Efficient End-to-end Multi-person Gaze Target Detection with Head-Target Association

Основні поняття

GazeHTA, an end-to-end multi-person gaze target detection framework, leverages semantic features from a pre-trained diffusion model, improves head priors through head feature re-injection, and establishes explicit associations between heads and gaze targets with a connection map.

Анотація

The paper proposes GazeHTA, an end-to-end multi-person gaze target detection framework. The key highlights are:

GazeHTA exploits features from a pre-trained diffusion model, Stable Diffusion, to extract rich semantic information for the gaze target detection task.

It introduces a head feature re-injection mechanism to enhance the head priors, improving the understanding of head locations.

GazeHTA establishes explicit visual associations between heads and gaze targets through the use of connection maps, which serve as links between the heads and their corresponding gaze targets.

The proposed framework takes a single scene image as input and directly predicts multiple head-target instances, addressing the challenges in previous approaches that rely on independent components or struggle with establishing robust associations between heads and gaze targets.

Extensive experiments demonstrate that GazeHTA outperforms state-of-the-art gaze target detection methods on two standard datasets, GazeFollow and VideoAttentionTarget, across various evaluation metrics.

The authors also show that the model components of GazeHTA, including the head feature re-injection and connection maps, can generalize beyond the diffusion model backbone and improve performance when integrated with other backbone architectures, such as DETR.

Статистика

The average gaze point distance on the GazeFollow dataset is reduced from 0.067 to 0.062, a 7% improvement.
The mean average precision (mAP) on the GazeFollow dataset is improved from 0.572 to 0.639, a 12% increase.
The average gaze point distance on the VideoAttentionTarget dataset is reduced from 0.081 to 0.069, a 15% improvement.
The mAP on the VideoAttentionTarget dataset is improved from 0.719 to 0.762, a 6% increase.

Цитати

"GazeHTA addresses challenges in gaze target detection by (1) leveraging a pre-trained diffusion model to extract scene features for rich semantic understanding, (2) re-injecting a head feature to enhance the head priors for improved head understanding, and (3) learning a connection map as the explicit visual associations between heads and gaze targets."
"Our extensive experimental results demonstrate that GazeHTA outperforms state-of-the-art gaze target detection methods and two adapted diffusion-based baselines on two standard datasets."

Ключові висновки, отримані з

GazeHTA: End-to-end Gaze Target Detection with Head-Target Association

by Zhi-Yi Lin,J... о arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10718.pdf

Глибші Запити

How can the proposed GazeHTA framework be extended to handle an arbitrary number of head-target instances per image, rather than being limited to a predefined cap?

To extend the GazeHTA framework to handle an arbitrary number of head-target instances per image, a dynamic approach to instance prediction can be implemented. Instead of fixing the number of instances to a predefined cap, the model can be designed to predict a variable number of instances based on the characteristics of each input image. This can be achieved by incorporating a mechanism that dynamically adjusts the number of predicted instances based on the features extracted from the scene image.
One way to implement this is by introducing a mechanism that can adaptively predict the number of head-target instances by analyzing the features extracted from the scene image. This mechanism can involve a dynamic instance prediction module that evaluates the scene features and adjusts the number of predicted instances accordingly. By incorporating this adaptive mechanism, the model can flexibly predict the appropriate number of head-target instances based on the complexity and content of each image.
Additionally, the model can utilize techniques such as instance segmentation or object detection to identify and delineate individual head-target instances within the scene image. By integrating these techniques into the GazeHTA framework, the model can effectively handle an arbitrary number of instances by detecting and associating multiple heads and gaze targets in a more flexible and adaptive manner.

How might the GazeHTA approach be adapted to incorporate temporal information, such as video sequences, to further improve the accuracy and robustness of gaze target detection in dynamic environments?

To adapt the GazeHTA approach to incorporate temporal information from video sequences for improved accuracy and robustness in dynamic environments, several modifications and enhancements can be implemented:

Temporal Context Modeling: Introduce recurrent or temporal convolutional layers to capture temporal dependencies and context across frames in a video sequence. By considering the temporal evolution of gaze behavior, the model can better understand the dynamics of attention shifts and gaze target transitions over time.

Frame Aggregation: Implement mechanisms for aggregating information across multiple frames to enhance the understanding of gaze patterns and target associations over time. Techniques such as temporal pooling or attention mechanisms can be employed to aggregate features from multiple frames and improve the overall gaze target detection performance.

Motion and Action Recognition: Incorporate motion and action recognition modules to analyze human movements and interactions in the video sequence. By integrating these modules, the model can leverage information about gestures, body language, and dynamic scene changes to enhance the prediction of gaze targets in dynamic environments.

Temporal Consistency Constraints: Introduce constraints or regularization techniques to enforce temporal consistency in gaze target predictions across consecutive frames. By ensuring smooth transitions and logical associations between gaze targets over time, the model can produce more reliable and coherent results in dynamic video sequences.

By integrating these temporal modeling techniques and enhancements into the GazeHTA framework, the model can effectively leverage temporal information from video sequences to improve the accuracy, robustness, and temporal coherence of gaze target detection in dynamic environments.