3D Vision and Learning-based
- methods.
+
+
+
+|
+
+
+ |
+
+ 16-385 : Computer Vision
+ This course provides a comprehensive introduction to computer vision. Major topics include image processing, detection and recognition, geometry-based and physics-based vision and video analysis. Students will learn basic concepts of computer vision as well as hands on experience to solve real-life vision problems.
+Course Website
+ |
+
+
+
+
+
+
+
+|
+
+
+ |
+
+ 16-824 : Visual Learning and Recognition
+ This graduate-level computer vision course focuses on representation and reasoning for large amounts of data (images, videos, and associated tags, text, GPS locations, etc.) toward the ultimate goal of understanding the visual world surrounding us. We will be reading an eclectic mix of classic and recent papers on topics including Theories of Perception, Mid-level Vision (Grouping, Segmentation, Poses), Object and Scene Recognition, 3D Scene Understanding, Action Recognition, Contextual Reasoning, Joint Language and Vision Models, Deep Generative Models, etc. We will be covering a wide range of supervised, semi-supervised and unsupervised approaches for each of the topics above.
+Course Website
+ |
+
+
+
- Course Website
- |
-
- 
+ 
+
+ 
+def parse_course_info(course_id, course_folder):
+ elems = {}
+ keys = []
+ fpath = os.path.join(course_folder, course_id + '.txt')
+
+ with open(fpath, 'r') as f:
+ for ln in f.readlines():
+ ln_items = ln.split('::')
+ if len(ln_items) != 2:
+ continue
+ key, val = ln_items[0], ln_items[1]
+ key = key.rstrip().lstrip()
+ val = val.rstrip().lstrip()
+ elems[key] = val
+ keys.append(key)
+
+ if 'img' not in keys:
+ elems['img'] = 'courses/16385.jpeg'
+
+ return elems, keys
+
+
+def get_course_html_string(id, elems, keys):
+ comment_str = ''
+ course_str = ''
+ course_str += '\n' + comment_str + '\n'
+ course_str += '
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+