Source-linked AI summary
Vision-Language Transformer and Query Generation for Referring Segmentation
Henghui Ding, Chang Liu, Suchen Wang, Xudong Jiang
TL;DR
Referring segmentation must locate an object from language that may describe relationships and ambiguous emphases, requiring holistic image-language understanding. The paper uses a transformer attention architecture with vision-guided query generation and adaptive query balancing, and reports new state-of-the-art performance across three datasets.
Problem
Referring segmentation requires holistic understanding because language expressions identify targets through relationships, while varied objects, images, and unconstrained language create ambiguity.
Method
The method reformulates referring segmentation as attention-based image querying with a Vision-Language Transformer, Query Generation Module, and Query Balance Module.
Results
The proposed model achieves new state-of-the-art performance consistently on three referring-segmentation datasets.
Takeaways & Limitations
Vision-guided diversified comprehensions and adaptive query selection provide the paper’s approach to ambiguous referring expressions and complex visual contexts.
Abstract
from arXiv · showhide
In this work, we address the challenging task of referring segmentation. The query expression in referring segmentation typically indicates the target object by describing its relationship with others. Therefore, to find the target one among all instances in the image, the model must have a holistic understanding of the whole image. To achieve this, we reformulate referring segmentation as a direct attention problem: finding the region in the image where the query language expression is most attended to. We introduce transformer and multi-head attention to build a network with an encoder-decoder attention mechanism architecture that "queries" the given image with the language expression. Furthermore, we propose a Query Generation Module, which produces multiple sets of queries with different attention weights that represent the diversified comprehensions of the language expression from different aspects. At the same time, to find the best way from these diversified comprehensions based on visual clues, we further propose a Query Balance Module to adaptively select the output features of these queries for a better mask generation. Without bells and whistles, our approach is light-weight and achieves new state-of-the-art performance consistently on three referring segmentation datasets, RefCOCO, RefCOCO+, and G-Ref. Our code is available at https://github.com/henghuiding/Vision-Language-Transformer.
1. Introduction
Referring segmentation requires holistic vision-language understanding because expressions often identify targets through relationships and varied descriptions. The paper addresses these challenges with a fully attention-based Vision-Language Transformer, diversified query generation, and adaptive query balancing.
- Challenge: Referring segmentation must identify a target object from a natural-language expression describing its properties and relationships.The task combines natural language processing and computer vision and requires understanding the image globally.
- Motivation: Convolution-based pipelines model long-range pixel and word dependencies indirectly, limiting global-context understanding.The paper motivates direct interactions among pixels, words, and cross-modal features through global operations.
- Approach: The Vision-Language Transformer reformulates referring segmentation as attention-based querying of the image with language features.Its architecture replaces the FCN-like prediction and identification head with fully attention-based processing to model long-range dependencies.
- Approach: The Query Generation Module produces multiple language comprehensions, while the Query Balance Module selects suitable query outputs for mask generation.The query vectors use vision-guided attention, and balancing helps focus on more reasonable comprehension ways for each image-expression pair.
- Results: The lightweight method achieves new state-of-the-art performance consistently across multiple referring-segmentation datasets, especially difficult and complex ones.The contribution statement reports consistent gains across datasets, with particular strength on hard and complex cases.
2. Related Works
Prior referring-segmentation methods combine linguistic and visual features using recurrent, convolutional, or auxiliary-attention architectures. This work instead uses a fully attention-based architecture to model long-range image dependencies directly.
- Referring Segmentation: Early referring-segmentation methods fused LSTM language features with CNN vision features before applying a fully convolutional network.Later recurrent multimodal approaches incorporated each word from the referring sentence.
- Attention-Based Methods: Prior attention methods adaptively emphasize informative words and image regions or learn language-guided and vision-guided cross-modal relationships.These approaches use attention modules to improve multimodal feature processing.
- Transformer Architecture: Unlike FCN-based approaches, the proposed architecture replaces the prediction and identification head with fully attention-based processing for direct long-range dependency modeling.The distinction is architectural: attention is used as the main prediction pathway rather than only as an auxiliary module.
- Attention and Transformer: Transformers use attention mechanisms to model global information, contrasting with CNNs that focus on local pixels through kernels.The related-work discussion situates transformer adoption across language and vision tasks.
3. Methodology
The method uses a shallow transformer to directly model global vision-language interactions, generating multiple image-guided language queries and balancing their decoder responses for mask prediction. Its modules address complex image relationships, variable language emphasis, and highly random target properties.
- Overall Architecture: The network encodes image and language features, generates language query vectors with QGM, queries transformer vision memory, balances decoder responses, and outputs a target mask.The transformer encoder processes flattened vision features into memory, while QGM-derived queries drive the decoder.
- Transformer Architecture: The architecture replaces indirect long-range convolutional interactions with global attention operations among image and language elements.The motivation is to improve holistic multimodal understanding when target objects are identified through complicated relationships.
- Query Generation Module: Vision features are reduced to Nq spatial feature maps and flattened into sequential features, with each map participating in one query’s generation.This preparation converts regular 2D vision features into a feature matrix of size Nq × (HW).
- Query Generation Module: QGM produces multiple language queries whose attention weights are guided by vision features, representing different comprehensions of the expression.Each query is formed from language features with a distinct word-attention pattern, allowing different aspects of the expression to be represented.
- Query Generation Module: Image-conditioned attention distinguishes which words matter for a specific image, unlike language-only self-attention that produces one emphasis pattern.For “The large circle on the left,” “left” is more informative in one image while “large” is more useful in another.
- Query Balance Module: The Query Balance Module computes confidence levels for query-response pairs and uses them to control each query’s influence on the final mask.This adaptively selects more suitable comprehension ways while retaining only one mask output.
4. Experiments
Experiments evaluate the model on three referring-segmentation datasets, ablate its modules and query count, and compare it with prior methods. The results show consistent gains, especially on harder cases and longer expressions, while visualizations illustrate diverse queries and long-range attention.
- Experiment Settings: The approach is evaluated on RefCOCO, RefCOCO+, and G-Ref using mask IoU and Precision@X.IoU measures overall mask quality, while Precision@X focuses on successful targeting at an IoU threshold.
- Ablation Study: The transformer module exceeds a similarly sized seven-convolution-layer alternative by over 5% IoU and 7% Prec@0.5.The attention-based module has a parameter size roughly equivalent to seven convolutional layers.
- Ablation Study: The Query Generation Module outperforms two alternative query-vector generation methods by about 3%–6%.The comparison is reported in Table 2 for alternative ways of producing query vectors.
- Ablation Study: Increasing queries from 1 to 16 yields about a 5% performance gain, while gains slow beyond 8 queries.Removing the Query Balance Module causes an approximately 1% performance loss, and 16 queries are selected by default.
- Comparison with State-of-the-art: Compared with previous state-of-the-art methods, VLT gains about 1% IoU on RefCOCO, around 5% on RefCOCO+, and about 2%–5% on G-Ref.The authors report higher performance on all three datasets, including the especially difficult RefCOCO+ testB split.
- Visualization and Qualitative Results: Qualitative examples cover location, color, comparative, attribute, rare-word, relational, directional, and multi-attribute expressions.The examples include “Elephant with rider” and densely arranged people described by direction, clothing, and posture.
- Visualization and Qualitative Results: Transformer attention maps show long-range dependencies within one encoder layer, while query vectors exhibit diversity.A point from one instance attends to related instances across the image.
5. Conclusion
The paper reformulates referring segmentation as an attention problem and uses transformer-based global interactions to query images with language. Query Generation and Query Balance Modules address ambiguous emphasis, and the model achieves large-margin gains on three datasets.
- Conclusion: The method uses attention networks and transformers to alleviate global information exchange problems in conventional convolutional networks.It treats referring segmentation as finding image regions attended to by the language expression.
- Conclusion: The Query Generation and Query Balance Modules use the referred image to handle ambiguous emphasis in referring sentences.The first generates diversified comprehensions, while the second adaptively selects query features for mask generation.
- Conclusion: The proposed model outperforms other methods with a large margin on RefCOCO, RefCOCO+, and G-Ref.