Learning Tracking Representations from Efficient Single Point Annotations

Konsep Inti

The proposed soft contrastive learning (SoCL) framework can effectively learn tracking representations from low-cost single point annotations, achieving comparable performance to fully supervised methods while significantly reducing annotation time and cost.

Abstrak

The paper proposes a soft contrastive learning (SoCL) framework to learn tracking representations from single point annotations, which are 4.5x faster to annotate than bounding boxes.
The key components of SoCL are:

Global soft template (GST) generation: Aggregating features based on a target objectness prior (TOP) map to obtain a global representation of the target.
Soft negative sample (SNS) generation: Leveraging the context features to adaptively generate hard negative samples in a memory-efficient manner.
Local soft template (LST) generation: Sampling high-probability target locations from the TOP map to obtain local positive samples, improving robustness to partial occlusion and appearance variations.

The SoCL framework is trained using a contrastive loss that contrasts the GSTs, SNSs, and LSTs. The learned representations can be directly applied to both Siamese and correlation filter tracking frameworks.
The authors also propose a new annotation scheme that combines sparse bounding box annotations with point annotations to train state-of-the-art scale regression-based trackers, further reducing the overall annotation cost.
Experiments show that the SoCL-based trackers can: 1) achieve comparable performance to fully supervised baselines using the same number of training frames, while reducing annotation time by 78% and total fees by 85%; 2) outperform the fully supervised baseline when using the same annotation time cost; and 3) be robust to annotation noise.

Statistik

Annotating a bounding box takes about 10.2 seconds per frame, while a single point annotation takes about 2.27 seconds per frame, which is 4.5x faster.
Existing tracking datasets have millions of annotated bounding boxes, e.g., ILSVRC (2.5M) and GOT-10K (1.4M), which would take 7,083 and 3,967 hours to annotate, respectively.

Kutipan

"To ease the annotation cost in visual tracking, recent progress [42, 44] on unsupervised tracking generate pseudo labels for representation learning. However, these works still lag behind their fully supervised counterparts [9, 27, 55] due to noise in the pseudo labels."
"Different from previous trackers that use expensive bounding box annotations for fully supervised training, in this paper, we propose to learn tracking representations from low-cost and efficient single point annotations (see Fig. 1) in a weakly supervised manner."

Wawasan Utama Disaring Dari

Learning Tracking Representations from Single Point Annotations

by Qiangqiang W... pada arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09504.pdf

Pertanyaan yang Lebih Dalam

How can the proposed SoCL framework be extended to leverage additional modalities, such as video or audio, to further improve the learned tracking representations

The SoCL framework can be extended to leverage additional modalities, such as video or audio, by incorporating multi-modal information into the learning process. For video, temporal information can be integrated by considering consecutive frames in the training process. This can be achieved by incorporating recurrent neural networks (RNNs) or temporal convolutional networks (TCNs) to capture temporal dependencies in the data. By feeding sequential frames into the network, the model can learn to track objects over time, improving the robustness of the tracking representations.
Incorporating audio information can also enhance the tracking representations. Audio cues can provide valuable context for tracking, especially in scenarios where visual information may be limited or ambiguous. Audio features can be extracted and fused with visual features using multi-modal fusion techniques, such as late fusion or early fusion. This integration of audio features can help the model better understand the environment and improve tracking accuracy, especially in noisy or challenging visual conditions.
By combining visual, temporal, and audio modalities in the SoCL framework, the model can learn more comprehensive representations that capture a richer set of features and context, leading to improved tracking performance in diverse and complex scenarios.

What are the potential limitations of using single point annotations, and how could they be addressed to make the approach more robust in real-world scenarios

While single point annotations offer a faster and more cost-effective alternative to traditional bounding box annotations, they come with certain limitations that can impact tracking performance. One potential limitation is the lack of scale information provided by point annotations. Objects in videos can vary in size, and without explicit scale information, the model may struggle to accurately track objects at different scales.
To address this limitation, scale information can be incorporated into the training process by introducing scale-aware mechanisms. This can involve augmenting the training data with objects at various scales, introducing scale-specific features or modules in the network architecture, or integrating scale estimation techniques into the tracking framework. By training the model to be scale-invariant or scale-aware, it can adapt to objects of different sizes and improve tracking accuracy in real-world scenarios.
Another limitation of single point annotations is the potential for annotation noise, where the annotated point may not perfectly align with the ground truth object center. This noise can introduce errors in the learned representations and impact tracking performance. To mitigate this, techniques such as data augmentation, robust loss functions, or outlier rejection mechanisms can be employed to make the model more resilient to annotation noise. Additionally, incorporating uncertainty estimation in the training process can help the model learn to weigh the reliability of each annotation, improving its robustness in the presence of noisy data.
By addressing these limitations through scale-aware training and noise robustness strategies, the SoCL framework can be made more robust and effective in real-world tracking scenarios.

Given the success of the SoCL framework in visual tracking, how could the underlying principles be applied to other computer vision tasks that rely on expensive annotations, such as object detection or semantic segmentation

The success of the SoCL framework in visual tracking can be extended to other computer vision tasks that rely on expensive annotations, such as object detection or semantic segmentation, by applying similar principles of contrastive learning and weakly supervised training.
For object detection, the SoCL framework can be adapted to learn object representations from single point annotations, similar to how it learns tracking representations. By generating objectness priors and leveraging contrastive learning, the model can learn to detect objects in images without the need for expensive bounding box annotations. This approach can significantly reduce annotation costs and make object detection more accessible for large-scale datasets.
In semantic segmentation, the SoCL framework can be utilized to learn pixel-wise representations from point annotations. By generating soft templates and negative samples based on semantic segmentation masks, the model can learn to segment objects in images with minimal annotation effort. This weakly supervised approach can be particularly beneficial for tasks where pixel-level annotations are labor-intensive and costly.
By applying the principles of contrastive learning and weakly supervised training from the SoCL framework to other computer vision tasks, researchers can explore more efficient and cost-effective ways to train models on large-scale datasets, ultimately advancing the field of computer vision and making it more accessible for various applications.