TIPS: Text-Image Pretraining with Spatial awareness

## Abstract

Abstract 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 8 8 8 tasks involving 16 16 16 16 datasets in total, demonstrating strong off-the-shelf performance on both dense and global understanding, for several image-only and image-text tasks.

## 1 Introduction

The quest for effective image representations has permeated much of the research work in computer vision over the past two decades: starting with hand-crafted techniques such as SIFT (Lowe, ) and HOG (Dalal & Triggs, ), then moving into the deep learning era with supervised (Krizhevsky et al., ; He et al., ; Dosovitskiy et al., ), weakly-supervised (Radford et al., ; Mahajan et al., ) or self-supervised (Chen et al., ; Caron et al., ; He et al., ) techniques.
Most computer vision tasks critically depend on capable image encodings, and for this reason the holy grail in image representation learning is a generic model that can be used off the shelf for a variety of downstream tasks.

One of the most promising directions for such representation learning research is on leveraging noisy textual supervision, which is abundant on the web, as introduced by CLIP (Radford et al., ) and ALIGN (Jia et al., ).
Recent methods (Zhai et al., ; Sun et al., ) have also made progress on image-text learning. However, these techniques have for the most part not been successful at dense image prediction tasks, such as depth estimation or semantic segmentation.
On the other hand, self-supervised learning techniques (Caron et al., ; Zhou et al., ), although lacking semantic signals to guide the training, can enforce feature consistency between distorted images or spatially adjacent patches, and result in effective pretraining techniques for dense image understanding (Oquab et al., ). These methods, however, do not use any text for training, and are by nature limited to vision-only tasks.
In this work, we build on top of both the image-text and self-supervised learning paradigms, to develop general-purpose image representations which can be used for a variety of image-only and image-text tasks.
We build strong representations for dense prediction tasks, such as segmentation and depth estimation, and global prediction tasks that reason about the image as a whole, such as image classification and image-text retrieval.
We illustrate our method in Fig. 1.

Figure: Figure 1: We introduce TIPS: Text-Image Pretraining with Spatial awareness. TIPS is a general-purpose image-text encoder model, which can be effectively used for dense and global understanding, in vision-only or vision-language tasks.
Refer to caption: x1.png

We address the limitations of image-text learning, which hinder its applicability to dense spatial understanding tasks, with two simple and effective ideas that improve weak supervision and incentivize image features to become spatially coherent:
(1) Enhancing the textual labels via automated generation of image captions, leveraging a recent multimodal generative model (Beyer et al., ).
Such synthetic image captions tend to describe the visual contents more comprehensively than the noisy captions mined from the web, by capturing all the objects in the scene and their spatial relationships, being a rich supervision signal for dense understanding.
However, at the same time, noisy web captions often contain fine-grained details which can be helpful for global understanding tasks (as discussed in Fig. 3).
Thus, we devise an effective method to train our model with both noisy and synthetic captions, with separate image-text contrastive losses for them, to achieve strong performance on both dense and global tasks.
(2) Encouraging the learned image features to be spatially coherent, inspired by lessons from the self-supervised literature.
We incorporate self-distillation and masked image modeling (MIM) into the image-text learning framework, with carefully designed adaptations, resulting in substantial improvements for many applications, especially the dense ones.

Finally, we build on these ideas to scale a Vision Transformer (Dosovitskiy et al., ) with text alignment on a training dataset of $117$M public images, leveraging their noisy web captions and synthetically-generated ones.
Our method showcases spatial understanding and textual alignment in the same model, essentially combining the strengths of the image-text and self-supervised literature.
We refer to our method as Text-Image Pretraining with Spatial awareness (TIPS), and thoroughly evaluate it across many downstream tasks.
Specifically, we demonstrate that TIPS achieves strong and competitive performance off-the-shelf across $8$ computer vision tasks involving $16$ datasets in total, comprising image-only or image-text evaluations, for dense or image-level predictions.
We hope that our findings will inspire the community towards the development of next-generation image representations, to enable multimodal and spatially grounded applications.

## 2 Related Work

General-purpose image representation models have been proposed for computer vision tasks, generally leveraging self-supervised or weakly-supervised learning.
Recent self-supervised techniques include DINO (Caron et al., ), MAE (He et al., ), iBOT (Zhou et al., ), I-JEPA (Assran et al., ) and the scaled-up DINOv2 (Oquab et al., ), which employs a large curated dataset.
Our work differs from self-supervised approaches by learning with readily-available and public textual captions, which makes the model more capable as it can handle language inputs.
Weakly-supervised learning of image representations generally leverages noisy textual captions, with early examples coming from Joulin et al. (); Mahajan et al. ().
Modern approaches include CLIP (Radford et al., ),
ALIGN (Jia et al., ),
CoCa (Yu et al., ), OpenCLIP (Cherti et al., ), SigLIP (Zhai et al., ), Florence (Yuan et al., ), InternVL (Chen et al., ) and the EVA series (Fang et al., ; ; Sun et al., ).
Different from these image-text techniques, we design TIPS to offer frozen pretrained image features that are directly useful to a broad range of downstream vision tasks, without model fine-tuning.
Generally, existing image-text models do not focus much on downstream dense prediction tasks with frozen features.
Additionally, our technique differs from these by enhancing the core contrastive training common to all of these methods to obtain spatially-coherent representations, via improved captions and loss functions.

Image-text learning for dense understanding tasks.
While the above-mentioned existing image-text learning approaches lead to powerful representations, they have not demonstrated clear benefits for dense image prediction tasks.
As a consequence, today the models learned with self-supervised techniques are preferred for these cases: for example, the recent DepthAnything (Yang et al., ) model is built on top of the self-supervised DINOv2 features, even though weakly-supervised image backbones are widely available.
Similarly, Tong et al. () have demonstrated shortcomings of CLIP-style models and incorporated DINOv2 to enhance the visual grounding of multimodal models.
However, recent work has adapted image-text learning for dense prediction, e.g. for open-vocabulary detection (Kim et al., ; Minderer et al., ; Rao et al., ) and segmentation (Mukhoti et al., ; Wu et al., ; Wysoczanska et al., ).
SLIP (Mu et al., ) combines an adapted SimCLR (Chen et al., ) self-supervised objective with CLIP for classification tasks.
Closer to our work, the very recent SILC method (Naeem et al., ) proposes to combine contrastive image-text training with self-distillation.
Different from our goals, most of these methods are specifically tailored towards improving vision-language tasks that require spatial understanding and do not aim to learn a general-purpose vision encoder.
Besides, most of these do not incorporate dense objectives during pretraining, and may require additional fine-tuning stages or dense supervision, which can be costly.
SILC proposes to incorporate self-supervised losses into image-text training, but only employs self-distillation; in contrast, our TIPS technique goes beyond to employ a more sophisticated self-supervised approach leveraging masked image modeling with an online tokenizer, which we show to boost performance significantly for dense image prediction.
FLIP (Li et al., ) proposed to combine contrastive learning with masking, but without any reconstruction loss, aiming only at efficient language-image training.
Altogether, our approach demonstrates for the first time a way to learn image-text models whose vision representations rival those of self-supervised approaches in dense image understanding tasks.

Synthetic data for image representation learning.
One of our contributions is to show the power of synthetic textual captions for image representation learning, in particular for dense prediction.
Previous work has explored the training of visual representation models with synthetic data, especially with synthetic images (Ren & Lee, ; Tian et al., ; Sariyildiz et al., ), which may be generated based on LLM captions (Tian et al., ; Hammoud et al., ).
CapsFusion (Yu et al., ) leverages synthetic captions to improve large multimodal models for generative text applications.
More similar to our work, VeCLIP (Lai et al., ) and LaCLIP (Fan et al., ) generate synthetic captions for contrastive image-text training.
However, their methods are only applied to global image understanding tasks such as image retrieval and classification, and there is no consideration related to dense prediction.
In contrast, we reveal for the first time the power of synthetic captions to improve spatial understanding in image-text models.
Additionally, we propose a new learning method to combine noisy web captions with synthetic descriptions, by introducing an additional vision transformer class token to better leverage synthetic descriptions, boosting performance in many tasks.
Our technique goes beyond the sampling or multi-text caption combination strategies proposed in LaCLIP, enabling more flexible learning with two image-level tokens which focus on different characteristics.

## 3 TIPS

Figure: Figure 2: Block diagram of TIPS. From bottom to top: given an input image, we produce masked and cropped augmentations, along with synthetic descriptive captions from a captioner model. They are fed into the text and image encoders, along with the noisy web caption, and the output tokens are used in the losses. The contrastive loss makes use of the two captions, aligning them with two [CLS] tokens obtained from the image encoder. TIPS also employs self-distillation applied to the local crops and a masked image modeling loss applied to dense patch tokens, which encourage spatially-aware and discriminative image representations.
Refer to caption: x2.png

Our goal is to create a general-purpose image representation model, with text alignment, which can be used off-the-shelf for dense and global vision tasks.
While image-text contrastive techniques (Radford et al., ; Jia et al., ) can effectively model global image information, they tend to underperform for dense understanding tasks, where self-supervised models are the method of choice today (Oquab et al., ).
To bridge this gap, we propose Text-Image Pretraining with Spatial awareness (TIPS), illustrated in Fig. 2, which leverages enhanced weak supervision via synthetic image captions, as well as self-supervised masked modeling, improving image feature quality significantly, for both dense and global understanding.

Problem setup.
Given a collection of image-text pairs $\{(I_{k},T_{k})\}$, where $T_{k}$ is a noisy textual caption for image $I_{k}$, we aim to learn a model which encodes images into dense and global embeddings that are useful to a variety of multimodal tasks.
More concretely, we set out to train the function $f$, mapping image $I$ to a set of image embeddings $\{\mathbf{e}^{g},\mathbf{e}_{1},\mathbf{e}_{2},\ldots,\mathbf{e}_{N}\}$, where $\mathbf{e}^{g}$ is the global embedding representation of the entire image and $\{\mathbf{e}_{n}\}_{n=1}^{N}$ are patch embeddings corresponding to different image regions.
The text associated with the images can be leveraged to train a semantically meaningful joint embedding space, leading to useful image features.
We build on top of the standard CLIP method (Radford et al., ), which learns a text encoder $g$, mapping $T$ to its embedding $\mathbf{e}^{t}$, by pushing $\mathbf{e}^{g}$ and $\mathbf{e}^{t}$ close for corresponding images and captions, and far otherwise.
CLIP uses a cross-entropy loss with softmax normalization of cosine similarities, referred to as InfoNCE (van den Oord et al., ), which we denote $\mathcal{L}_{CLIP}$.
In this work, we model $f$ as a Vision Transformer (ViT) (Dosovitskiy et al., ) and obtain the image embeddings from the final layer’s feature map, with $\mathbf{e}^{g}$ corresponding to the [CLS] token.
The function $g$ is modeled as a standard transformer (Vaswani et al., ).

### 3.1 Enhancing Weak Supervision with Synthetic Image Captions

A limitation of standard image-text learning using large-scale web data is the quality of the captions, which are noisy and may not accurately describe images.
An example is shown in Fig. 3 (top), where the words “for sale dealership $30k” are not describing the image contents.
While this may hinder model learning, the caption still captures the main object (‘2007 Cadillac Escalade’).

Figure: Figure 3: Example [web image (CC BY-SA 2.0)](https://commons.wikimedia.org/wiki/File:Cadillac_Escalade07.jpg) with noisy caption (top) and synthetic caption by PaliGemma (Beyer et al., ) (bottom).
Refer to caption: x3.png

However, a deeper issue we commonly observe is that these captions often only mention salient objects, without describing their arrangement in the scene.
In other words, the captions usually serve as noisy image-level supervision and generally tend to be of limited use for learning spatially-aware representations.
This motivates us to investigate automated generation of synthetic captions, which could serve as useful pre-training weak supervision for dense tasks.

We employ off-the-shelf, publicly available models which can caption images effectively: given the image $I$, we generate text $\hat{T}$.
In particular, we leverage captioning models which tend to generate accurate and high-level image descriptions – an example is given in Fig. 3 (bottom).
Note the use of the preposition “in front of", which indicates the spatial arrangement of the scene, the description of the background (“building") and the color of the object (“black"), all combined providing rich signals for dense image representation learning.

However, a drawback of the synthetically generated captions is their lack of detailed object semantics.
Referring again to Fig. 3, the synthetically-generated caption misses information of the specific car model (2007 Cadillac Escalade), which can be helpful to learn discriminative representations.
For this reason, we propose to combine the original $T$ and synthetic $\hat{T}$ captions, to aim for globally discriminative and spatially-aware image features.

Dual image-text embedding.
We strive to leverage relevant information from both captions, and thus propose to modify the vision transformer $f$ to learn from them, in an approach we call “dual embedding”.
We insert an additional [CLS] token in the model, to be used for learning with the synthetic caption, obtaining an additional global embedding $\mathbf{\hat{e}}^{g}$.
At training time, we feed both $T$ and $\hat{T}$ into the text encoder, to obtain their text embeddings $\mathbf{e}^{t}$ and $\mathbf{\hat{e}}^{t}$.
In addition to the $\mathcal{L}_{CLIP}$ loss between $\mathbf{e}^{g}$ and $\mathbf{e}^{t}$, we compute $\hat{\mathcal{L}}_{CLIP}$ between $\mathbf{\hat{e}}^{g}$ and $\mathbf{\hat{e}}^{t}$.
This introduces flexibility in the model to learn an object-centric image embedding in $\mathbf{e}^{g}$, and a more spatially-aware image embedding in $\mathbf{\hat{e}}^{g}$.
Both back-propagate into the dense feature maps to learn improved patch embeddings $\{\mathbf{e}_{n}\}_{n=1}^{N}$.
At inference time, the model can have access to both types of global image embeddings, and the one to use may be decided based on the downstream task: generally spatially aware tasks will use $\mathbf{\hat{e}}^{g}$ while object-centric ones will employ $\mathbf{e}^{g}$.

### 3.2 Integrating Self-Distillation and Masking to Boost Image Features

In addition to improving training data quality and learning with different types of textual supervision, we propose to incentivize the model to learn spatially-aware representations via dedicated loss functions.
We are inspired by recent self-supervised learning techniques, which produce features suitable to dense downstream tasks (Caron et al., ; Zhou et al., ; Oquab et al., ).
We incorporate self-distillation and masking losses in our training setup, adapting them to work in a weakly-supervised image-text learning framework.
Building on top of CLIP, we introduce a teacher ViT model, $f_{t}$, to help guide the training process, which processes the full image $I$.
The teacher’s weights are updated by Exponential Moving Average (EMA) of the main (student) ViT, $f_{s}=f$, as per (He et al., ).
Two additional loss terms are introduced, as described next.

Self-distillation loss.
We create $M$ local crops from the input image $I$, which are processed by $f_{s}$, to obtain $M$ local crop embeddings via their [CLS] tokens, $\{\mathbf{e}^{g,m}\}_{m=1}^{M}$.
During training, we enforce these embeddings to match predictions of the teacher’s [CLS] token, $\mathbf{e}^{g,t}$, which is obtained from a forward pass of $I$ through $f_{t}$.
This incentivizes the model to learn representations which are consistent across the local crops and the original (global) image.
The embeddings are used to compute prototype scores using an MLP-based projection head, on top of which softmax normalization and cross-entropy loss are applied:

$$ $\mathcal{L}_{distill}=-\sum_{b}\sum_{m}\mathrm{softmax}((\mathbf{p}_{b}^{t}- \mathbf{c})/\tau_{t})\,\mathrm{log}(\mathrm{softmax}(\mathbf{p}_{b}^{m}/\tau_{ s}))$ (1) $$

where $b$ iterates over the images in the batch. $\mathbf{p}^{t}=\mathrm{P_{t}}(\mathbf{e}^{g,t})$ and $\mathbf{p}^{m}=\mathrm{P_{s}}(\mathbf{e}^{g,m})$ correspond to the teacher’s and student’s prototype scores respectively, which are computed with the teacher and student projections, $P_{t}$ and $P_{s}$, where $P_{t}$ is updated with an EMA of $P_{s}$.
$\tau_{t}$ and $\tau_{s}$ correspond to the teacher’s and student’s temperatures, used for sharpening the scores, and $\mathbf{c}$ to a centering variable which is updated with an EMA of $\mathbf{p}_{b}^{t}$, to encourage a uniform distribution.

Masking loss.
We introduce a masked image modeling loss in order to encourage the learned patch embeddings to understand their spatial surroundings.
The high-level idea is to have the visible patch representations recover the semantics of the masked patches.
More concretely, we feed a masked version of $I$ through $f_{s}$, where the masked patches are replaced by mask tokens, $\{\mathbf{m}_{n}\}_{n}$.
The encoded mask tokens, $\{\mathbf{e}_{n}^{\mathbf{m}}\}_{n}$, are then projected to prototype scores and compared to the teacher’s corresponding unmasked tokens, $\{\mathbf{e}^{t}_{n}\}_{n}$, similarly to Eq. 1:

$$ $\mathcal{L}_{mask}=-\sum_{b}\sum_{n}\mathrm{softmax}((\mathbf{p}_{b,n}^{t}- \mathbf{c^{\prime}})/\tau_{t}^{\prime})\,\mathrm{log}(\mathrm{softmax}(\mathbf {p}_{b,n}^{\mathbf{m}}/\tau_{s}^{\prime}))$ (2) $$

where, again, $b$ iterates over the batch. $\mathbf{p}_{n}^{t}=\mathrm{P^{\prime}_{t}}(\mathbf{e}^{t}_{n})$ and $\mathbf{p}_{n}^{\mathbf{m}}=\mathrm{P^{\prime}_{s}}(\mathbf{e}_{n}^{\mathbf{m}})$ correspond respectively to the teacher’s and student’s prototype scores for patch $n$, which are computed with the teacher and student projections, $P^{\prime}_{t}$ and $P^{\prime}_{s}$, where $P^{\prime}_{t}$ is updated with an EMA of $P^{\prime}_{s}$.
Similarly as before, $\tau_{t}^{\prime}$ and $\tau_{s}^{\prime}$ correspond to the teacher’s and student’s temperatures and $\mathbf{c}^{\prime}$ to the centering variable.

The total loss for our method is then: $\mathcal{L}_{total}=\frac{1}{2}(\mathcal{L}_{CLIP}+\hat{\mathcal{L}}_{CLIP})+
\alpha\mathcal{L}_{distill}+\beta\mathcal{L}_{mask}$.

Discussion.
Our method builds on representation learning ideas from the weakly and self-supervised literature, and to the best of our knowledge is the first to demonstrate that combining contrastive image-text learning with self-distillation and masked image modeling leads to improvements across many tasks, indicating positive synergies between these objectives.
The closest existing technique is SILC (Naeem et al., ), which combined CLIP with self-distillation.
As we show in experimental ablations, though, the addition of the masked image loss substantially improves performance across dense tasks, being critical for downstream applications.
We note some key differences compared to previous methods.
Given that we use a CLIP loss, the self-supervised components can be simplified, compared to the original formulations in DINO (Caron et al., ) and iBOT (Zhou et al., ).
A major difference is that we use a single global “crop", instead of two in DINO, iBOT and SILC, substantially increasing throughput by $25\%$.
In contrast to many self-supervised methods, we use comparatively simple data augmentations: the local crop is just a random crop of the original image, and the global crop is a larger random crop with horizontal flips.
This is similar to Assran et al. (); Moutakanni et al. () who argue that complex augmentations may not be necessary for representation learning.
Finally, our masking approach is simply random, in contrast to blockwise in iBOT.

### 3.3 Scaling TIPS

We aim at creating a highly-capable and general-purpose model, and for this reason it is critical to scale it to a large model architecture and training dataset, aiming at enhanced image representations.

Model.
The ViT architecture has been shown to scale well to billion-sized models in a variety of tasks (Zhai et al., ; Oquab et al., ; Chen et al., ).
We scale our TIPS model to the ViT-g architecture, with patch size $14$, and use the SwiGLU (Shazeer, ) feed-forward network variant.
Similar to Oquab et al. (), we adapt the embedding dimension to $1536$ with $24$ heads.
This makes our image encoder directly comparable to DINOv2-g, counting $1.1$B parameters in total.
On the text side, we scale the transformer to $12$ layers, with the same embedding dimension and number of heads as the image encoder.

Data.
We leverage the WebLI dataset (Chen et al., ), which is a large and noisy web dataset of public images and associated alt-text containing $10$B image-text pairs.
We filter the dataset in successive rounds in order to enhance its quality for model training, similar to previous work in language (Gunasekar et al., ; Wenzek et al., ) and vision (Oquab et al., ; Parthasarathy et al., ).
This is critical for our model, since it is intended for off-the-shelf use in many downstream applications.
First, similar to Schuhmann et al. (), we filter the image-text pairs based on their contents, by discarding those whose image-text similarities are low, as computed by a pretrained alignment model.
Second, we filtered the resulting dataset to only keep pairs with English captions.
These two initial steps result in a dataset of $1.7$B images.
Finally, we follow a similar curation process as previous work (Oquab et al., ; Parthasarathy et al., ) and select images that are similar enough to those in curated datasets, leveraging a pretrained model to compute image embeddings; further details on the curated datasets and filtering strategies are detailed in Appendix A.3.
Note that we also remove near-duplicate images from our dataset if they appeared in any of the evaluation datasets used in this paper.
This process generates our main curated pretraining dataset, containing $116$M image-text pairs in total.

## 4 Experiments

### 4.1 Experimental Setup

Evaluation datasets and protocols.
Our models are evaluated on a suite of $8$ tasks involving $16$ datasets in total, comprising images-only or images-and-text tasks.
We assess the quality of the learned representations thoroughly in a wide range of conditions, covering indoor/outdoor scenes and object-centric captures.
Note that, in all evaluations, our image-text representations are kept frozen, since our goal is to assess their applicability as off-the-shelf feature extractors.
We evaluate $3$ dense prediction tasks, $2$ holistic global image understanding tasks and $3$ multimodal retrieval tasks.
We introduce the tasks below, and provide further details about their evaluation protocols in the appendix (Section A.4).

Semantic segmentation is a dense task evaluated on the PASCAL (Everingham et al., ) and ADE20k (Zhou et al., ) datasets, using mean Intersection over Union (mIoU). We use a simple linear probe setup similar to (Oquab et al., ), where classes are predicted from the spatial features.

Monocular depth estimation aims to predict the depth value for each pixel on the image. We benchmark depth estimation on the scene-centric NYUv2 (Silberman et al., ), and the object-centric NAVI (Jampani et al., ), and we use the RMSE metric.
For NYUv2, we use a linear probe setup similar to (Oquab et al., ), where patch tokens are concatenated to the global embedding, on top of which a linear classifier predicts among $256$ quantized depth values.
For NAVI, we follow (El Banani et al., ) and probe with the DPT (Ranftl et al., ) decoder.

Surface normal estimation is the task of densely predicting the 3D surface normal direction of each pixel, and is also assessed using NYUv2 and NAVI. We use both datasets with the setup of (El Banani et al., ), and report angular RMSE.

Image classification is evaluated on the ImageNet-$1$K dataset (Russakovsky et al., ), where we consider K-Nearest-Neighbor (KNN) and linear probe evaluations on top of the learned features. We report top-1 accuracy.

Fine-grained and instance-level retrieval is evaluated leveraging the Universal Embeddings Dataset (UnED) (Ypsilantis et al., ), which itself is a benchmark combining datasets from $8$ domains: food (Food2k dataset, Min et al. ()), cars (CARS196 dataset, Krause et al. ()), online products (SOP dataset, Song et al. ()), clothing (InShop dataset, Liu et al. ()), natural world (iNat dataset, Van Horn et al. ()), artworks (Met dataset, Ypsilantis et al. ()), landmarks (GLDv2 dataset, Weyand et al. ()) and retail products (Rp2k dataset, Peng et al. ()).
We report the average recall@$1$ (R@$1$) over the $8$ domains, and per-domain results in the appendix.

Image-to-text (I$\rightarrow$T) retrieval is assessed using the Flickr$30$K (Young et al., ), DOCCI (Onoe et al., ) and COCO (Chen et al., ) datasets, also reporting the R@$1$ metric.

Text-to-image (T$\rightarrow$I) retrieval is similarly assessed using Flickr$30$K, DOCCI and COCO, with the R@$1$ metric.

Zero-shot classification is conducted on ImageNet-$1$K by retrieving the class text embedding closest to the each test image’s embedding, following  (Radford et al., ), and using top-1 accuracy.

Additional training dataset details.
As discussed in Sec. 3.3, we use images from a set of curated datasets as queries for mining among a large pool of web images.
Following the steps adopted by DINOv2 (Oquab et al., ), we use the training sets of some of our evaluation datasets as the curated queries, details provided in the appendix.
This leads to a web-based training dataset with $116$M image-text pairs.
Additionally, for the scaling ViT-g experiment, we add the training set of the Mapillary SLS dataset (Warburg et al., ) as-is to our training set to compensate for the lack of street-level imagery in web images, and in the absence of any alt-text we use the generated synthetic caption for training both CLS tokens. This increases the number of images in our training set to $117$M in total.
A similar procedure is conducted by DINOv2 for their LVD-$142$M dataset.

Implementation details.
For all our models, we use $1$ global crop at resolution $224$ and $M=6$ local crops at resolution $98$. We train the ViT-B models for $70$ epochs at batch size $16$k, which takes $4$ days on $256$ TPUv3 chips. For the larger ViT-g model we train for $15$ epochs at batch size $16$k, which takes $2$ days on $512$ TPUv5 chips, and results in our low-res model (TIPS-g/14 LR). For our high-res variant (TIPS-g/14 HR), we add an additional finetuning stage with global crops at resolution $448$ and local crops at resolution $140$, which runs for $0.1$ epochs at batch size $4$k.
We use only random resize crops and horizontal flips as image augmentations.
Other details can be found in Sec. A.2.

Captioner model.
We leverage the recent PaliGemma (Beyer et al., ) model for image captioning. Specifically, we use the version fine-tuned on COCO, with the $224$ image size version used for the core pretraining run and the $448$ version for the short high-resolution fine-tuning stage.

Compared techniques.
We strive to provide a large number of comparisons against recent work.
For each existing model family, we compare against the largest instantiation up to ViT sizes “g” or “G”, at about $1.8$B parameters or less in the image encoder.
We benchmark our method against a wide range of methods, from the self-supervised, weakly-supervised and supervised literature.
All methods are used off-the-shelf, with frozen weights, for fair comparisons.
As self-supervised methods, we compare against DINO (Caron et al., ), MAE (He et al., ), iBOT (Zhou et al., ) and DINOv2 (Oquab et al., ).
As weakly-supervised methods, we compare against CLIP (Radford et al., ), OpenCLIP (Cherti et al., ), SigLIP (Zhai et al., ), SILC (Naeem et al., ) and EVA-CLIP (Sun et al., ).
As a supervised method, we benchmark against the ViT-g trained on JFT-$3$B, as per (Zhai et al., ).

### 4.2 Results

**Table 1: Ablations for enhanced captions and improved losses, using the ViT-B backbone on $5$ representative dense, global and image-text tasks. Our final method presented in (D) achieves large gains in all tasks compared to the baseline CLIP shown in (A).**
| Method | $\uparrow$ Segmentation | $\downarrow$ Depth | $\uparrow$ KNN classif. | $\uparrow$ I$\rightarrow$T retrieval | $\uparrow$ T$\rightarrow$I retrieval |
| --- | --- | --- | --- | --- | --- |
| (PASCAL) | (NYUv2) | (ImageNet) | (Flickr) | (Flickr) |  |
| (A) Baseline |  |  |  |  |  |
| CLIP (noisy captions) | 64.4 | 0.620 | 76.9 | 79.1 | 62.9 |
| (B) CLIP using synthetic captions |  |  |  |  |  |
| PaliGemma captions | 74.5 | 0.544 | 70.0 | 79.8 | 60.1 |
| Both captions, sampled | 71.8 | 0.563 | 77.0 | 90.2 | 75.4 |
| Both captions, multi-text | 72.1 | 0.580 | 76.9 | 85.1 | 73.9 |
| Both captions, dual | 73.3 | 0.588 | 78.3 | 88.7 | 77.1 |
| (C) Improved loss functions, with noisy captions |  |  |  |  |  |
| CLIP $+$ self-dist | 70.3 | 0.589 | 79.1 | 81.5 | 67.0 |
| CLIP $+$ self-dist $+$ MIM | 75.9 | 0.511 | 79.0 | 82.6 | 67.6 |
| (D) Ours: combining improved captions from (B) and losses from (C) |  |  |  |  |  |
| CLIP $+$ self-dist $+$ MIM |  |  |  |  |  |
| Both captions, dual | 79.0 | 0.478 | 78.8 | 89.2 | 77.3 |

**Table 2: Image-only evaluations for dense and global prediction tasks. Experiments using the largest backbone available for each model variant, comparing recent self-supervised and image-text models against TIPS. Rows that are highlighted refer to image-only models that are very good at dense prediction tasks, but are by nature unable to perform text-related tasks. We also highlight the best and second-best number of each column. TIPS achieves the best or second-best performance in $7$ out of $9$ evaluations.**
| Method | $\uparrow$ Segmentation | $\downarrow$ Depth | $\downarrow$ Normals | $\uparrow$ Fine-grained | $\uparrow$ ImageNet classif. |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| PASCAL | ADE20k | NYUv2 | NAVI | NYUv2 | NAVI | retrieval (UnED) | KNN | lin |  |
| DINO-B | 66.4 | 31.8 | 0.555 | - | 28.4 | 28.8 | - | 77.4 | 80.1 |
| MAE-H/14 | 67.6 | 33.3 | 0.517 | - | - | - | - | 49.4 | 76.6 |
| iBOT-L/16 | 82.3 | 44.6 | 0.417 | - | 24.5 | 26.6 | - | 72.9 | 82.3 |
| JFT-3B ViT-g/14 | 70.7 | 37.5 | 0.605 | 0.096 | 24.4 | 26.7 | 59.5 | 85.1 | 87.4 |
| DINOv2-g/14 | 83.0 | 49.0 | 0.344 | 0.054 | 20.5 | 24.0 | 62.2 | 83.5 | 86.5 |
| CLIP-L | 74.5 | 39.0 | 0.553 | 0.073 | 24.3 | 25.5 | 57.4 | 79.8 | 84.3 |
| SigLIP-SO/14 | 67.8 | 35.8 | 0.580 | 0.074 | 25.6 | 25.7 | 70.8 | 84.4 | 86.4 |
| OpenCLIP-G/14 | 71.4 | 39.3 | 0.541 | - | - | - | - | 83.2 | 86.2 |
| TIPS-g/14 LR (ours) | 82.8 | 47.4 | 0.377 | 0.061 | 23.0 | 24.5 | 71.4 | 83.6 | 86.4 |
| TIPS-g/14 HR (ours) | 83.6 | 49.9 | 0.353 | 0.058 | 21.9 | 24.2 | 68.2 | 83.3 | 86.2 |

Ablations.
We present in Tab. 1 ablative experiments on $5$ different tasks to isolate the effect of the enhanced textual supervision and new losses, where a ViT-B backbone is used.
The baseline CLIP model with the noisy web captions is presented in (A).

Part (B) of the table ablates the contribution of enhanced textual supervision.
Simply replacing the web captions by PaliGemma-generated ones improves segmentation by $10.1$ percentage points and reduces depth RMSE by 0.076, which are big positive gains.
This shows the potential of synthetic captions for dense understanding with image-text models.
However, at the same time global tasks show significant regressions, with KNN classification loss of $6.9$ points.
But the CLIP performance can be improved in all tasks by combining the web and synthetic captions: using our dual embedding approach, we achieve large gains across the board.
We also compare our dual approach against two other caption combination options, inspired by the ones proposed by Fan et al. (): “sampled”, where either the web or the synthetic caption is chosen at random; or “multi-text”, where both captions are matched against the same image embedding.
Our dual approach performs better than other caption combinations in $3$ out $5$ cases and achieves competitive results on the other $2$, which indicates its effectiveness.

Part (C) ablates the effect of the self-supervised losses, using web captions.
The addition of self-distillation brings improvements in all tasks.
This is a setup similar to SILC (Naeem et al., ): we confirm their findings for I$\rightarrow$T and T$\rightarrow$I retrieval, and additionally show that the self-distillation loss is effective for image-only tasks, notably dense ones.
With our additional masked image modeling (MIM) loss, significant improvements are observed in dense tasks, while maintaining high scores in the other tasks: $5.6$ points gain in segmentation and $0.078$ reduction in depth RMSE.

Part (D) combines the findings of (B) and (C) to deliver very substantial improvements against the baseline CLIP setup in all tasks, notably: $14.6$ points gain in segmentation, $0.142$ reduction in depth RMSE, $10.1$ points gain in I$\rightarrow$T retrieval and $14.4$ points gain in T$\rightarrow$I retrieval.
Additional ablations for the effects of synthetic captions and dataset curation can be found in the appendix, Sec. A.1.

Comparisons against existing general-purpose methods are provided in Tables 2 and 3, for tasks involving images only or images and text, respectively, where results for TIPS are provided for the model before (“LR”) and after (“HR”) high-resolution fine-tuning.
Overall, TIPS achieves strong results, with competitive performance across a wide range of tasks, reaching the best or second-best numbers in $13$ out of the $16$ reported evaluations.
Compared against existing image-text methods, TIPS improves on I$\rightarrow$T and T$\rightarrow$I retrieval, while also achieving substantial gains in dense prediction tasks, reaching the level of DINOv2 and surpassing it in some cases.
It is interesting to note that while recent image-text models have achieved excellent results in multimodal retrieval or zero-shot classification, those gains do not translate to improved features for dense undertanding, whose performance lags substantially behind TIPS and self-supervised approaches.
In particular, even CLIP-L, with much worse performance on image-level prediction tasks, outperforms the recent SigLIP-SO on all $6$ dense evaluations.
Another recent and much larger image model trained with contrastive learning, InternViT-$6$B (Chen et al., ), achieves $47.2$% on ADE$20$k, which is much worse than our $1.1$B TIPS-g model.
In terms of supervised methods, the ViT-g trained on JFT-3B also performs worse on dense tasks than CLIP-L.
And an even larger ViT-$22$B (Dehghani et al., ), also trained on JFT, achieves only $34.6$% in ADE20k on the same setup, as reported by Chen et al. ().
In comparison to self-supervised techniques, TIPS achieves strong results, with numbers comparable to DINOv2 in most cases and surpassing them significantly in segmentation and retrieval, while at the same time enabling multimodal tasks which cannot be performed with self-supervised methods alone.
Fig. 4 shows qualitative examples for our dense feature probes.

Application: Single-image to 3D.
Modern large reconstruction models rely on high-quality pre-trained image encoders to produce image tokens for an encoder/decoder transformer (Hong et al., ; Wang et al., ). For example, LRM (Hong et al., ) predicts parameters of a neural rendering model from the image features of a single input image.
The authors choose the ViT-based DINO encoder over more semantic-aware ones (such as CLIP) due to its knowledge of structural and texture information necessary for 3D tasks.

**Table 3: Image-text evaluations for multimodal retrieval and zero-shot classification, where TIPS outperforms others in $6$ out of $7$ cases. Note that we compare solely against weakly-supervised methods, since self-supervised ones are not naturally aligned with language. We highlight the best and second-best number of each column.**
| Method | $\uparrow$ I$\rightarrow$T retrieval | $\uparrow$ T$\rightarrow$I retrieval | $\uparrow$ ImageNet |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
| COCO | Flickr | DOCCI | COCO | Flickr | DOCCI | 0-shot |  |
| CLIP-L/14 | 56.3 | 85.2 | 44.4 | 36.5 | 65.2 | 40.4 | 75.5 |
| OpenCLIP-G/14 | 67.3 | 92.9 | - | 51.4 | 79.5 | - | 80.1 |
| EVA-CLIP-g/14 | 68.2 | 91.6 | - | 50.3 | 78.9 | - | 79.3 |
| SigLIP-SO/14 | 70.2 | 91.0 | 27.5 | 52.0 | 75.3 | 28.4 | 83.2 |
| SILC-G/16 | 73.2 | - | - | 54.7 | - | - | 83.7 |
| TIPS-g/14 LR (ours) | 73.7 | 93.0 | 56.4 | 58.3 | 83.2 | 58.9 | 79.7 |
| TIPS-g/14 HR (ours) | 74.0 | 93.0 | 57.2 | 59.4 | 84.5 | 58.8 | 79.9 |

**Table 4: LRM novel view synthesis using original DINO features vs. TIPS features. TIPS yields improvements with a model of similar size.**
| Method | $\uparrow$ PSNR |
| --- | --- |
| DINO-B/16 | 21.13 |
| TIPS-B/14 | 21.75 |

In order to better understand our model’s capabilities for neural 3D reconstruction, we evaluate TIPS performance in the LRM framework and compare DINO-B/16 to an equivalently-sized TIPS-B/14. We opt to use DINO-B/16 to follow after the original paper’s implementation.
Single-image to $3$D results on the Objaverse (Deitke et al., ) dataset are presented in Tab. 4, showing that TIPS outperforms DINO as an image encoder for large reconstruction models, with enhanced novel view synthesis capabilities ($0.62$ increase in PSNR). Additional qualitative results can be found in A.4.4.

Figure: Figure 4: Qualitative results for dense prediction tasks. For a given image (first column), we illustrate the principal components of the predicted spatial features (column 2) . Depth (column 3) and normals (column 4) are trained on NYUD, and for semantic segmentation (last column) we used the model trained on ADE20k. All dense tasks used the DPT decoder, while keeping the image encoder weights frozen. More qualitative results can be found in Section A.5.
Refer to caption: extracted/5897335/images/8_rgb.jpg

## 5 Conclusions

We introduce TIPS (Text-Image Pretraining with Spatial awareness), a new general-purpose image-text encoder.
TIPS can be successfully applied off-the-shelf to a variety of computer vision tasks, enabling dense and image-level prediction, leveraging two simple and effective contributions.
First, we employ existing multimodal generative models to produce high-quality synthetic image descriptions, which are used to improve contrastive learning and boost performance on dense image prediction.
We propose a dual embedding approach to leverage both synthetic and noisy web captions, unlocking gains across a wide range of tasks.
Second, we combine contrastive image-text learning with self-distillation and masked image modeling, incentivizing the model to learn spatially-aware representations.
These two contributions are complementary and allow us to effectively scale our models to a ViT-g architecture trained on a curated dataset of $117$M images.
Our comprehensive experiments demonstrate strong off-the-shelf results on $8$ tasks comprising $16$ datasets in total, enabling a wide variety of computer vision applications which involve only images, or images and text.

#### Acknowledgments

We would like to thank Xiaohua Zhai, Xiao Wang, Lucas Beyer, Alexey Gritsenko, Matthias Minderer, Mohamed El-Banani, Muhammad Ferjad Naeem, Austin Stone, Hagen Soltau, Jonathon Shlens, Abhijit Ogale, Ming-Hsuan Yang and Huizhong Chen for thoughtful discussions and helpful pointers to datasets, experiments and related work.

## Appendix A Appendix

### A.1 Additional experimental results

Ablation on synthetic caption versions.
To understand in more detail the impact of synthetically-generated descriptions on different tasks, we create different variants to ablate the effect of the composition of the description.
“PaliGemma object list” is created by prompting Gemini $1.5$ Flash (Gemini Team Google, ) to take the original PaliGemma caption and produce a list of the objects that are mentioned in it, for example: “A black SUV parked in front of a building” becomes “black SUV, building”.
“PaliGemma main object” is created similarly to the object list version, except that it is prompted to produce only the main object in the caption, for example: “A black SUV parked in front of a building” becomes “black SUV”.
Results are presented in Tab. 5, using a CLIP model with a ViT-B backbone.
First, note how “PaliGemma object list” already provides significant boost over the noisy captions for dense prediction tasks, which indicates that listing the multiple objects in the images, without noisy web terms, helps substantially.
On top of this, the full “PaliGemma captions”, including descriptions about object spatial arrangements, further improves dense understanding, notably for depth estimation.
The results for “PaliGemma main object” show some improvement compared to noisy captions in depth, but not for segmentation.
On I$\rightarrow$T and T$\rightarrow$I retrieval, “PaliGemma captions” also provide significant improvements compared against other PaliGemma caption variants.

**Table 5: Synthetic caption ablations for representative evaluations, using a CLIP ViT-B model.**
| Captioning version | $\uparrow$ Segmentation | $\downarrow$ Depth | $\uparrow$ KNN classif. | $\uparrow$ I$\rightarrow$T retrieval | $\uparrow$ T$\rightarrow$I retrieval |
| --- | --- | --- | --- | --- | --- |
| (PASCAL) | (NYUv2) | (ImageNet) | (Flickr) | (Flickr) |  |
| Noisy captions | 64.4 | 0.620 | 76.9 | 79.1 | 62.9 |
| PaliGemma captions | 74.5 | 0.544 | 70.0 | 79.8 | 60.1 |
| PaliGemma object list | 73.8 | 0.575 | 70.1 | 66.4 | 45.0 |
| PaliGemma main object | 61.9 | 0.598 | 67.7 | 8.7 | 4.7 |

Ablation on dataset versions.
We compare our final curated version of WebLI, with $116$M image-text pairs in total, to $2$ other versions: “raw” ($10$B unfiltered image-text pairs) and “EN quality-filtered” ($1.7$B image-text pairs filtered with pretrained alignment model and keeping only English-captioned pairs).
Results are presented in Tab. 6, using a CLIP model with a ViT-B backbone, for a representative set of image evaluations.
Our curated dataset helps improve performance across all of these evaluations, while being $1$ or $2$ orders of magnitude smaller.

**Table 6: Training set ablations for representative image evaluations, using a CLIP ViT-B model.**
| Training set | $\uparrow$ Segmentation | $\downarrow$ Depth | $\uparrow$ KNN classif. | $\uparrow$ Fine-grained retrieval |
| --- | --- | --- | --- | --- |
| (ADE$20$k) | (NYUv2) | (ImageNet) | (UnED) |  |
| Raw ($10$B) | 29.1 | 0.698 | 68.4 | 45.8 |
| EN quality-filtered ($1.7$B) | 31.5 | 0.632 | 76.2 | 59.3 |
| Ours curated ($116$M) | 31.6 | 0.620 | 76.9 | 62.9 |

UnED detailed results can be found in Tab. 7, covering all of the $8$ domains in the benchmark: food (Food2k dataset, Min et al. ()), cars (CARS196 dataset, Krause et al. ()), online products (SOP dataset, Song et al. ()), clothing (InShop dataset, Liu et al. ()), natural world (iNat dataset, Van Horn et al. ()), artworks (Met dataset, Ypsilantis et al. ()), landmarks (GLDv2 dataset, Weyand et al. ()) and retail products (Rp2k dataset, Peng et al. ()).
We compare TIPS against the main competitors, outperforming SigLIP on average by $0.6$ percentage point and DINOv2 with a very significant $9.2$ points gain.
TIPS achieves the best score in $3$ domains, SigLIP in another $3$ domains and DINOv2 in $2$ domains.

**Table 7: UnED detailed results over the $8$ fine-grained/instance-level recognition domains, measured with Recall@1. We highlight the best and second-best number of each column.**
| Method | Food2k | CARS196 | SOP | InShop | iNat | Met | GLDv2 | Rp2k | Mean |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| CLIP-L/14 | 46.7 | 89.8 | 63.5 | 61.1 | 72.4 | 30.8 | 36.7 | 58.3 | 57.4 |
| JFT-3B ViT-g/14 | 56.7 | 96.9 | 64.0 | 58.0 | 75.4 | 28.2 | 27.2 | 69.6 | 59.5 |
| DINOv2-g/14 | 54.4 | 83.2 | 56.3 | 35.8 | 82.3 | 60.4 | 55.3 | 69.5 | 62.2 |
| SigLIP-SO/14 | 60.6 | 97.5 | 76.7 | 76.1 | 76.5 | 58.9 | 44.6 | 76.2 | 70.8 |
| TIPS-g/14 LR (ours) | 63.6 | 94.9 | 73.8 | 83.9 | 83.3 | 55.6 | 41.9 | 74.4 | 71.4 |
| TIPS-g/14 HR (ours) | 57.0 | 94.8 | 73.2 | 81.3 | 80.8 | 48.2 | 40.8 | 72.4 | 68.6 |

### A.2 Additional implementation details

Loss weight coefficients as in Sec. 3.2 are $\alpha=1,\beta=2$. We use the Adafactor optimizer  (Shazeer & Stern, ) with a learning rate schedule of linear warm-up for 1.4 epochs up to 5e-4, and then linear decay down to 0 for the remaining epochs.
The teacher model is updated with the EMA of the student using a momentum on a cosine schedule from $0.994$ to $1$.
The projection heads for self-distillation and masking are identical but unshared, and consist of a $3$-layer MLP, $l_{2}$ normalization, and a weight-normalized projection layer to a prototype dimension of $32$k. We use sharpening and centering operations after the projection head to avoid collapse to either uniform or Dirac delta distributions.
To center, we subtract the student scores by the EMA of their means using constant momentum 0.9. To sharpen, we set temperatures $\tau_{s}=\tau_{s}^{\prime}=0.1$, $\tau_{t}=0.07$, and warm-up $\tau_{t}^{\prime}$ along a linear schedule from $0.04$ to $0.07$.
For TIPS-g/14 LR, we stop the training early at 15 epochs due to evaluation saturation. For TIPS-g/14 HR, we start high-resolution finetuning from the 13-epoch checkpoint of TIPS-g/14 LR.

### A.3 Dataset curation

**Table 8: Dataset curation statistics. High-quality image datasets are used to filter WebLI. We use precomputed image embeddings to calculate image similarity between target datasets and raw WebLI data.**
| Dataset Name | Dataset Size | # Clusters | # Images Sampled |
| --- | --- | --- | --- |
| PASCAL-VOC-2007-train | 2,501 | 5 | 16,935,483 |
| PASCAL-VOC-2012-train | 8,648 | 8 | 17,925,056 |
| ADE20K-train | 20,210 | 20 | 17,288,796 |
| NYU-Depth-V2-train | 24,231 | 5 | 1,004,740 |
| ImageNet-2012-train | 1,281,167 | 1000 | 17,792,551 |
| ImageNet22k-train | 12,720,275 | 1000 | 19,859,560 |
| UnED-Food2k-train | 472,349 | 100 | 4,063,232 |
| UnED-CARS196-train | 6,346 | 6 | 5,012,681 |
| UnED-SOP-train | 48,942 | 48 | 15,246,219 |
| UnED-InShop-train | 20,897 | 20 | 13,611,747 |
| UnED-iNaturalist-train | 273,929 | 188 | 10,017,768 |
| UnED-Met-train | 397,121 | 397 | 8,010,633 |
| UnED-GLDv2-train | 1,422,914 | 1000 | 12,323,999 |
| UnED-Rp2k-train | 188,724 | 50 | 561,095 |
| Total | 16,888,254 | - | 159,654,074 |
| Total (after self-dedup.) | 16,888,254 | - | 115,913,373 |
| Total (after self+eval-dedup.) | 16,888,254 | - | 115,894,610 |

Table 8 lists the high-quality datasets used to curate WebLI beyond filtering based on image-text and English language.
For each target dataset, we first extract image embeddings from a pretrained model and perform k-means clustering; Table 8 includes the number of clusters chosen manually to avoid overclustering. WebLI images are assigned to its closest cluster by image embedding distance, and a probability distribution is defined over the clusters using cluster membership sizes. Then, we sample from the clusters accordingly, ignoring members that are sufficiently far from their assigned cluster center (90th percentile or above). We repeat this process for each target dataset independently and perform deduplication across all samples. We also perform deduplication with all evaluation data, which removes around $19$k images.

### A.4 Detailed evaluation protocols

In this section we provide the detailed evaluation protocols of all evals used in this work. As a general remark, we use identical protocols for low-res and high-res models, without modifying the input resolution, consistent with previous work (Oquab et al., ). The parameters of the pretrained transformer network remain frozen throughout the evals.

#### A.4.1 Dense Image Tasks

For dense prediction tasks, we evaluate the quality of the patch tokens. As is common practice, we concatenate the [CLS] token to each of the patch tokens. For our method, we choose the [CLS] token that was trained with the more spatially aware synthetic caption ($\mathbf{\hat{e}}^{g}$). We attach two different types of probes to the image encoder: a simple linear layer (segmentation, depth) or a powerful DPT (Ranftl et al., ) decoder (depth, normals).

Semantic segmentation. For semantic segmentation, we train the network with images of resolution $512\times 512$. We use batch size of 16 images, and train for 40k iterations. We attach a linear layer to the main network, and up-sample to the input resolution to apply the cross-entropy loss. At test time, we run inference on full-resolution images. We note that for DINOv2 (Oquab et al., ) concatenating the [CLS] token to the patch tokens consistently yields inferior results, so we report results using their original protocol that does not use the [CLS].

Monocular depth estimation. For NYUv2, we use the simple evaluation protocol of Li et al. (); Oquab et al. (). We train with a resolution of $480\times 640$. Similar to segmentation evals, we concatenate the [CLS] token to the patch tokens, and we upsample by a factor of 4. We attach a linear layer and cast the prediction as classification into 256 uniformly distributed bins. Note that this version of NYUv2 is slightly different to the one used in (El Banani et al., ), and the numbers not directly comparable with that work.

For NAVI, we follow El Banani et al. () and attach the DPT decoder (Ranftl et al., ) to 4 uniformly distributed layers ($l={10,20,30,40}$ for ViT-g/14). We train and test on center crops of objects, with a resolution of $512\times 512$.

For both datasets we use batch size of 8 and train for 50k iterations.

Surface normal estimation. We follow similar protocols to El Banani et al. () for both NYUv2 and NAVI, using their data and metrics. For both datasets, we probe with the powerful DPT decoder using outputs from 4 uniformly sampled transformer blocks. For NYUv2 we train with a resolution of $480\times 480$, and we test with full-resolution $480\times 640$ images. For NAVI, we train and test on center crops of the objects, with a resolution of $512\times 512$. For both datasets we use batch size of 8 and we train for 50k iterations.

#### A.4.2 Global Image Tasks

In global image tasks, we use the [CLS] token that corresponds to the noisy image label that often contains more fine-grained information ($\mathbf{e}^{g}$).

Image classification. We use ImageNet-1k for classification. We train a linear classifier on top of the $\mathbf{e}^{g}$ embedding. We use input image resolution of $224\times 224$ for 10 epochs, using an effective batch size of 1024 (smaller batch sizes achieve identical results). We run a grid search with the search space of (Oquab et al., ), and we report the maximum accuracy, which is a standard practice. We also report the accuracy of soft KNN classification with 20 neighbours (Oquab et al., ), without training any weights.

Fine-grained and instance-level retrieval. We use the UnED (Ypsilantis et al., ) dataset for retrieval.
We use an input size of $224\times 224$, and we $l_{2}$-normalize the resulting $\mathbf{e}^{g}$ embedding. As is standard for this dataset, we run KNN with a single neighbour. As also mentioned in the main text, UnED consists of 8 datasets of different domains. We report recall@1 as the final aggregated result, and report results per domain in Sec. A.1.

#### A.4.3 Multimodal Retrieval Tasks

Image-to-text (I$\rightarrow$T) and text-to-image (T$\rightarrow$I) retrieval.
We use the [CLS] token that was trained with the synthetic caption ($\mathbf{\hat{e}}^{g}$) as the image representation. We prefer synthetic captions because they tend to describe image content more comprehensively than noisy web captions, making them better aligned with the image-text retrieval evaluation datasets used in this work: Flickr30k (Young et al., ), DOCCI (Onoe et al., ) and COCO (Chen et al., ).
Our text encoder handles a maximum of 64 tokens. Texts longer than this limit are truncated, which happens more frequently in DOCCI.
Notably, the text encoder of SigLIP has a small maximum token length of 16, which likely contributes to its relatively low performance in DOCCI.

Figure: Figure 5: More qualitative results for dense prediction tasks. For a given image (first column), we illustrate the principal components of the predicted spatial features (column 2). Depth (column 3) and normals (column 4) are trained on NYUD, and for semantic segmentation (last column) we used the model trained on ADE20k. All dense tasks used the DPT decoder, while keeping the image encoder weights frozen.
Refer to caption: extracted/5897335/images/2_rgb.jpg

Zero-shot classification.
We use the [CLS] token that corresponds to the noisy web caption, which often contains more fine-grained information ($\mathbf{e}^{g}$), as image embedding. We adhere to the established protocol initiated by Radford et al. (), which utilizes 80 context prompts to transform each ImageNet1k class into 80 distinct texts. The class embedding is then formed by taking the average of the embeddings from these 80 texts. Classification of the image is achieved by retrieving the nearest class embedding.

#### A.4.4 3D Vision Tasks.

Single-image to 3D.
We use the baseline LRM (Hong et al., ) method and follow the architectural and training details of the original paper closely unless otherwise mentioned.
We obtain images from Objaverse (Deitke et al., ) by sampling random rotations around each object and rendering with uniformly sampled focal lengths $\in[512,1024]$ and a nominal distance of $0.4$.
Cameras are oriented towards the origin and objects are centered at the origin. For each entry in Objaverse, we render reference views for training and novel target views for evaluating view synthesis.
We freeze the image encoder to extract patch features and directly compare the paper baseline (DINO-B) with ours at the ViT-B/14 scale, for a fair comparison. Figure 6 shows qualitative results of LRM trained on top of DINO-B/16 and our TIPS-B/14. We show the input single image used to generate neural rendering model parameters, along with RGB and depth images rendered via NeRF-style ray marching (Mildenhall et al., ).

### A.5 Qualitative results

Figure 5 illustrates further qualitative results of the features and outputs obtained by our method for dense features, probing the image encoder features. In Figure 6 we show the quality improvement when substituting the default DINO-B with the TIPS-B model.

Figure: Figure 6: Qualitative results on novel view synthesis from LRM (Hong et al., ) trained on top of DINO-B/16 and TIPS-B/14. The input image is used to generate parameters for a neural rendering model, after image encoding. We visualize RGB and depth images rendered with the same camera parameters (intrinsics and extrinsics) using NeRF-style ray marching. We can observe that LRM-TIPS is able to predict the geometry of the captured object with a higher degree of accuracy, as indicated by the gain in PSNR from Tab. 4.
Refer to caption: x4.png

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: Please refer to my comments above.
soundness: 3
strengths: - The paper presents a solid work on text-image pre-training: a large-scale synthetic caption dataset is created, the method is evaluated on both classification and dense prediction tasks, and extensive experimental studies are conducted for ablation studies and analyses. 

- The results look good. The proposed method can achieve good dense prediction and classification/retrieval performance simultaneously. Ablation results provided in the paper may be helpful for developing new text-image models.
summary: This paper presents a spatial-aware text-image pre-training method that combines contrastive image-text learning with self-supervised masked image modeling. Besides, the method proposes to combine the noisy web captions and synthetic captions that are more helpful to learn spatially aware representations. The method is evaluated on both zero-shot classification and dense prediction tasks.
weaknesses: - The general idea of combining contrastive image-text learning and masked image modeling is not new. Previous work like EVA-CLIP [r1] has already show that MIM can improve the spatial awareness or locality of CLIP features and improve CLIP performance. The core different between TIPS and the line of work is to combine MIM and CLIP successively or simultaneously. I think simultaneously perform the two tasks may be better to preserve the spatial awareness/locality, but it may also make the training more costly, or possibly unstable. It would be better to provide a comparison/analysis on the pros and cons of the two strategies.  

[r1] EVA-CLIP: Improved Training Techniques for CLIP at Scale

- The study use the proprietary WebLI dataset to train the model. Is it possible that the improvements over previous methods mainly come from better data sources?  How about the results if both the proposed model and the baseline use publicly available datasets like LAION, COYO or DataComp.

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confidence: 3
contribution: 4
initial_rating: 8
presentation: 4
questions: Will the pretrained model and the curation dataset with synthetic captions be released?
soundness: 4
strengths: 1. The author proposes an effective approach that enhances the utility of both synthetic and noisy web captions in training. They also introduce contrastive image-text learning with self-supervised masked image modeling, which effectively encourage the learning of spatial coherence.
2. The author conduct a variety of experiments in 8 downstream tasks demonstrate the effectiveness of its spatial-aware text-image encoder.
summary: This paper introduces a novel pretrained image-text encoder with spatial awareness which is effective in a variety of downstream computer vision tasks. To achieve this, the author first employs pretrained multimodal generative models to generate high-quality synthetic image descriptions and develops a dual embedding approach that leverages both synthetic and noisy web captions in training. Additionally, contrastive image-text learning, coupled with self-distillation and masked image modeling, is introduced to encourage the model to learn spatially aware representations. Experiments conducted on eight downstream tasks validate the effectiveness of the proposed method.
weaknesses: The formatting of the paper needs improvement and there are  a lot of empty spaces around fig1 and fig2.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 4
questions: - As the motivation of this paper is to bridge the gap between image-text representation learning and SSL, although the ablation studies are provided, this paper lacks an in-depth analysis on how the two paradigms interact with each other. For example, how the SSL design choices such as augmentations (mask ratio, etc.) affect the image-text representation learning. 

- The idea of dual embedding is interesting. I'm curious about the different roles of the two embeddings, and how they interact with the network. Could the authors provide more empirical analysis on this? For example, visualization of the attention maps of the two different $[CLS]$ to see their focus areas.
soundness: 3
strengths: - This paper is well written.

- The experiments on dense image prediction tasks are comprehensive and promising, outperforming DINO V2 on several tasks.

- Improving the spatial awareness of image-text representation learning is an important direction, combining DINO v2 and CLIP, where both are foundational works in their respective fields, is intuitive and promising.
summary: This paper targets integrating the paradigms of both image-text representation learning and self-supervised learning to improve the spatial awareness of the former. For the SSL branch, the authors leverage the DINO V2 (iBOT) pre-training method; for the image-text branch, they propose the dual image-text embedding technique that learns from both noisy and sythetic captions while harnessing the distribution gap between two types of captions. The effeciveness of the proposed method is evaluated on several image-level multimodal tasks and comprehensive dense image prediction tasks.
weaknesses: - The technical contributions are limited. The proposed method is a combination of existing methods, with the dual embedding technique being the only novel contribution. Nonetheless, I'm okay with this, since the proposed model effectively and adequately solves model's spatial awareness limitation.

- As claim in Line 300: 
>Our method is the first to demonstrate that combining contrastive image-text learning with self-distillation and masked image modeling leads to improvements across many tasks

However, both integrating CLIP with self-distillation and masked image modeling[1][2] have been proposed before. And this paper lacks a further discussion against these works.

- Since this is a multimodal model with spatial awareness, only I$\rightarrow$T and T$\rightarrow$I retrieval tasks are not enough to evaluate the model's fine-grained spatial awareness under multimodal settings. Including more experiments like open-vocabulary segmentation would be beneficial.

reference:

[1] Maskclip: Masked self-distillation advances contrastive language-image pretraining.  CVPR 23.

[2] Scaling Language-Image Pre-Training via Masking. CVPR 23

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: 1.	Authors are suggested to add detailed ablation results to isolate the impact of the synthetic captions on different spatial tasks.
2.	Have you considered alternative ways of introducing spatial awareness besides synthetic captions and masking?
soundness: 2
strengths: 1.	The paper is well-structured and clearly articulated, with detailed experimental records. By cleaning and constructing a high-quality dataset and incorporating self-supervision, methods such as dual captioning and masked image modeling enable the model to achieve significant (albeit incremental) advancements in dense prediction tasks.
2.	The trained model demonstrates strong generalizability across multiple tasks, indicating its broad applicability in vision tasks.
3.	The paper includes a substantial amount of experimental comparisons and work.
summary: This paper addresses dense and global vision tasks by enhancing textual supervision and integrating contrastive image-text learning with self-supervised techniques. The method combines noisy web captions with synthetically generated captions to improve spatial awareness and applies masked image modeling to promote coherence in spatial understanding. As a result, the model demonstrates robust performance across various tasks without the need for fine-tuning, showcasing its general-purpose applicability in both image-only and image-text applications.
weaknesses: 1.	The work presents only a limited amount of novelty. The main critique lies in the lack of significant innovation. The paper largely repurposes existing techniques like synthetic captioning and contrastive learning, and while the results are solid, they do not represent a substantial leap forward in the field.
2.	The improvements over existing models such as CLIP and DINOv2 are incremental, and the performance gains are sometimes marginal or context-specific. The originality in combining these techniques does not feel transformative.
3.	More detailed ablation studies focusing on the contribution of each component (e.g., the specific impact of spatial coherence from the captions) could strengthen the claim of novelty.

Meta-review: The paper presents an approach to integrating spatial awareness into text-image pretraining. The reviewers generally agree on the paper's strengths, including its extensive experiments, strong generalization across tasks, and intuitive dual-embedding technique. Some concerns were raised about the novelty of combining existing methods, though these were largely addressed during the rebuttal phase. Given the positive scores after the rebuttal and discussion period, the AC recommends acceptance.

Decision: Accept (Poster)