video analysis
-
From Object Detection to Video Intelligence
Read Full Article: From Object Detection to Video Intelligence
Object detection models like YOLO excel at real-time, frame-level inference and producing clean bounding box outputs, but they fall short when it comes to understanding video as data. The limitations arise in system design rather than model performance, as frame-level predictions do not naturally support temporal reasoning, nor do they provide a searchable or queryable representation. Additionally, audio, context, and higher-level semantics are often disconnected, highlighting the difference between identifying objects in a frame and understanding the events in a video. The focus needs to shift towards building pipelines that incorporate temporal aggregation, multimodal fusion, and systems that enhance rather than replace models. This approach aims to address the complexities of video analysis, emphasizing the need for both advanced models and robust systems. Understanding these limitations is crucial for developing comprehensive video intelligence solutions.
-
Modular Pipelines vs End-to-End VLMs
Read Full Article: Modular Pipelines vs End-to-End VLMs![[D] Reasoning over images and videos: modular pipelines vs end-to-end VLMs](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-7835-300x171.png)
Exploring the best approach for reasoning over images and videos, the discussion contrasts modular pipelines with end-to-end Vision-Language Models (VLMs). While end-to-end VLMs show impressive capabilities, they often struggle with brittleness in complex tasks. A modular setup is proposed, where specialized vision models handle perception tasks like detection and tracking, and a Language Model (LLM) reasons over structured outputs. This approach aims to improve tasks such as event-based counting in traffic videos, tracking state changes, and grounding explanations to specific objects, while avoiding hallucinated references. The tradeoff between these methods is examined, questioning where modular pipelines excel and what reasoning tasks remain challenging for current video models. This matters because improving how machines interpret and reason over visual data can significantly enhance applications in areas like autonomous driving, surveillance, and multimedia analysis.