Image Segmentation Using Text and Image Prompts (2112.10003v2)

Abstract: Image segmentation is usually addressed by training a model for a fixed set of object classes. Incorporating additional classes or more complex queries later is expensive as it requires re-training the model on a dataset that encompasses these expressions. Here we propose a system that can generate image segmentations based on arbitrary prompts at test time. A prompt can be either a text or an image. This approach enables us to create a unified model (trained once) for three common segmentation tasks, which come with distinct challenges: referring expression segmentation, zero-shot segmentation and one-shot segmentation. We build upon the CLIP model as a backbone which we extend with a transformer-based decoder that enables dense prediction. After training on an extended version of the PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on an additional image expressing the query. We analyze different variants of the latter image-based prompts in detail. This novel hybrid input allows for dynamic adaptation not only to the three segmentation tasks mentioned above, but to any binary segmentation task where a text or image query can be formulated. Finally, we find our system to adapt well to generalized queries involving affordances or properties. Code is available at https://eckerlab.org/code/clipseg.

A Formal Analysis of Image Segmentation Using Text and Image Prompts

The paper "Image Segmentation Using Text and Image Prompts" presents a novel system named CLIPSeg for addressing various image segmentation tasks using both textual and visual prompts. This versatile model is a significant evolution from traditional segmentation methods that require training on fixed object classes, offering a more flexible alternative capable of adapting to several tasks using a single pretrained backbone.

System Overview and Approach

The proposed method leverages the CLIP model, known for its efficiency in creating joint image-text embeddings, as a foundational backbone. This backbone is extended with a transformer-based decoder dedicated to dense predictions, enabling the segmentation of images not only based on text but also upon other exemplar images. The model's architecture is designed to generalize across three primarily distinct tasks: referring expression segmentation, zero-shot segmentation, and one-shot segmentation.

A salient innovation is the use of hybrid input, allowing the system to interpret segmentation targets through either a text prompt or an image query, thereby enhancing the model's adaptability. The authors employ a dense prediction approach using a binary setting and demonstrate the applicability of their technique in both multi-label scenarios and classes outside of the training set via image-text interpolation.

Datasets and Experiments

The model is evaluated using the PhraseCut dataset alongside an augmented version, PhraseCut+, which includes negative samples and various prompt forms. This setup facilitates assessing the model's capability to generalize beyond the classes explicitly presented during training. Moreover, the versatility of CLIPSeg is illustrated through its performance over traditional benchmarks like Pascal-VOC and custom sets demonstrating affordance recognition.

Results and Analysis

Across the board, CLIPSeg exhibits competitive performance, surpassing existing referring expression models like those based on CNN and RNN combinations. For zero-shot segmentation, the model achieves balanced performance across seen and unseen classes, suggesting robust generalization capabilities largely attributed to the CLIP pretrained weights. In the one-shot segmentation task, the approach stands strong against state-of-the-art models while offering the unique advantage of handling textual input natively.

One of the key outcomes of the paper lies in the successful application of visual prompt engineering -- a novel concept analogous to linguistic prompt techniques. Through various transformations like background blurring and intensity modification, this method enables CLIPSeg to strongly align its predictions with the intended object classes. Such detailed manipulations have demonstrated substantial improvements in segmentation accuracy, especially when no direct textual description is available.

Implications and Future Directions

The implications of this research extend to practical applications wherein segmentation needs to accommodate dynamic environments with new objects and expressions, such as in robotics or user-interactive systems. The integration of text and image prompt handling furnishes a path for developing more adaptable vision solutions, reducing the dependency on extensive retraining cycles when encountering diverse tasks.

For future work, the exploration of larger and more diverse datasets could further enhance the adaptability of the model, bridging the gap between laboratory performance and real-world application. Additionally, the integration of other sensory inputs such as sound or tactile feedback could be explored to further expand the model's utility in multi-modal AI systems. Furthermore, addressing limitations like dataset biases inherited from training paradigms and extending applications to video data with temporal consistency stands as valuable avenues for ongoing research.

In conclusion, the paper provides a well-articulated and methodologically sound approach to redefining image segmentation tasks through the utilization of flexible prompting techniques, positioning CLIPSeg as a formidable tool in the expansive field of computer vision.