Learning to detect unseen object classes by between-class attribute transfer

Lampert, Christoph H.; Nickisch, Hannes; Harmeling, Stefan

doi:10.1109/cvprw.2009.5206594

Cited by 1,181 publications

(1,863 citation statements)

References 36 publications

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“…Second, the conventional top-down approach imposes weights on certain feature types that are considered optimal in a universal sense; while the bottom-up approach aims to discover a set of discriminative features and quantify their importance specific to each individual. From another perspective, the notion of bottom-up learning can also be interpreted as a process of unsupervised discovering latent attribute (see Section 3.1), which is largely different from existing top-down supervised attribute learning [16,15] that requires exhaustive humanspecified attributes.…”

Section: Introductionmentioning

confidence: 99%

On-the-fly feature importance mining for person re-identification

Liu

Gong

Loy

2014

Pattern Recognition

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Section: Introductionmentioning

confidence: 99%

On-the-fly feature importance mining for person re-identification

Liu

Gong

Loy

2014

Pattern Recognition

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“…In particular, while both visual attributes [18,6,21,11] and linguistic semantic representations such as word vectors [23,7,34] have been independently exploited successfully, it remains unattempted and not straightforward to exploit synergistically multiple semantic 'views'. This is because they are of very different dimensions and types and each suffers from different domain shift effects discussed above.…”

Section: Introductionmentioning

confidence: 99%

“…Such a semantic representation is assumed to be shared between the auxiliary and target datasets. More specifically, apart from class label, each auxiliary data point is labelled by a semantic representation such as visual attributes [18,6,21,11], semantic word vectors [23,7,34] or others [28]. A projection function mapping low-level features to the semantic space is learned from the auxiliary dataset by either classification or regression models.…”

Section: Introductionmentioning

confidence: 99%

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Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation

Hospedales

Xiang

et al. 2014

Lecture Notes in Computer Science

197

206

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Abstract. Most existing zero-shot learning approaches exploit transfer learning via an intermediate-level semantic representation such as visual attributes or semantic word vectors. Such a semantic representation is shared between an annotated auxiliary dataset and a target dataset with no annotation. A projection from a low-level feature space to the semantic space is learned from the auxiliary dataset and is applied without adaptation to the target dataset. In this paper we identify an inherent limitation with this approach. That is, due to having disjoint and potentially unrelated classes, the projection functions learned from the auxiliary dataset/domain are biased when applied directly to the target dataset/domain. We call this problem the projection domain shift problem and propose a novel framework, transductive multi-view embedding, to solve it. It is 'transductive' in that unlabelled target data points are explored for projection adaptation, and 'multi-view' in that both lowlevel feature (view) and multiple semantic representations (views) are embedded to rectify the projection shift. We demonstrate through extensive experiments that our framework (1) rectifies the projection shift between the auxiliary and target domains, (2) exploits the complementarity of multiple semantic representations, (3) achieves state-of-the-art recognition results on image and video benchmark datasets, and (4) enables novel cross-view annotation tasks.

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“…[28] focuses on key frames so essentially treats it as an image interestingness problem, whilst [21] is the first work that proposes benchmark video interestingness datasets and evaluates different features for video interestingness prediction. In a broader sense of attributes [26,11,12,27,13] interestingness can be considered as one type of relative attributes [35], although those attributes, such as how smiling a person is, are much less subjective. Computational models of interestingness Most earlier work casts the aesthetics or interestingness prediction problem as a regression problem [22,7,19,28].…”

Section: Related Workmentioning

confidence: 99%

Interestingness Prediction by Robust Learning to Rank

Hospedales

Xiang

et al. 2014

Lecture Notes in Computer Science

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Abstract. The problem of predicting image or video interestingness from their low-level feature representations has received increasing interest. As a highly subjective visual attribute, annotating the interestingness value of training data for learning a prediction model is challenging. To make the annotation less subjective and more reliable, recent studies employ crowdsourcing tools to collect pairwise comparisons -relying on majority voting to prune the annotation outliers/errors. In this paper, we propose a more principled way to identify annotation outliers by formulating the interestingness prediction task as a unified robust learning to rank problem, tackling both the outlier detection and interestingness prediction tasks jointly. Extensive experiments on both image and video interestingness benchmark datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives.

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Learning to detect unseen object classes by between-class attribute transfer

Cited by 1,181 publications

References 36 publications

On-the-fly feature importance mining for person re-identification

On-the-fly feature importance mining for person re-identification

Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation

Interestingness Prediction by Robust Learning to Rank

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