# CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning

arXiv: https://arxiv.org/abs/2306.17462 · HTML: https://mlsanity.com/paper/2306.17462/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 30 Jun 2023.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering](https://mlsanity.com/paper/2207.12647/) (2022)
- [Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition](https://mlsanity.com/paper/2009.00210/) (2020)
- [TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning](https://mlsanity.com/paper/2112.03587/) (2021)
- [Cross-modal Knowledge Distillation for Vision-to-Sensor Action Recognition](https://mlsanity.com/paper/2112.01849/) (2021)
- [Causal Reasoning Meets Visual Representation Learning: A Prospective Study](https://mlsanity.com/paper/2204.12037/) (2022)
- [Deep Image-to-Video Adaptation and Fusion Networks for Action Recognition](https://mlsanity.com/paper/1911.10751/) (2019)
- [Masked Images Are Counterfactual Samples for Robust Fine-tuning](https://mlsanity.com/paper/2303.03052/) (2023)
- [Hierarchically Learned View-Invariant Representations for Cross-View Action Recognition](https://mlsanity.com/paper/1809.00421/) (2018)
- [Global Temporal Representation based CNNs for Infrared Action Recognition](https://mlsanity.com/paper/1909.08287/) (2019)
- [Visual Causal Scene Refinement for Video Question Answering](https://mlsanity.com/paper/2305.04224/) (2023)
- [Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition](https://mlsanity.com/paper/1909.08297/) (2019)
- [Urban Regional Function Guided Traffic Flow Prediction](https://mlsanity.com/paper/2303.09789/) (2023)
- [DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback](https://mlsanity.com/paper/2306.07553/) (2023)
- [Towards Causality-Aware Inferring: A Sequential Discriminative Approach for Medical Diagnosis](https://mlsanity.com/paper/2003.06534/) (2020)

## Built on by (most cited first, 5 of 5)

- [Cross-modal Causal Relation Alignment for Video Question Grounding](https://mlsanity.com/paper/2503.07635/) (2025)
- [MEIA: Multimodal Embodied Perception and Interaction in Unknown Environments](https://mlsanity.com/paper/2402.00290/) (2024)
- [Multimodal Sentiment Analysis Based on Causal Reasoning](https://mlsanity.com/paper/2412.07292/) (2024)
- [EG-VQA: Benchmarking Verifiable Video Question Answering with Grounded Temporal Evidence](https://mlsanity.com/paper/2606.24797/) (2026)
- [The Abstraction Gap in Vision-Language Causal Reasoning](https://mlsanity.com/paper/2605.28779/) (2026)

## Abstract (arXiv)

We present CausalVLR (Causal Visual-Linguistic Reasoning), an open-source toolbox containing a rich set of state-of-the-art causal relation discovery and causal inference methods for various visual-linguistic reasoning tasks, such as VQA, image/video captioning, medical report generation, model generalization and robustness, etc. These methods have been included in the toolbox with PyTorch implementations under NVIDIA computing system. It not only includes training and inference codes, but also provides model weights. We believe this toolbox is by far the most complete visual-linguitic causal reasoning toolbox. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to re-implement existing methods and develop their own new causal reasoning methods. Code and models are available at https://github.com/HCPLab-SYSU/CausalVLR. The project is under active development by HCP-Lab's contributors and we will keep this document updated.