This research focuses on improving visual systems so they can move from basic perception toward structured scene understanding in both images and videos. The work focuses on how models interpret visual data and how different elements in a scene are connected. The goal is to go beyond detecting individual objects and build representations that capture relationships and interactions between them. In video data, this also includes understanding how these elements change over time. The research also considers efficiency so that these systems can be applied in real-world environments with limited computational resources.
To illustrate this idea, consider a street scene. A basic system may detect a person, a bicycle, and a traffic light. A more advanced system can understand that the person is standing next to the bicycle and waiting for the light. In a video, it can further recognize that the person starts moving, gets on the bicycle, and crosses the street. This example shows the difference between simple detection and structured understanding, where objects, relationships, and actions are connected into a meaningful scene.
Visual data in the form of images and videos is widely used across many applications, and current systems have achieved strong performance in recognizing objects and basic attributes. They can identify people, vehicles, animals, and common objects within a scene, which provides a useful starting point for visual analysis.
However, real-world understanding requires more than object recognition. It involves understanding how objects relate to each other and how they form a complete situation. For example, detecting a person and a bicycle is not enough. A deeper level of understanding comes from recognizing whether the person is riding the bicycle, standing next to it, or interacting with the surrounding environment. These relationships provide important context that supports more accurate interpretation.
In video data, the challenge becomes more complex because understanding must extend over time. Objects can change their roles and relationships across frames. A person may move from standing still to walking, and then interact with other elements in the scene. This requires linking information across time in a consistent way. Context also plays an important role. Surrounding information can help interpret unclear or partially visible elements. At the same time, real-world data often varies due to lighting, viewpoint, motion, and occlusion. A reliable system must maintain stable interpretation under these changing conditions.
These challenges motivate the need for structured representation learning, contextual reasoning, and unified approaches that can handle both images and videos in a consistent manner.
This research aims to develop a unified framework for structured scene understanding across both images and videos. One key outcome is the ability to represent not only objects but also the relationships between them in a structured form. This allows the system to capture spatial relationships in images and interaction patterns that evolve over time in videos.
Another important result is the improved use of context. The system integrates local visual details with broader scene information to support more accurate and stable interpretation. In video settings, earlier frames provide useful cues that help explain current observations, especially when the input is incomplete or changing. This helps the model maintain consistency across time. The research also focuses on robustness under different conditions. The system is designed to produce consistent interpretations even when the same scene appears under different lighting, viewpoints, or partial visibility. This is essential for real-world applications where data is often noisy and uncontrolled.
In addition, the framework supports shared representations that can be applied across related tasks. This reduces the need to build separate models for images and videos, leading to more efficient training and deployment. Efficiency is a key consideration, allowing the system to operate on devices with limited computational capacity.
The impact of this research lies in improving how systems understand visual information in practical settings. In applications such as autonomous systems and monitoring systems, understanding how objects interact and how scenes change over time can support better decision making. For example, recognizing how a situation develops can help a system respond more safely in dynamic environments.
Structured scene understanding can also improve clarity in image based applications, while temporal understanding enhances the tracking of actions and events in video based tasks. The focus on efficiency makes it possible to deploy these systems in a wide range of environments, including mobile devices, cameras, and embedded systems with limited computing power.
This research faces several challenges in modeling structured relationships in visual data. One major challenge is representing many objects and their interactions efficiently, especially in complex and crowded scenes. As the number of elements increases, the relationships between them become more difficult to capture.
Another challenge is handling variation in real-world data. Changes in lighting, viewpoint, motion, and occlusion can affect how scenes are perceived. Ensuring stable and consistent understanding under these conditions requires robust modeling techniques.
There are also important directions for future work. One direction is improving the connection between visual understanding and language, so that systems can both interpret and describe scenes, including summarizing events over time. Another direction is extending the approach to longer video sequences, where understanding must remain consistent across many frames. Future research can also explore learning from limited labeled data and improving generalization across different environments. These advances can make structured scene understanding more reliable and widely applicable in real-world settings.
Dang Huynh
