Annotation Bootstrapping: Reinforcing Visual Pre-Training using Unlabelled Images
visual pretraining;self supervised learning;bootstrapping
Yes
No ethics review needed.
Overall, the paper doesn't feel like it was ready for submission. To even consider recommending acceptance, I would need the following details:
- Please clarify fundamental details about the approach
- Please show evaluations using more standardized setups

Other than that please look at the weaknesses
- The paper starts with a good intuition, and proposes to use annotations to apply an SSL loss where one crop predicts the annotations for another crop
- The results seem strong (albeit in a very uncommon settings)
- The approach supports SSL, image-text, semi-labeled data, etc. extending its applicability
- The paper presents annotation bootstrapping, a learning approach which trains a model to predict semantic “annotations” and “bootstrapping” by predicting the annotations for a different crop given a crop
- The method supports SSL training, WSL, and semi supervised
- The paper shows AB with two objectives, SimCLR and CLIP
- The paper shows results on various tasks by training on image and video datasets
- The paper is quite hard to understand. Figure 2 works explain things at a high level, but doesn’t explain the exact approach. Section 3 also talks about mathematical equations but doesn’t discuss practical details. Finally Algorithm 1 doesn’t help clarify details either. The annotation l is a function of the input x, so if x is transformed, l should be different. But since it isn’t defined as a function, it becomes an overloaded term. Input x has an annotation l, but also we try to find l given a transformation a on x. In Algorithm 1 in annotation bootstrapping, is $g_\theta(x, a, l)$ the simCLR loss applied to the transformed image? SimCLR itself applies transforms so it’s not clear what exactly is happening practically to me. What does “ copy (only) the annotations from the labelled batch” mean in L281? While bootstrapping are we using both labelled and unlabeled images? What does “copy” the annotations mean – we anyway create new crops for an image during bootstrapping so we don’t have real annotations for these crops if I understand things correctly. 
- The evaluations are performed with a single setup of using a decoder-based probe which cross attends to the ViT outputs. While this is an interesting setup, it is not commonplace. This makes the results hard to compare vs. “regular” setups like linear probing, finetuning for the tasks, etc. It is not clear if AB performs better in this setting vs. other settings. Especially for a new method like AB, it is very important to have evaluation results in more common settings like linear probes and finetuning, at least on ImageNet, and then making comparisons
  - MAE only works well when finetuned, so the current setup puts it at a disadvantage anyway
- There are lots of typos, mistakes, etc. which again make the paper hard to read, e.g. Table 1 says AB_{CLIP} on ImageNet (No Labels) whereas the discussion refers to this as AP_{SimCLR} – it has to not be CLIP since there’s no labels I assume. Table 2 bolds MAE for some columns even though the results are not best. It is not clear whether the numbers are incorrect or if there is some other mistake? There is no Figure 5.2 in the paper (L432). I see Figure 3, but I don’t see anything in the plots referring to $AP_SimCLR(I1K)$
- How is the video training performed? There are no details about the approach around EK.
- There should be comparisons to JEPA style architectures like I-JEPA, especially since even there the model is trained to produce representations (not annotations) for one crop from another crop
Yes
No ethics review needed.
Other comments and suggestions:
  - L.13-15: This is not correct. Many existing methods work learn well from web datasets or even satellite images (DINOv2, Tolan et al. [1], Vo et al. [2], AIM [3]).
  - L.164: p(x,l) seems to be the joint image-annotation distribution, not the annotation distribution
  - How exactly COCO captions and bounding boxes are used as annotation in Table 3? Which dataset/annotation is used in contrastive annotation loss and predictive propagation loss?
  - L431. How to generate annotation with SimCLR, what are the annotations?
  - claims in L.432-439 are not supported by the figure without results on DINO/1K+COCO
  - L 464-471. If smaller IoU leads to better performance, why don't we train on non-overlapping crops?
  - The selection of l_i from the annotation batch in the annotation boostrapping update in Algo 1 seems arbitrary since the images are different in the two updates. It seems that a any projection of the image or image/action embeddings into the annotation embedding space would work here. In this spirit, this projection is very similar to the projection to the prototype space in DINO. The main difference is the addition of the action tokens in the loss equation. What is the performance if learnt prototypes are used as in DINO instead of such arbitrary projection to the annotation space?

[1] Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar. Tolan et al., 2024

[2] Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach. Vo et al., 2024

[3] Scalable Pre-training of Large Autoregressive Image Models. El-Nouby et al., 2024
The framework discussed in the paper provides a view that unifies certain self-supervised methods such as DINO and Swav and image-text alignment methods such as Naeem et al.. This view is interesting. In the fully unlabelled setting, the main difference between the proposed framework and DINO or Swav is the consideration of the transformation, or actions, that transform one view to another. This seems to implicitly enforce equivariance instead of invariance on the model's prediction on the two views, and makes the framework more robust when cropping invariance does not apply.

The paper shows that the proposed method improves upon baselines in multiple benchmarks.
The paper proposes a framework for pre-training visual features that can incorporate both labelled and unlabelled data. The framework has two main components: the first matches images to its annotations (could be weak labels like image captions or automatic annotations like an augmented view of the image) while given two views of an image, the second makes the model predictive of the annotation distribution of the second view given the first view and the transformation turning the first view to the second view. The approach in the second component is different from other works in the literature which often enforces invariance on the prediction on the two views. 

The papers shows better results than competitors in both self-supervised learning and text-image aligning tasks.
There are several issues with the paper's presentation:
  - The presentation of the method is overly complex with unnecessary and under-clarified equations such as Eq. 1. In particular, what is the space that the right term is optimized on? What are l and x in the left term? Are they variables or given fixed image and annotation? The inclusion of this equation seems unnecessary. 
    The whole framework is very similar to the common framework used for semi-supervised learning [1, 2] which often consists of a superivsed loss computed on labelled data and an unsupervised loss to enforce invariance or equivariance [3] on the model's prediction. In this case, the L_annotation loss corresponds to the supervised loss and the L_propagation loss amounts to implicitly enforcing equivariance with respect to the transformation "a" on the model's prediction. From this point of view, the proposed framework is very similar to DINO or Swav consider multi crops and enforce consistency between global crops (L_annotation) and between global crops and local crops (L_boostrapping without considering the transformation). The projection of image features to the annotation space is also similar to the projection to the space of prototypes in DINO/Swav. It is also very similar to Naeem et al. in the case of text-image alignment. The paper should remove unnecessarily heavy maths  and make the connnections to DINO/Swav and Naeem et al. clearer.
  - The paper contains many typos such as missing citation "cite" (L.92, 97), wrong refs (Figure 5.2 in L432, 455), inconsistent font size in Fig. 3, short, uninformative captions for Tables 2-3

[1] S4L: Self-Supervised Semi-Supervised Learning. Zhai et al., 2019

[2] Revisiting Consistency Regularization for Semi-Supervised Learning. Fan et al., 2022

[3] A Survey on Deep Semi-Supervised Learning. Yang et al., 2022
Yes
No ethics review needed.
1. Can the authors provide more implementation details? What are the actions (the text describing the transformation) in each dataset (esp Epic Kitchen) look like? What transformations are considered for each dataset/experimental setup? 
2. In table 2, the numbers are showing that AB_simclr is worse than simclr in `ImageNet` and `EK Action recog` columns. Can the author elaborate on this?
- Novel idea: instead of drawing different views from the same image closer, the paper proposes to use description of the transformations to connect them together.
- Wide range of evaluation datasets
The paper proposes to bootstrap the annotations under the unlabeled learning and weakly labeled learning setups. The notion of annotations here are universal including human labels such as captions, bounding boxes, or even pseudo-labels (under unlabeled learning setup). The experiment section demonstrates great improvements popular learning algorithms including SimCLR and CLIP.
- Only considers extend one learning algorithm under each setup. Namely, SimClr for unlabeled data learning and CLIP for weakly labeled learning. I'd expect more than one especially for unlabeled data learning since there exists more variants there.
- The abstract is mentioning incorporating captions, bounding boxes, and other annotations, etc. Yet, the experimental section only demonstrate caption-supervised learning algorithms.
Yes
No ethics review needed.
- Does this method use a pre-training stage to initialize the parameters, or is it simply trained in a self-supervised manner from scratch? From the head figure in page 6, it seems to be a two-stage training framework.
- How well does this method perform when compare with I-JEPA, if trained under the same-scale training data?
1. The motivation of utilizing the web-scale unlabeled images is good. It does not limit the method to smaller-scale ImageNet.
2. The improvement is obvious compared with previous methods, such as SLIP and SiLC.
This paper presents an Annotation Bootstrapping method to learn useful representations from web-crawled unlabeled images. It enforces the self-supervised models to learn to predict the representations of an unseen view based on the given view and the associated transformations from the given view to the unseen view. It achieves better results than previous methods that focus on combing the strength of vision-language contrastive learning (*e.g.*, CLIP)and self-distillation learning (*e.g.*, SimCLR).
1. The method is very similar to the practice of I-JEPA [1], which predicts the representations of masked regions based on the learned representations of the visible regions. Slightly differently, this work does not use the masked regions as the optimization target, but uses another cropped view for prediction. It is hard to tell which practice is better, but they are generally very similar.

2. The paper is not very easy to follow. Specifically, it is hard to know what the proposed method exactly is, before reading Equation (5). I am still unsure whether my understanding of the proposed method is correct, by guessing from Equation (5). The first three figures fail to convey enough information of this method. And the caption of the third figure is missing. More importantly, some important details are missing. For example, what is the structure of $g$? How can it accept both the representations and the transformations as inputs?

3. The method is only evaluated under ViT-S. However, as a self-supervised method, it will be more convincing to provide additional results with (at least) ViT-B. The pre-training data has already reached 8M images, therefore, the adopted ViT-S with only 20M parameters may be inadequate to fully unleash the power of self-supervised paradigm. Although the authors have mentioned this point as the limitation, it is still strongly recommend to provide these results.

4. Some typos should be fixed, *e.g.*, there should be a right bracket in L288, and there are two "we" in L245.

[1] Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture, ICCV 2023
A RL-like approach for pre-training on all types of image data (labeled and unlabeled), focused on learning ``web-scale'' datasets like web-crawls and videos.
@inproceedings{
anonymous2024annotation,
title={Annotation Bootstrapping: Reinforcing Visual Pre-Training using Unlabelled Images},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=PD8JVDg8mB},
note={under review}
}
A common approach to learning from unlabeled images is to train models to satisfy invariances on these images, such as consistency under augmentations or crops. Despite successes on Imagenet, these approaches struggle to learn from larger uncurated datasets like web crawls or video, where such inductive biases only weakly hold. How can we more effectively learn from broader datasets? Instead of training models to be invariant across views, we study an alternative approach encouraging model representations to be \textit{predictive} of important semantics of adjacent views of an image. We concurrently train a model to predict semantic annotations from images (generated either self-supervised, or from auxiliary datasets); and bootstrap the model's semantics by predicting, given a cropped view of an image and the coordinates for a nearby crop, the model's annotation distribution for the neighboring view.  A core strength of this approach is the ability to extract information universally from both unlabelled and labelled image data, incorporating captions, bounding boxes, and other annotations when they are present. Our experiments show that annotation propagation improves pre-training on unlabelled datasets in the wild, including video datasets like EpicKitchens, scene datasets like COCO, and uncurated web-scale image datasets like CC12M.
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visual pretraining self supervised learning bootstrapping
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unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Annotation Bootstrapping: Reinforcing Visual Pre-Training using Unlabelled Images
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