Language as Queries for Referring Video Object Segmentation
ReferFormer uses Transformer with language as queries, achieving 55.6 J&F on Ref-Youtube-VOS, surpassing previous SOTA by 8.4 points, enabling end-to-end video object segmentation and tracking.
Key Findings
Methodology
This approach employs a Transformer-based architecture where natural language expressions serve as conditioned queries. A small set of object queries, guided by the language, are input into the Transformer to locate the referred object. These queries are transformed into dynamic kernels that generate segmentation masks directly from multi-scale visual features. The model integrates a cross-modal feature pyramid network (CM-FPN) to fuse visual and linguistic information at multiple scales. Tracking is achieved naturally by linking queries across frames, enabling an end-to-end process that simplifies previous multi-stage pipelines. Extensive experiments on datasets like Ref-Youtube-VOS and Ref-DAVIS17 demonstrate significant performance gains, with ResNet-50 backbone achieving 55.6 J&F and Swin-Large reaching 64.2.
Key Results
- On Ref-Youtube-VOS, ReferFormer with ResNet-50 backbone achieves 55.6 J&F, outperforming prior SOTA by 8.4 points. Using Swin-Large backbone, it reaches 64.2, the best among existing methods. On A2D-Sentences and JHMDB-Sentences, it achieves 55.0 and 43.7 mAP respectively, vastly superior to previous approaches. The model's ability to perform detection, segmentation, and tracking simultaneously in an end-to-end manner is validated across multiple benchmarks, confirming its robustness and effectiveness.
Significance
This work addresses the complexity and inefficiency of traditional multi-stage R-VOS methods by proposing a unified, end-to-end framework. It enhances cross-modal understanding by directly integrating language as queries, leading to more accurate and efficient target localization and segmentation. The approach simplifies the pipeline, reduces computational cost, and improves scalability. Its strong performance on standard datasets indicates its potential for real-world applications such as video editing, surveillance, and human-computer interaction, pushing the frontier of cross-modal video analysis.
Technical Contribution
Key contributions include: 1) framing language as conditioned queries within a Transformer architecture; 2) introducing dynamic kernels for high-quality mask generation; 3) designing a multi-scale cross-modal feature pyramid network (CM-FPN) for effective fusion; 4) enabling natural object tracking via query linking without additional post-processing. These innovations collectively enable a simple yet powerful end-to-end R-VOS system with superior accuracy and efficiency compared to prior multi-stage methods.
Novelty
This is the first work to treat natural language expressions as queries in a Transformer-based framework for referring video object segmentation, integrating dynamic kernel generation and multi-scale cross-modal fusion. Unlike previous methods relying on separate detection and association stages, this approach achieves detection, segmentation, and tracking simultaneously within a unified model. The conditional query mechanism ensures focus on the referred object, significantly improving robustness and interpretability. These innovations mark a new paradigm in cross-modal video understanding.
Limitations
- Despite strong performance, the model's effectiveness diminishes in scenarios with severe occlusion, fast motion, or complex multi-object interactions. High computational demands of large backbones like Swin-Large limit real-time deployment. The reliance on large annotated datasets restricts generalization to unseen scenarios. Future work should focus on improving robustness, efficiency, and scalability, possibly through self-supervised learning or model compression techniques.
Future Work
Future directions include enhancing model robustness to occlusion and clutter, reducing computational costs for real-time applications, extending to multi-object scenarios, and exploring self-supervised or semi-supervised training to reduce annotation dependency. Additionally, integrating more sophisticated language understanding modules could improve performance on complex expressions. Expanding datasets to cover diverse environments will further boost generalization, facilitating broader deployment in real-world systems.
AI Executive Summary
Referring video object segmentation (R-VOS) is a challenging task that combines visual and linguistic understanding to locate and segment objects in videos based on natural language descriptions. Existing solutions often rely on multi-stage pipelines involving detection, association, and segmentation, which are computationally intensive and difficult to optimize end-to-end. To address these issues, this work introduces ReferFormer, a Transformer-based framework that treats language as conditioned queries. This approach simplifies the pipeline by directly attending to relevant regions in each frame, transforming queries into dynamic kernels that generate precise segmentation masks. The core innovation lies in integrating a cross-modal feature pyramid network (CM-FPN), which fuses visual and linguistic features at multiple scales, enhancing discriminative power. Extensive experiments on datasets like Ref-Youtube-VOS and Ref-DAVIS17 demonstrate the effectiveness of the method, with ResNet-50 backbone achieving 55.6 J&F, surpassing previous state-of-the-art by 8.4 points, and Swin-Large reaching 64.2. The model also performs strongly on A2D-Sentences and JHMDB-Sentences, with 55.0 and 43.7 mAP respectively. The end-to-end architecture naturally links queries across frames, enabling seamless object tracking without additional post-processing. This unified framework significantly advances the efficiency and accuracy of R-VOS, opening new avenues for applications in video editing, surveillance, and human-computer interaction. Future work will focus on improving robustness in complex scenarios, reducing computational costs, and extending multi-object capabilities, aiming to bring intelligent video understanding closer to real-world deployment.
Deep Dive
Limitations & Outlook
What gaps remain?
Abstract
Referring video object segmentation (R-VOS) is an emerging cross-modal task that aims to segment the target object referred by a language expression in all video frames. In this work, we propose a simple and unified framework built upon Transformer, termed ReferFormer. It views the language as queries and directly attends to the most relevant regions in the video frames. Concretely, we introduce a small set of object queries conditioned on the language as the input to the Transformer. In this manner, all the queries are obligated to find the referred objects only. They are eventually transformed into dynamic kernels which capture the crucial object-level information, and play the role of convolution filters to generate the segmentation masks from feature maps. The object tracking is achieved naturally by linking the corresponding queries across frames. This mechanism greatly simplifies the pipeline and the end-to-end framework is significantly different from the previous methods. Extensive experiments on Ref-Youtube-VOS, Ref-DAVIS17, A2D-Sentences and JHMDB-Sentences show the effectiveness of ReferFormer. On Ref-Youtube-VOS, Refer-Former achieves 55.6J&F with a ResNet-50 backbone without bells and whistles, which exceeds the previous state-of-the-art performance by 8.4 points. In addition, with the strong Swin-Large backbone, ReferFormer achieves the best J&F of 64.2 among all existing methods. Moreover, we show the impressive results of 55.0 mAP and 43.7 mAP on A2D-Sentences andJHMDB-Sentences respectively, which significantly outperforms the previous methods by a large margin. Code is publicly available at https://github.com/wjn922/ReferFormer.