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Improving Image Captioning with Better Use of Captions
Zhan Shi, Xu Zhou, Xipeng Qiu, Xiaodan Zhu
TL;DR
Image captioning methods have not adequately leveraged caption semantics to build image representations and generate captions. The paper constructs caption-guided visual relationship graphs with weakly supervised multi-instance learning, enriches them with contextual features, and uses multi-task word-tag generation; on MSCOCO, the framework significantly outperforms baselines and achieves state-of-the-art performance across evaluation metrics.
Problem
Existing methods have not adequately leveraged semantic information available in captions to construct image representations and generate captions.
Method
The framework constructs caption-guided visual relationship graphs with weakly supervised multi-instance learning, enriches representations with neighboring and contextual text and visual features, and jointly predicts words and object/predicate tags.
Results
The framework significantly outperforms baselines and achieves state-of-the-art performance on MSCOCO across various evaluation metrics.
Takeaways & Limitations
Caption semantics can provide inductive bias for image representation and explicit object/predicate constraints for caption generation within this framework.
Abstract
from arXiv · showhide
Image captioning is a multimodal problem that has drawn extensive attention in both the natural language processing and computer vision community. In this paper, we present a novel image captioning architecture to better explore semantics available in captions and leverage that to enhance both image representation and caption generation. Our models first construct caption-guided visual relationship graphs that introduce beneficial inductive bias using weakly supervised multi-instance learning. The representation is then enhanced with neighbouring and contextual nodes with their textual and visual features. During generation, the model further incorporates visual relationships using multi-task learning for jointly predicting word and object/predicate tag sequences. We perform extensive experiments on the MSCOCO dataset, showing that the proposed framework significantly outperforms the baselines, resulting in the state-of-the-art performance under a wide range of evaluation metrics.
1 Introduction
The paper addresses the limited use of caption semantics in image representation and generation by constructing caption-guided visual relationship graphs and incorporating explicit visual relationships during decoding. Its framework combines weakly supervised graph construction, contextual node features, and multi-task generation, achieving state-of-the-art performance on MSCOCO.
- Existing captioning methods often translate image representations into sentences without explicitly investigating semantic cues from texts and images.
- Caption-guided visual relationship graphs bridge captions and images by using weakly supervised multi-instance learning to resolve ambiguous predicate-to-region links.The graphs represent object and predicate nodes, with graph convolution operations producing image representation units for generation.
- The framework enhances graph representations with neighboring and contextual nodes using textual and visual features.
- Multi-task learning jointly predicts words and object, predicate, or none tags to impose explicit object/predicate constraints during caption generation.The tag sequence helps regulate whether predicate-node features, object-node features, or the language decoder state receives greater consideration at each generation step.
- The proposed framework achieves state-of-the-art performance on the MSCOCO image captioning dataset.The experiments include supervised and reinforcement-learning settings and report performance across a wide range of evaluation metrics.
2 Related Work
Image captioning work has increasingly connected visual representations with caption semantics through attention, semantic concepts, visual relationships, and graph-based encoders. This paper extends that direction by using caption-guided visual relationship graphs and multi-task generation to connect relational semantics with both image representation and caption generation.
- Image Captioning: Encoder-decoder captioning models commonly combine CNN-based image encoding with RNN-based text generation and visual attention.Attention mechanisms focus generation on relevant image regions, while adaptive attention selects between visual features and language-model states.
- Image Captioning: Semantic concepts, attributes, visual relationships, and scene graphs have been incorporated into image encoders to improve captioning representations.Prior work uses graph convolutional mechanisms in unimodal and multimodal settings, while relationship-based captioning targets image understanding through relationships.
- Motivation: Existing approaches have not adequately leveraged caption semantics to construct image representations and generate captions, while pretrained visual-relationship detectors may omit caption-relevant predicates.The paper highlights predicates such as “grab” as relationships that conventional detection models can ignore.
- Constructing CGVRG: Caption-guided visual relationship graphs address ambiguous links between caption predicates and multiple candidate object-region pairs using weakly supervised multi-instance learning.The method represents object-region pairs, predicts caption-derived predicates, and uses positive or negative bags depending on predicate occurrence in captions.
- Context-Aware CGVRG: The proposed graph contains object and predicate nodes connected by directed edges, and weak supervision permits predicates present in captions but absent from prior VRD models.Graph convolution operations then produce representations for object and predicate nodes used in generation.
- Multi-task Caption Generation: The generation module jointly predicts words and object/predicate/none tags, using graph-node features to incorporate visual relationships during caption generation.The two tasks are trained jointly through multi-task learning, with the tag prediction providing explicit constraints on the information considered at each generation step.
3 Experiments
Experiments on MSCOCO evaluate the proposed framework against established captioning and visual-relationship baselines using automatic metrics, ablations, human judgments, and qualitative examples. The model improves CIDEr-D, benefits from graph convolution and multi-task learning, and produces captions judged favorably across several criteria.
- Datasets and Experiment Setup: Experiments use the MSCOCO benchmark, standard captioning metrics, Visual Genome pretraining, and comparisons with established visual-relationship methods.The Karpathy split contains 113K training, 5K validation, and 5K testing images; evaluation includes SPICE, CIDEr-D, METEOR, ROUGE-L, and BLEU.
- Quantitative Analysis: The proposed model improves CIDEr-D from 113.5 to 119.0 during cross-entropy optimization and from 120.1 to 129.6 during CIDEr-D optimization.These results are reported for single models on the MSCOCO Karpathy split.
- Quantitative Analysis: On the COCO online test server, CIDEr-D improves from 120.5 to 126.7, with the model achieving the best results across all evaluation metrics on c40.The comparison includes the baseline and three other models incorporating visual relationship graphs.
- Quantitative Analysis: Ablations show that both graph convolution and multi-task learning improve the quality of generated captions.The multi-task loss weight around 0.15 yields the best performance in both multi-task blocks.
- Human Evaluation: Human evaluations favor MT-I over Up-Down on 44% of images for fidelity and 60% for informativeness, while fluency is equal on 72%.MT-I is equal to Up-Down on 46% of images for fidelity and 34% for informativeness, and on 72% for fluency.
- Qualitative Analysis: Qualitative examples show captions coherent with detected visual relationships, including a novel (table, filled with, food) composition, while tag probabilities align with predicted word categories.The tag sequence distinguishes object, predicate, and none categories during generation.
4 Conclusions
The paper concludes that caption-guided visual relationship graphs, contextual node features, and multi-task generation improve the use of caption semantics in image captioning. Extensive MSCOCO experiments report state-of-the-art performance across multiple evaluation metrics.
- Conclusions: The framework constructs caption-guided visual relationship graphs and enhances their representations with neighboring nodes’ textual and visual features.These components introduce inductive bias to better use caption information.
- Conclusions: Multi-task learning regularizes generation with explicit object and predicate constraints alongside word prediction.The framework jointly models word and object/predicate information during caption generation.
- Conclusions: Extensive MSCOCO experiments show significant improvements over baselines and state-of-the-art performance across various evaluation metrics.The paper identifies additional language-vision modeling tasks as a future extension.