Zero-Shot Visual Recognition via Bidirectional Latent Embedding (1607.02104v4)

Abstract: Zero-shot learning for visual recognition, e.g., object and action recognition, has recently attracted a lot of attention. However, it still remains challenging in bridging the semantic gap between visual features and their underlying semantics and transferring knowledge to semantic categories unseen during learning. Unlike most of the existing zero-shot visual recognition methods, we propose a stagewise bidirectional latent embedding framework to two subsequent learning stages for zero-shot visual recognition. In the bottom-up stage, a latent embedding space is first created by exploring the topological and labeling information underlying training data of known classes via a proper supervised subspace learning algorithm and the latent embedding of training data are used to form landmarks that guide embedding semantics underlying unseen classes into this learned latent space. In the top-down stage, semantic representations of unseen-class labels in a given label vocabulary are then embedded to the same latent space to preserve the semantic relatedness between all different classes via our proposed semi-supervised Sammon mapping with the guidance of landmarks. Thus, the resultant latent embedding space allows for predicting the label of a test instance with a simple nearest-neighbor rule. To evaluate the effectiveness of the proposed framework, we have conducted extensive experiments on four benchmark datasets in object and action recognition, i.e., AwA, CUB-200-2011, UCF101 and HMDB51. The experimental results under comparative studies demonstrate that our proposed approach yields the state-of-the-art performance under inductive and transductive settings.

• The paper proposes a novel bidirectional latent embedding framework mapping visual features via SLPP and semantics via LSM into a shared latent space for zero-shot recognition.
• Experiments on AwA, CUB, UCF101, and HMDB51 datasets demonstrated state-of-the-art recognition accuracy for the framework in zero-shot learning settings.
• The findings advance zero-shot learning theory and enable better classification of new categories in dynamic systems like autonomous image analysis.

A Study on Zero-Shot Visual Recognition via Bidirectional Latent Embedding

The paper presented by Wang and Chen centers on a novel framework, amalgamating a structured approach to tackling zero-shot learning (ZSL), particularly for visual recognition tasks such as object and human action recognition. Confronted by the obstacles inherent in ZSL, namely the semantic gap and hubness, this paper introduces a bidirectional latent embedding structure aimed at overcoming these issues by bridging the gap between visual features and high-level semantic representations without prior exposure to class samples during training.

The bidirectional latent embedding framework described operates in two phases: a bottom-up stage and a top-down stage. The bottom-up stage involves mapping the visual representations of known classes to a latent space via a supervised subspace learning algorithm, specifically the Supervised Locality Preserving Projection (SLPP). This phase not only facilitates the maintenance of the intrinsic topological structure of the data but simultaneously increases class discriminability. In contrast, the top-down stage capitalizes on the latent space established, embedding unseen-class semantics into this space through a semi-supervised adaptation of the Sammon mapping, termed the Landmark-based Sammon Mapping (LSM). This ensures the semantic relatedness is preserved across all class types, both seen and unseen, derived from a common vocabulary.

A thorough evaluation executed by the authors involved four benchmark datasets: AwA, CUB-200-2011, UCF101, and HMDB51. Results indicated a state-of-the-art performance both under inductive and transductive learning settings. In numerical terms, the proposed framework showed improved recognition accuracy across the aforementioned datasets, substantiating its efficacy over existing methodologies. However, it's noteworthy that the attributes within datasets such as UCF101 and HMDB51 require manual annotation, presenting an overhead which is gracefully mitigated via the framework's robust handling of multiple semantic representations, including word vectors, expanding its versatility.

The implications of these findings traverse both theoretical and practical dimensions. Theoretically, the paper elucidates a pathway to ameliorate the transferability of knowledge between semantically proximal and syntactically distant classes, directly addressing the ZSL gap and enriching domain understanding. Practically, such advancements suggest enhancements in classification tasks where category growth is dynamic and resource constraints preclude exhaustive training data acquisition. Recognizing new and unseen classes effectively can streamline developments in intelligent systems, portending significant impacts on fields like autonomous image classification and dynamic video analysis.

Future course may involve further exploration of the embedding dimension's adaptability and the latent space's scalability with evolving datasets. The substantial inclusion of auxiliary sources, such as large-scale unannotated databases could bolster the semantic embedding process, thereby further narrowing the perceptual gap. With ZSL techniques becoming ever-pertinent, ongoing refinement and augmentation of models like those proposed herein hold promising prospects for developing increasingly autonomous and intuitive visual recognition systems.