Exploring Temporally-Aware Features for Point Tracking (2501.12218v2)

Abstract: Point tracking in videos is a fundamental task with applications in robotics, video editing, and more. While many vision tasks benefit from pre-trained feature backbones to improve generalizability, point tracking has primarily relied on simpler backbones trained from scratch on synthetic data, which may limit robustness in real-world scenarios. Additionally, point tracking requires temporal awareness to ensure coherence across frames, but using temporally-aware features is still underexplored. Most current methods often employ a two-stage process: an initial coarse prediction followed by a refinement stage to inject temporal information and correct errors from the coarse stage. These approach, however, is computationally expensive and potentially redundant if the feature backbone itself captures sufficient temporal information. In this work, we introduce Chrono, a feature backbone specifically designed for point tracking with built-in temporal awareness. Leveraging pre-trained representations from self-supervised learner DINOv2 and enhanced with a temporal adapter, Chrono effectively captures long-term temporal context, enabling precise prediction even without the refinement stage. Experimental results demonstrate that Chrono achieves state-of-the-art performance in a refiner-free setting on the TAP-Vid-DAVIS and TAP-Vid-Kinetics datasets, among common feature backbones used in point tracking as well as DINOv2, with exceptional efficiency. Project page: https://cvlab-kaist.github.io/Chrono/

• The paper introduces Chrono, a novel feature backbone built on DINOv2 with a temporal adapter, to improve point tracking by integrating spatiotemporal awareness directly into feature extraction.
• Chrono achieves state-of-the-art accuracy on benchmarks like TAP-Vid, outperforming existing methods and demonstrating significant efficiency gains.
• This work suggests a shift towards integrating temporal dynamics at the feature level for video tasks, offering benefits in efficiency, simplicity, and potential applications in segmentation and object detection.

An Analysis of "Exploring Temporally-Aware Features for Point Tracking"

The paper at hand introduces a novel feature backbone design, named Chrono, aimed at improving point tracking in video sequences by leveraging temporally-aware features. Point tracking is a critical operation in various fields, such as robotics and video editing, which involves determining and following specific point correspondences across video frames. Chrono seeks to enhance point tracking by integrating spatiotemporal awareness directly into feature extraction, circumventing traditional multi-stage processes that rely on iterative refinement—processes noted to be both computationally demanding and susceptible to redundancy.

Methodological Contributions and Results

Chrono is constructed on the foundations of DINOv2—a self-supervised learning model celebrated for its comprehensive feature representations produced on a large corpus of diverse tasks. However, DINOv2 lacks temporal awareness, which limits its effectiveness in point tracking scenarios. To address this, the authors develop a temporal adapter to accompany DINOv2, thus facilitating the capture of long-range temporal contexts and permitting precise frame-by-frame predictions without requiring post-hoc refinement stages.

Chrono's performance is evaluated both quantitatively and qualitatively against existing methods on well-regarded benchmarks like TAP-Vid-DAVIS and TAP-Vid-Kinetics. It achieves state-of-the-art results by improving position accuracy significantly, especially in metrics that assess within varied pixel accuracy thresholds. Specifically, Chrono outpaces other architectures like TSM-ResNet models and DINOv2 alone by over 20 percentage points in certain benchmarks. Notably, Chrono demonstrates superior position accuracy with remarkable efficiency, achieving up to 12.5 times greater throughput over TAPIR-based methods—a testament to the efficiency of embedding temporal awareness directly into the feature backbone.

Implications and Future Directions

The results demonstrated by Chrono indicate a potential paradigm shift in video point tracking towards models that integrate temporal dynamics at the feature level, thus offering benefits in efficiency and precision. The elimination of redundant refinement stages not only accelerates computational speed but also enhances the overall simplicity of implementation—a crucial consideration for real-time applications and constrained environments like mobile platforms or embedded systems.

The work posits further inquiries into the broader applications of temporally-aware feature backbones, including but not limited to tasks such as video segmentation and object detection where temporal coherence is advantageous. Moreover, with its foundation in self-supervised models, future iterations could incorporate learnings from even broader datasets, possibly encompassing domain-specific video corpora to improve the robustness across diverse environmental conditions.

Concluding Remarks

Chrono's formulation provides a compelling case for the integration of pre-trained spatial feature backbones with additional temporal adapters tailored for video applications. This strategy offers a more streamlined and effective approach to threading temporal information through video sequences than existing two-stage models. Consequently, Chrono levels up the current standards in point tracking efficiency and accuracy, paving the way for its adoption into various automated video analysis systems and raising profound questions around the broader adoption of temporally-sensitive modelling in computer vision tasks.