In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].

A pesar de que el tema referente a la problematica de la navegacion autonoma de robots moviles es de interes, el trabajo presentado no necesariamente tendria que serlo. Sin embargo a medida que el lector aborda en profundidad el documento es posible darse cuenta que el trabajo presentado constituye un aporte al tema. Ya desde el titulo se plantea con claridad lo que el lector encontrara en el documento, lo cual se ve reforzado con un planteamiento sencillo del tema por parte del autor facilitando la comprension del trabajo realizado. Por otra parte, se presenta un prototipo cuya implementacion es posible de replicar en laboratorio por quien se sienta interesado en el tema, lo cual contribuye a que este trabajo sea considerado como posible referencia en el ambito educacional.
es

This paper explores different strategies for instance-level image retrieval with deep CNNs. The approach consists of extracting features from a network pre-trained for image classification (e.g. VGG), and post-process them for image retrieval. In other words, the network is off-the-shelf and solely acts as a feature extractor. The post-processing strategies are borrowed from traditional retrieval pipelines relying on hand-crafted features (e.g. SIFT + Fisher Vectors), denoted by the authors as "traditional wisdom". Specifically, the authors examine where to extract features in the network (i.e. features are neurons activations of a convolution layer), which type of feature aggregation and normalization performs best, whether resizing images helps, whether combining multiple scales helps, and so on. While this type of experimental study is reasonable and well motivated, it suffers from a huge problem. Namely it "ignores" 2 major recent works that are in direct contradictions with many claims of the paper ([a] "End-to-end Learning of Deep Visual Representations for Image Retrieval" by Gordo et al. and [b] "CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples" by Radenovic et al., both ECCV'16 papers). These works have shown that training for retrieval can be achieved with a siamese architectures and have demonstrated outstanding performance. As a result, many claims and findings of the paper are either outdated, questionable or just wrong. Here are some of the misleading claims: - "Features aggregated from these feature maps have been exploited for image retrieval tasks and achieved state-of-the-art performances in recent years." Until [a] (not cited), the state-of-the-art was still largely dominated by sparse invariant features based methods (see last Table in [a]). - "the proposed method [...] outperforms the state-of-the-art methods on four typical datasets" That is not true, for the same reasons than above, and also because the state-of-the-art is now dominated by [a] and [b]. - "Also in situations where a large numbers of training samples are not available, instance retrieval using unsupervised method is still preferable and may be the only option.". This is a questionable opinion. The method exposed in "End-to-end Learning of Deep Visual Representations for Image Retrieval" by Gordo et al. outperforms the state-of-the-art on the UKB dataset (3.84 without QE or DBA) whereas it was trained for landmarks retrieval and not objects, i.e. in a different retrieval context. This demonstrates that in spite of insufficient training data, training is still possible and beneficial. - Finally, most findings are not even new or surprising (e.g. aggregate several regions in a multi-scale manner was already achieved by Tolias at al, etc.). So the interest of the paper is limited overall. In addition, there are some problems in the experiments. For instance, the tuning experiments are only conducted on the Oxford dataset and using a single network (VGG-19), whereas it is not clear whether these conditions are well representative of all datasets and all networks (it is well known that the Oxford dataset behaves very differently than the Holidays dataset, for instance). In addition, tuning is performed very aggressively, making it look like the authors are tuning on the test set (e.g. see Table 3). To conclude, the paper is one year too late with respect to recent developments in the state of the art.
en

The authors propose a Gated Muiltimodal Unit to combine multi-modal information (visual and textual). They also collect a large dataset of movie summers and posters. Overall, the reviewers were quite positive, while AR4 points to related models and feels that the contribution in the current version is too weak for ICLR. The AC read the paper and the authors responses but tends to agree with AR4. The authors are encouraged to strengthen their work and resubmit to a future conference.
en