# CaRDiff: Video Salient Object Ranking Chain of Thought Reasoning for Saliency Prediction with Diffusion

arXiv: https://arxiv.org/abs/2408.12009 · HTML: https://mlsanity.com/paper/2408.12009/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 31 of its 44 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)
- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Builds on (most cited first, 30 of 31)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://mlsanity.com/paper/2306.14824/) (2023)
- [Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic](https://mlsanity.com/paper/2306.15195/) (2023)
- [VILA: On Pre-training for Visual Language Models](https://mlsanity.com/paper/2312.07533/) (2023)
- [Caption Anything: Interactive Image Description with Diverse Multimodal Controls](https://mlsanity.com/paper/2305.02677/) (2023)
- [Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model](https://mlsanity.com/paper/1611.09571/) (2016)
- [Unified Image and Video Saliency Modeling](https://mlsanity.com/paper/2003.05477/) (2020)
- [Revisiting Video Saliency: A Large-scale Benchmark and a New Model](https://mlsanity.com/paper/1801.07424/) (2018)
- [SalGAN: Visual Saliency Prediction with Generative Adversarial Networks](https://mlsanity.com/paper/1701.01081/) (2017)
- [TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency Detection](https://mlsanity.com/paper/1908.05786/) (2019)
- [Pink: Unveiling the Power of Referential Comprehension for Multi-modal LLMs](https://mlsanity.com/paper/2310.00582/) (2023)
- [V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning](https://mlsanity.com/paper/2404.12353/) (2024)
- [ViNet: Pushing the limits of Visual Modality for Audio-Visual Saliency Prediction](https://mlsanity.com/paper/2012.06170/) (2020)
- [Text-image Alignment for Diffusion-based Perception](https://mlsanity.com/paper/2310.00031/) (2023)
- [STAViS: Spatio-Temporal AudioVisual Saliency Network](https://mlsanity.com/paper/2001.03063/) (2020)
- [Actions in the Eye: Dynamic Gaze Datasets and Learnt Saliency Models for Visual Recognition](https://mlsanity.com/paper/1312.7570/) (2013)
- [Revisiting Salient Object Detection: Simultaneous Detection, Ranking, and Subitizing of Multiple Salient Objects](https://mlsanity.com/paper/1803.05082/) (2018)
- [Simple vs complex temporal recurrences for video saliency prediction](https://mlsanity.com/paper/1907.01869/) (2019)
- [Spatio-Temporal Self-Attention Network for Video Saliency Prediction](https://mlsanity.com/paper/2108.10696/) (2021)
- [DiffSal: Joint Audio and Video Learning for Diffusion Saliency Prediction](https://mlsanity.com/paper/2403.01226/) (2024)
- [Relative Saliency and Ranking: Models, Metrics, Data, and Benchmarks](https://mlsanity.com/paper/1810.02426/) (2018)
- [Video Saliency Prediction Using Enhanced Spatiotemporal Alignment Network](https://mlsanity.com/paper/2001.00292/) (2020)
- [Learning to Predict Salient Faces: A Novel Visual-Audio Saliency Model](https://mlsanity.com/paper/2103.15438/) (2021)

## Built on by (most cited first, 12 of 12)

- [Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey](https://mlsanity.com/paper/2503.12605/) (2025)
- [Audio-Reasoner: Improving Reasoning Capability in Large Audio Language Models](https://mlsanity.com/paper/2503.02318/) (2025)
- [Contextual Gesture: Co-Speech Gesture Video Generation through Context-aware Gesture Representation](https://mlsanity.com/paper/2502.07239/) (2025)
- [NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results](https://mlsanity.com/paper/2604.14816/) (2026)
- [Scaling Concept With Text-Guided Diffusion Models](https://mlsanity.com/paper/2410.24151/) (2024)
- [Refine-by-Align: Reference-Guided Artifacts Refinement through Semantic Alignment](https://mlsanity.com/paper/2412.00306/) (2024)
- [VITED: Video Temporal Evidence Distillation](https://mlsanity.com/paper/2503.12855/) (2025)
- [Text-Audio-Visual-conditioned Diffusion Model for Video Saliency Prediction](https://mlsanity.com/paper/2504.14267/) (2025)
- [Cut to the Chase: Training-free Multimodal Summarization via Chain-of-Events](https://mlsanity.com/paper/2603.06213/) (2026)
- [Do MLLMs Exhibit Human-like Perceptual Behaviors? HVSBench: A Benchmark for MLLM Alignment with Human Perceptual Behavior](https://mlsanity.com/paper/2412.09603/) (2024)
- [Empowering Multimodal LLMs with External Tools: A Comprehensive Survey](https://mlsanity.com/paper/2508.10955/) (2025)
- [Attend to Anything: Foundation Model for Unified Human Attention Modeling](https://mlsanity.com/paper/2606.03540/) (2026)

## Abstract (arXiv)

Video saliency prediction aims to identify the regions in a video that attract human attention and gaze, driven by bottom-up features from the video and top-down processes like memory and cognition. Among these top-down influences, language plays a crucial role in guiding attention by shaping how visual information is interpreted. Existing methods primarily focus on modeling perceptual information while neglecting the reasoning process facilitated by language, where ranking cues are crucial outcomes of this process and practical guidance for saliency prediction. In this paper, we propose CaRDiff (Caption, Rank, and generate with Diffusion), a framework that imitates the process by integrating a multimodal large language model (MLLM), a grounding module, and a diffusion model, to enhance video saliency prediction. Specifically, we introduce a novel prompting method VSOR-CoT (Video Salient Object Ranking Chain of Thought), which utilizes an MLLM with a grounding module to caption video content and infer salient objects along with their rankings and positions. This process derives ranking maps that can be sufficiently leveraged by the diffusion model to decode the saliency maps for the given video accurately. Extensive experiments show the effectiveness of VSOR-CoT in improving the performance of video saliency prediction. The proposed CaRDiff performs better than state-of-the-art models on the MVS dataset and demonstrates cross-dataset capabilities on the DHF1k dataset through zero-shot evaluation.