Video segmentation asks a model to find every object in a video, draw a mask around it in each frame, and keep that object’s identity consistent as it moves, gets occluded, and reappears. Video instance segmentation focuses on countable objects…
Decoupled Video Segmentation Outperforms End To End Models Read More »
In the fast-evolving world of deep learning, capsule networks (CapsNets) have emerged as a promising alternative to traditional convolutional neural networks (CNNs). Unlike CNNs, which lose spatial hierarchies due to pooling layers, CapsNets aim to preserve part-whole relationships through dynamic…
Capsule Networks Do Not Need to Model Everything: How REM Reduces Entropy for Smarter AI Read More »
Getting a computer to say which triangles of a mesh belong to a chair leg versus a chair seat sounds like a small problem next to full scene understanding, yet it underpins robotic grasping, 3D asset generation, and interactive editing…
GeoSAM2 Turns SAM2 Into a 3D Part Segmentation Tool Read More »
Australia is home to over 600 mammal species, 800 bird species, and countless reptiles and amphibians — many found nowhere else on Earth. Yet, as biodiversity declines at an alarming rate, accurate, fast, and scalable species identification has become a…
7 Reasons Why YOLO-FCE Outshines Traditional Models (And One Critical Flaw) Read More »
In the fast-evolving world of deep learning, one of the most promising techniques for deploying AI on edge devices is Knowledge Distillation (KD). But despite its popularity, many implementations suffer from critical flaws that undermine performance. A groundbreaking new paper…
Detecting tiny vehicles in drone footage. Spotting distant pedestrians in smart city surveillance. Identifying miniature components on a factory floor. These are the critical challenges facing modern computer vision—where small object detection (SOD) isn’t just a technical hurdle, but a…
7 Revolutionary Breakthroughs in Small Object Detection: The DAHI Framework Read More »
Semi supervised learning exists because labeling data is expensive and unlabeled data is cheap. Pseudo label methods try to make the most of that imbalance by having a model label its own unlabeled data, then training on those self generated…
How A Channel Based Ensemble Produces Cleaner Pseudo Labels Read More »