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Title: What is image captioning made of?

Paper content:
We hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn ‘distributional similarity’ in a multimodal feature space, by mapping a test image to similar training images in this space and generating a caption from the same space.
To validate our hypothesis, we focus on the ‘image’ side of image captioning, and vary the input image representation but keep the RNN text generation model of a CNN-RNN constant.
We propose a sparse bag-of-objects vector as an interpretable representation to investigate our distributional similarity hypothesis.
We found that image captioning models
(i) are capable of separating structure from noisy input representations;
(ii) experience virtually no significant performance loss when a high dimensional representation is compressed to a lower dimensional space;
(iii) cluster images with similar visual and linguistic information together;
(iv) are heavily reliant on test sets with a similar distribution as the training set;
(v) repeatedly generate the same captions by matching images and ‘retrieving’ a caption in the joint visual-textual space.
Our experiments all point to one fact: that our distributional similarity hypothesis holds.
We conclude that, regardless of the image representation, image captioning systems seem to match images and generate captions in a learned joint image-text semantic subspace.
Image description generation, or image captioning (IC), is the task of automatically generating a textual description for a given image.
The generated text is expected to describe, in a single sentence, what is visually depicted in the image, for example the entities/objects present in the image, their attributes, the actions/activities performed, entity/object interactions (including quantification), the location/scene, etc. (e.g. "a man riding a bike on the street").Significant
progress has been made with end-to-end approaches to tackling this problem, where large-scale parallel image-description datasets such as Flickr30k BID41 and MSCOCO BID4 are used to train a CNN-RNN based neural network IC system BID36 BID17 . Such systems
have demonstrated impressive performance in the COCO captioning challenge 1 according to automatic metrics, seemingly even surpassing human performance in many instances (e.g. CIDEr score > 1.0 vs. human's 0.85) BID3 . However, in
reality, the performance of end-to-end systems is still far from satisfactory according to metrics based on human judgement 2 . Thus, despite
the progress, this task is currently far from being a solved problem.In this paper, we challenge the common assumption that end-to-end IC systems are able to achieve strong performance because they have learned to 'understand' and infer semantic information from visual representations, i.e. they can for example deduce that "a boy is playing football" purely by learning directly from mid-level image features and the corresponding textual descriptions in an implicit manner, without explicitly modeling the presence of boy, ball, green field, etc. in the image. It is believed
that the IC system has managed to infer that the phrase green field is associated with some 'green-like' area in the image and is thus generated in the output description, or that the word boy is generated because of some CNN activations corresponding to a young person. However, there
seems to be no concrete evidence that this is the case. Instead, we hypothesize
that the apparently strong performance of end-to-end systems is attributed to the fact that they are exploiting the distributional similarity in the multimodal feature space. To our best knowledge,
our paper gives the first empirical analysis on visual representations for the task of image captioning.What we mean by 'distributional similarity' is that IC systems essentially attempt to match images from the training set that is most similar to a test image, and generate a caption from the most similar training instances (or generate a 'novel' description from a combination of training instances, for example by 'averaging' the descriptions). Previous work has alluded
to this observation BID16 BID36 , but it has not been thoroughly investigated. This phenomena could also
be in part attributed to the fact that the datasets are repetitive and simplistic, with an almost constant and predictable linguistic structure BID18 BID7 BID36 .In this paper we investigate
the hypothesis of distributional similarity in IC by focusing on the image side of image captioning. Most previous work has concentrated
on the text side of image captioning, e.g. by optimizing the language modelling capabilities of the RNN BID27 BID19 to improve its performance on automatic metrics. While there have been efforts on improving
IC by utilizing or modeling images more effectively, for example by using attention over mid-level image features and high-level object proposals BID1 , in this work we are specifically interested in interpretability and we focus on using a simpler (and faster) model for empirical evaluation. We explore the basic yet effective CNN-RNN
model BID17 , and investigate the representational contributions while keeping the RNN generator constant. More advanced models can be considered specific
variants of BID17 .It is worth noting that we are interested in demonstrating
the phenomenon of distributional similarity in IC, rather than achieving or improving state-of-the-art performance, As such, we do not resort to fine-tuning or extensive hyperparameter optimization or ensembles. Therefore, our model is not comparable to state-of-the-art
models such as BID36 , which optimize IC by fine-tuning the image representations, exploring beam size, scheduled sampling, and using ensemble models. Instead, we vary only the image representation to demonstrate
that end-to-end IC systems utilize distributional similarity on the image side to generate captions, regardless of the image representation used.Our main contributions are:• An IC experiment where we vary the input image representation but keep the RNN text generation model constant (Section 3). This experiment demonstrates that regardless of the image representation
(a continuous image embedding or a sparse, low-dimensional vector), end-to-end IC systems seem to utilize a visual-semantic subspace for IC.• The introduction of a simple, sparse bag-of-objects representation that
contains information about the presence of objects in the images. We use this as a tool to investigate the contribution of images in the image
captioning framework.• The introduction of pseudo-random vectors derived from object-level representations
as a means to evaluate IC systems. Our results show that end-to-end models in this framework are remarkably capable of separating
structure from noisy input representations.• An experiment where IC models are conditioned on image representations factorized and compresssed
to a lower dimensional space (Section 4.1). We show that high dimensional image embeddings that are factorized to a lower dimensional representation
and used as input to an IC model result in virtually no significant loss in performance, further strengthening our claim that IC models perform similarity matching rather than image understanding.• An analysis of different image representations and their transformed representations (Sections 4.2 and
4.3). We visualize the initial visual subspace and the learned joint visual semantic subspace and observe that
the visual semantic subspace has learned to cluster images with similar visual and linguistic information together, further validating our claims of distributional similarity.• An experiment where the IC model is tested on an out-of-domain dataset (Section 4.4), which has a slightly
different image distribution. We observe that models, including the state-of-the-art models, show a better performance on test sets that have
a similar distribution as the training. However, their performance deteriorates when the distributions are slightly different.• An analysis on the uniqueness
of captions generated by IC models using different image representations (Section 4.5)
. We hypothesize that the captions are often repeated as they are usually generated by matching images in the joint space
and retrieving a relevant caption. Our experiments validate this claim.Overall, the study suggests that regardless of the representation used, end-to-end
IC models implicitly learn and exploit multimodal similarity spaces rather than performing actual image understanding.This study is in line with the recent work that explore understanding of deep learning models and the representational interpretations BID23 BID32 BID30 and works that have tried to delve into the image captioning task BID7 BID36 . To the best of our knowledge, ours is the first work that investigates IC focusing specifically on image representations
and their effects.
We hypothesized that IC systems essentially exploit a distributional similarity space to 'generate' image captions, by attempting to match a test image to similar training image(s) and generate an image caption from these similar images.
Our study focused on the image side of image captioning:We varied the image representations while keeping the text generation component of an end-toend CNN-RNN model constant.
We found that regardless of the image representation, end-to-end IC systems seem to match images and generate captions in a visual-semantic subspace for IC.
We conclude that:• A sparse, low-dimensional bags-of-objects representation can be used as a tool to investigate the contribution of images in IC; we demonstrated that such a vector is sufficient for generating good image captions; • End-to-end IC models are remarkably capable of separating structure from noisy input representations, as demonstrated by pseudo-random vectors; • End-to-end IC models suffer virtually no significant loss in performance when a high dimensional representation is factorized to a lower dimensional space; • End-to-end IC models have learned a joint visual-textual semantic subspace by clustering images with similar visual and linguistic information together; • End-to-end IC models rely on test sets with a similar distribution as the training set for generating good captions; • End-to-end IC models repeatedly generate the same captions by matching images in the joint visual-textual space and 'retrieving' a caption in the learned joint space.All the observations above strengthen our distributional similarity hypothesis -that end-to-end IC performs image matching and generates captions for a test image from similar image(s) from the training set -rather than performing actual image understanding.
Our findings provide novel insights into what end-to-end IC systems are actually doing, which previous work only suggests or hints at without concretely demonstrating the distributional similarity hypothesis.
We believe our findings are important for the IC community to further advance image captioning in a more informed manner.
(c) Bag of objects: person (1), tie (1) Figure 5 : Example outputs from our system with different representations, the sub-captions indicate the annotation along with the frequency in braces.
We also show the CIDEr score and the difference in CIDEr score relative to the Bag of Objects representation.A ANALYSIS ON GENERATED CAPTIONSHere, we provide a qualitative analysis of different image representations presented and gain some insights into how they contribute to the the IC task.
The Bag of Objects representation led to a strong performance in IC despite being extremely sparse and low-dimensional (80 dimensions).
Analyzing the test split, we found that each vector consists of only 2.86 non-zero entries on average (standard deviation 1.8, median 2).
Thus, with the minimal information being provided to the generator RNN, we find it surprising that it is able to perform so well.We compare the output of the remaining models against the Bag of Objects representation by investigating what each representation adds to or subtracts from this simple, yet strong model.
We start by selecting images (from the test split) annotated with the exact same Bag of Objects representation -which should result in the same caption.
For our qualitative analysis, several sets of one to three MSCOCO categories were manually chosen.
For each set, images were selected such that there is exactly one instance of each category in the set and zero for others.
We then shortlisted images where the captions generated by the Bag of Objects model produced the five highest and five lowest CIDEr scores (ten images per set).
We then compare the captions sampled for each of the other representations.
Figure 5 shows some example outputs from this analysis.
In Figure 5a , Bag of Objects achieved a high CIDEr score despite only being given "bird" as input, mainly by 'guessing' that the bird will be perching/sitting on a branch.
The object-based Softmax (VGG and ResNet) models gave an even more accurate description as "owl" is the top-1 prediction of both representations (96% confidence for VGG, 77% for ResNet).
Places365 predicted "swamp" and "forest".
The Penultimate features on the other hand struggled with representing the images correctly.
In Figure 5b , Bag of Objects struggled with lack of information (only "airplane" is given), the Softmax features mainly predicted "chainlink fence", Places365 predicted "kennel" (hence the dog description), and it most likely that Penultimate has captured the fence-like features in the image rather than the plane.
In Figure 5c , the Softmax features generally managed to generate a caption describing a woman despite not explicitly containing the 'woman' category.
This is because other correlated categories were predicted, such as "mask", "wig", "perfume", "hairspray" and in the case of Places365 "beauty salon" and "dressing room".

assistant: text: This paper presents an empirical analysis on the role of different types of image representations and probes the properties of these representations for the task of image captioning.