# TIPS: Text-Image Pretraining with Spatial awareness

arXiv: https://arxiv.org/abs/2410.16512 · HTML: https://mlsanity.com/paper/2410.16512/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 71 of its 87 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 30 of 71)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [Scaling Vision Transformers](https://mlsanity.com/paper/2106.04560/) (2021)
- [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://mlsanity.com/paper/1804.04235/) (2018)
- [Vision Transformers Need Registers](https://mlsanity.com/paper/2309.16588/) (2023)
- [LRM: Large Reconstruction Model for Single Image to 3D](https://mlsanity.com/paper/2311.04400/) (2023)
- [Florence: A New Foundation Model for Computer Vision](https://mlsanity.com/paper/2111.11432/) (2021)

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

- [Perception Encoder: The best visual embeddings are not at the output of the network](https://mlsanity.com/paper/2504.13181/) (2025)
- [Scaling Language-Free Visual Representation Learning](https://mlsanity.com/paper/2504.01017/) (2025)
- [FG-CLIP 2: A Bilingual Fine-grained Vision-Language Alignment Model](https://mlsanity.com/paper/2510.10921/) (2025)
- [TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment](https://mlsanity.com/paper/2604.12012/) (2026)
- [AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception](https://mlsanity.com/paper/2602.09617/) (2026)
- [Dinomaly2: A Unified Framework for Unsupervised Image Anomaly Detection](https://mlsanity.com/paper/2510.17611/) (2025)
- [VITRIX-CLIPIN: Enhancing Fine-Grained Visual Understanding in CLIP via Instruction Editing Data and Long Captions](https://mlsanity.com/paper/2508.02329/) (2025)
- [What Holds Back Open-Vocabulary Segmentation?](https://mlsanity.com/paper/2508.04211/) (2025)
- [A Mixed Diet Makes DINO An Omnivorous Vision Encoder](https://mlsanity.com/paper/2602.24181/) (2026)
- [Universal CT Representations from Anatomy to Disease Phenotype through Agglomerative Pretraining](https://mlsanity.com/paper/2605.21906/) (2026)
- [Learning Visual Composition through Improved Semantic Guidance](https://mlsanity.com/paper/2412.15396/) (2024)
- [SigLino: Efficient Multi-Teacher Distillation for Agglomerative Vision Foundation Models](https://mlsanity.com/paper/2512.20157/) (2025)
- [Adaptive Global and Fine-Grained Perceptual Fusion for MLLM Embeddings Compatible with Hard Negative Amplification](https://mlsanity.com/paper/2602.05729/) (2026)
- [AlignDiff: Learning Physically-Grounded Camera Alignment via Diffusion](https://mlsanity.com/paper/2503.21581/) (2025)
- [Infusing fine-grained visual knowledge to Vision-Language Models](https://mlsanity.com/paper/2508.12137/) (2025)
- [DAVE: A VLM Vision Encoder for Document Understanding and Web Agents](https://mlsanity.com/paper/2512.17221/) (2025)
- [Revisiting Multi-Task Visual Representation Learning](https://mlsanity.com/paper/2601.13886/) (2026)
- [CLAMP: Contrastive Learning for 3D Multi-View Action-Conditioned Robotic Manipulation Pretraining](https://mlsanity.com/paper/2602.00937/) (2026)
- [SpatialBoost: Enhancing Visual Representation through Language-Guided Reasoning](https://mlsanity.com/paper/2603.22057/) (2026)
- [VIP: Visual-guided Prompt Evolution for Efficient Dense Vision-Language Inference](https://mlsanity.com/paper/2605.12325/) (2026)
- [Efficient Discriminative Joint Encoders for Large Scale Vision-Language Reranking](https://mlsanity.com/paper/2510.06820/) (2025)
- [GranViT: A Fine-Grained Vision Model With Autoregressive Perception For MLLMs](https://mlsanity.com/paper/2510.21501/) (2025)
- [FarSLIP: Discovering Effective CLIP Adaptation for Fine-Grained Remote Sensing Understanding](https://mlsanity.com/paper/2511.14901/) (2025)
- [AdaPerceiver: Transformers with Adaptive Width, Depth, and Tokens](https://mlsanity.com/paper/2511.18105/) (2025)
- [Evaluating Foundation Models' 3D Understanding Through Multi-View Correspondence Analysis](https://mlsanity.com/paper/2512.11574/) (2025)
- [Spatial Reasoning is Not a Free Lunch: A Controlled Study on LLaVA](https://mlsanity.com/paper/2603.12545/) (2026)
- [From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP](https://mlsanity.com/paper/2606.26535/) (2026)
- [CoCo-IR: Contextual Composed Image Retrieval](https://mlsanity.com/paper/2608.05149/) (2026)
- [Gen2Physics: Grounding Generated 3D Meshes in Physics via Multi-View Material Decomposition](https://mlsanity.com/paper/2608.23869/) (2026)
- [VESSA: Video-based objEct-centric Self-Supervised Adaptation for Visual Foundation Models](https://mlsanity.com/paper/2510.20994/) (2025)

## Abstract (arXiv)

While image-text representation learning has become very popular in recent years, existing models tend to lack spatial awareness and have limited direct applicability for dense understanding tasks. For this reason, self-supervised image-only pretraining is still the go-to method for many dense vision applications (e.g. depth estimation, semantic segmentation), despite the lack of explicit supervisory signals. In this paper, we close this gap between image-text and self-supervised learning, by proposing a novel general-purpose image-text model, which can be effectively used off the shelf for dense and global vision tasks. Our method, which we refer to as Text-Image Pretraining with Spatial awareness (TIPS), leverages two simple and effective insights. First, on textual supervision: we reveal that replacing noisy web image captions by synthetically generated textual descriptions boosts dense understanding performance significantly, due to a much richer signal for learning spatially aware representations. We propose an adapted training method that combines noisy and synthetic captions, resulting in improvements across both dense and global understanding tasks. Second, on the learning technique: we propose to combine contrastive image-text learning with self-supervised masked image modeling, to encourage spatial coherence, unlocking substantial enhancements for downstream applications. Building on these two ideas, we scale our model using the transformer architecture, trained on a curated set of public images. Our experiments are conducted on 8 tasks involving 16 datasets in total, demonstrating strong off-the-shelf performance on both dense and global understanding, for several image-only and image-text tasks. Code and models are released at https://github.com/google-deepmind/tips.