# Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations

arXiv: https://arxiv.org/abs/2305.06152 · HTML: https://mlsanity.com/paper/2305.06152/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 42 of its 52 references that are in the map.
- First posted on arXiv 6 May 2023.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 30 of 42)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [PaLI: A Jointly-Scaled Multilingual Language-Image Model](https://mlsanity.com/paper/2209.06794/) (2022)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [SimVLM: Simple Visual Language Model Pretraining with Weak Supervision](https://mlsanity.com/paper/2108.10904/) (2021)
- [When and why vision-language models behave like bags-of-words, and what to do about it?](https://mlsanity.com/paper/2210.01936/) (2022)
- [Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality](https://mlsanity.com/paper/2204.03162/) (2022)
- [Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks](https://mlsanity.com/paper/2208.10442/) (2022)
- [Scene Graph Generation by Iterative Message Passing](https://mlsanity.com/paper/1701.02426/) (2017)
- [Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training](https://mlsanity.com/paper/1908.06066/) (2019)
- [Neural Motifs: Scene Graph Parsing with Global Context](https://mlsanity.com/paper/1711.06640/) (2017)
- [Unifying Vision-and-Language Tasks via Text Generation](https://mlsanity.com/paper/2102.02779/) (2021)
- [Image Generation from Scene Graphs](https://mlsanity.com/paper/1804.01622/) (2018)
- [Graph R-CNN for Scene Graph Generation](https://mlsanity.com/paper/1808.00191/) (2018)
- [Learning to Compose Dynamic Tree Structures for Visual Contexts](https://mlsanity.com/paper/1812.01880/) (2018)
- [Auto-Encoding Scene Graphs for Image Captioning](https://mlsanity.com/paper/1812.02378/) (2018)
- [UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning](https://mlsanity.com/paper/2012.15409/) (2020)
- [ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph](https://mlsanity.com/paper/2006.16934/) (2020)

## Built on by (most cited first, 30 of 37)

- [CF-VLM:CounterFactual Vision-Language Fine-tuning](https://mlsanity.com/paper/2506.17267/) (2025)
- [Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey](https://mlsanity.com/paper/2402.05391/) (2024)
- [Enhancing Multimodal Compositional Reasoning of Visual Language Models with Generative Negative Mining](https://mlsanity.com/paper/2311.03964/) (2023)
- [Enhancing Vision-Language Compositional Understanding with Multimodal Synthetic Data](https://mlsanity.com/paper/2503.01167/) (2025)
- [Enhancing Vision-Language Models with Scene Graphs for Traffic Accident Understanding](https://mlsanity.com/paper/2407.05910/) (2024)
- [Neural Brain: A Neuroscience-inspired Framework for Embodied Agents](https://mlsanity.com/paper/2505.07634/) (2025)
- [Decoupled Global-Local Alignment for Improving Compositional Understanding](https://mlsanity.com/paper/2504.16801/) (2025)
- [ViewSRD: 3D Visual Grounding via Structured Multi-View Decomposition](https://mlsanity.com/paper/2507.11261/) (2025)
- [Graph-Based Captioning: Enhancing Visual Descriptions by Interconnecting Region Captions](https://mlsanity.com/paper/2407.06723/) (2024)
- [CLIP-UP: CLIP-Based Unanswerable Problem Detection for Visual Question Answering](https://mlsanity.com/paper/2501.01371/) (2025)
- [Beyond Accuracy: Ensuring Correct Predictions With Correct Rationales](https://mlsanity.com/paper/2411.00132/) (2024)
- [Causal Graphical Models for Vision-Language Compositional Understanding](https://mlsanity.com/paper/2412.09353/) (2024)
- [EvdCLIP: Improving Vision-Language Retrieval with Entity Visual Descriptions from Large Language Models](https://mlsanity.com/paper/2505.18594/) (2025)
- [CLIP-Powered TASS: Target-Aware Single-Stream Network for Audio-Visual Question Answering](https://mlsanity.com/paper/2405.07451/) (2024)
- [DGTRSD & DGTRS-CLIP: A Dual-Granularity Remote Sensing Image-Text Dataset and Vision Language Foundation Model for Alignment](https://mlsanity.com/paper/2503.19311/) (2025)
- [Visual Perturbation and Adaptive Hard Negative Contrastive Learning for Compositional Reasoning in Vision-Language Models](https://mlsanity.com/paper/2505.15576/) (2025)
- [Enhancing Compositional Reasoning in CLIP via Reconstruction and Alignment of Text Descriptions](https://mlsanity.com/paper/2510.16540/) (2025)
- [ABE-CLIP: Training-Free Attribute Binding Enhancement for Compositional Image-Text Matching](https://mlsanity.com/paper/2512.17178/) (2025)
- [LLM-enhanced Action-aware Multi-modal Prompt Tuning for Image-Text Matching](https://mlsanity.com/paper/2506.23502/) (2025)
- [UMCL: Unimodal-generated Multimodal Contrastive Learning for Cross-compression-rate Deepfake Detection](https://mlsanity.com/paper/2511.18983/) (2025)
- [Aligning Forest and Trees in Images & Long Captions for Visually Grounded Understanding](https://mlsanity.com/paper/2602.02977/) (2026)
- [UrbanGraphEmbeddings: Learning and Evaluating Spatially Grounded Multimodal Embeddings for Urban Science](https://mlsanity.com/paper/2602.08342/) (2026)
- [What CLIP Knows but Cannot Say: Recovering Negation from Frozen Intermediate Features](https://mlsanity.com/paper/2607.23271/) (2026)
- [A New Method to Capturing Compositional Knowledge in Linguistic Space](https://mlsanity.com/paper/2412.15632/) (2024)
- [Towards Human Cognition: Visual Context Guides Syntactic Priming in Fusion-Encoded Models](https://mlsanity.com/paper/2502.17669/) (2025)
- [Dynamic Relation Inference via Verb Embeddings](https://mlsanity.com/paper/2503.13021/) (2025)
- [Preserve and Sculpt: Manifold-Aligned Fine-tuning of Vision-Language Models for Few-Shot Learning](https://mlsanity.com/paper/2508.12877/) (2025)
- [MSGFusion: Multimodal Scene Graph-Guided Infrared and Visible Image Fusion](https://mlsanity.com/paper/2509.12901/) (2025)
- [SLIP: Structural-aware Language-Image Pretraining for Vision-Language Alignment](https://mlsanity.com/paper/2511.03019/) (2025)
- [SCLARO: A Dataset for Grounded Scenario-Level Scene Understanding and ScenarioCLIP for Benchmarking](https://mlsanity.com/paper/2511.20274/) (2025)

## Abstract (arXiv)

Large-scale vision-language pre-training has achieved significant performance in multi-modal understanding and generation tasks. However, existing methods often perform poorly on image-text matching tasks that require structured representations, i.e., representations of objects, attributes, and relations. As illustrated in Fig.~reffig:case (a), the models cannot make a distinction between ``An astronaut rides a horse" and ``A horse rides an astronaut". This is because they fail to fully leverage structured knowledge when learning representations in multi-modal scenarios. In this paper, we present an end-to-end framework Structure-CLIP, which integrates Scene Graph Knowledge (SGK) to enhance multi-modal structured representations. Firstly, we use scene graphs to guide the construction of semantic negative examples, which results in an increased emphasis on learning structured representations. Moreover, a Knowledge-Enhance Encoder (KEE) is proposed to leverage SGK as input to further enhance structured representations. To verify the effectiveness of the proposed framework, we pre-train our model with the aforementioned approaches and conduct experiments on downstream tasks. Experimental results demonstrate that Structure-CLIP achieves state-of-the-art (SOTA) performance on VG-Attribution and VG-Relation datasets, with 12.5% and 4.1% ahead of the multi-modal SOTA model respectively. Meanwhile, the results on MSCOCO indicate that Structure-CLIP significantly enhances the structured representations while maintaining the ability of general representations. Our code is available at https://github.com/zjukg/Structure-CLIP.