Evaluating Geometric Awareness in Large-Scale Vision Models for Long-Term Point Tracking
The strong generalization abilities of large-scale vision foundation models have contributed to their amazing performance in various computer vision tasks. These models are quite adaptable since they can handle a number of jobs without requiring a lot of task-specific training. Two-view correspondence, the act of matching points or features in one image with corresponding points in another, is one area where these models have proven especially useful. This comprehension and maintenance of correspondence between two viewpoints is essential for tasks like object recognition, picture matching, and 3D reconstruction.
However, a significant problem that has not received much attention is how well these models work in long-term correspondence tasks in dynamic and complicated situations. Tracking the same physical point over time is referred to as long-term correspondence, particularly in video sequences when the point may change in appearance illumination or may be partially obscured. Since it requires keeping a point’s geometric integrity across numerous frames or views, this is far more complicated than two-view correspondence. Numerous practical applications, including autonomous driving, robotics, and object tracking in surveillance, revolve around this issue.
In order to tackle this difficulty, researchers have assessed the geometric awareness of visual foundation models within the particular domain of point tracking. This includes following a 2D projection of an identical physical point over the course of a video clip. Three separate experimental setups have been used for the evaluation.
- Zero-Shot Setting: In this configuration, the models are not trained further. The objective is to evaluate the model’s tracking ability using only the features it has already learned. A geometrically aware model should be able to follow the same place throughout time and recognize similar characteristics in different frames.
- Using Low-Capacity Layers for Probing: In this method, the pre-trained foundation model is layered with low-capacity layers that are taught to probe the geometric information embedded within the model. This enables researchers to evaluate if the model contains geometric properties that are practical and applicable to correspondence tasks involving long-term learning.
- Fine-Tuning with Low-Rank Adaptation (LoRA): In this scenario, a method known as Low-Rank Adaptation (LoRA) is used to fine-tune the foundation model. In addition to being computationally less expensive, this method enables effective fine-tuning by modifying only a limited number of parameters, enhancing the model’s performance on the particular job of point tracking.
These assessments’ outcomes produced insightful findings. In the zero-shot condition, it was discovered that two well-known vision foundation models, Stable Diffusion and DINOv2, had better geometric correspondence abilities. This suggests that even in the absence of extra training for point-tracking tasks, these models possess a robust intrinsic comprehension of geometric relationships.
DINOv2 showed performance in the adaption situation that was on par with fully supervised models. This indicates that DINOv2 can perform comparably to models that have been specially trained for the job with little fine-tuning, indicating its potential as a great initialization for learning tasks involving long-term correspondence.
In conclusion, this research broadens the range of circumstances in which large-scale vision models can be applied, even though they have already demonstrated significant promise in two-view correspondence. This includes long-term point tracking. The study demonstrates that models like Stable Diffusion and DINOv2 possess great geometric awareness, making them extremely suitable for sophisticated computer vision applications like object tracking and autonomous systems. These models are evaluated in zero-shot, probing, and fine-tuning scenarios.
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Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.
