# Zero-Shot Visual Recognition via Bidirectional Latent Embedding

arXiv: https://arxiv.org/abs/1607.02104 · HTML: https://mlsanity.com/paper/1607.02104/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2016.

## Built on by (most cited first, 27 of 27)

- [FREE: Feature Refinement for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2107.13807/) (2021)
- [HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning](https://mlsanity.com/paper/2109.15163/) (2021)
- [MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning](https://mlsanity.com/paper/2203.03137/) (2022)
- [Audio-visual Generalised Zero-shot Learning with Cross-modal Attention and Language](https://mlsanity.com/paper/2203.03598/) (2022)
- [Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization](https://mlsanity.com/paper/2308.03950/) (2023)
- [Multimodal Open-Vocabulary Video Classification via Pre-Trained Vision and Language Models](https://mlsanity.com/paper/2207.07646/) (2022)
- [Attribute Prototype Network for Any-Shot Learning](https://mlsanity.com/paper/2204.01208/) (2022)
- [Learning Robust Visual-semantic Mapping for Zero-shot Learning](https://mlsanity.com/paper/2104.05668/) (2021)
- [Cross-modal Representation Learning for Zero-shot Action Recognition](https://mlsanity.com/paper/2205.01657/) (2022)
- [XOV-Action: Towards Generalizable Open-Vocabulary Action Recognition](https://mlsanity.com/paper/2403.01560/) (2024)
- [A New Split for Evaluating True Zero-Shot Action Recognition](https://mlsanity.com/paper/2107.13029/) (2021)
- [Zero-Shot Aerial Object Detection with Visual Description Regularization](https://mlsanity.com/paper/2402.18233/) (2024)
- [Zero-Shot Skeleton-based Action Recognition with Dual Visual-Text Alignment](https://mlsanity.com/paper/2409.14336/) (2024)
- [Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2101.03292/) (2021)
- [ActionHub: A Large-scale Action Video Description Dataset for Zero-shot Action Recognition](https://mlsanity.com/paper/2401.11654/) (2024)
- [Tell me what you see: A zero-shot action recognition method based on natural language descriptions](https://mlsanity.com/paper/2112.09976/) (2021)
- [EGANS: Evolutionary Generative Adversarial Network Search for Zero-Shot Learning](https://mlsanity.com/paper/2308.09915/) (2023)
- [Interpretable Zero-Shot Learning with Locally-Aligned Vision-Language Model](https://mlsanity.com/paper/2506.23822/) (2025)
- [ZS-SLR: Zero-Shot Sign Language Recognition from RGB-D Videos](https://mlsanity.com/paper/2108.10059/) (2021)
- [Multi-Modal Zero-Shot Sign Language Recognition](https://mlsanity.com/paper/2109.00796/) (2021)
- [Visual-Semantic Graph Matching Net for Zero-Shot Learning](https://mlsanity.com/paper/2411.11351/) (2024)
- [Global Semantic Descriptors for Zero-Shot Action Recognition](https://mlsanity.com/paper/2209.12061/) (2022)
- [End-to-End Semantic Video Transformer for Zero-Shot Action Recognition](https://mlsanity.com/paper/2203.05156/) (2022)
- [Learning Using Privileged Information for Zero-Shot Action Recognition](https://mlsanity.com/paper/2206.08632/) (2022)
- [Prototype-Guided Curriculum Learning for Zero-Shot Learning](https://mlsanity.com/paper/2508.07771/) (2025)
- [Prototype-Based Semantic Consistency Alignment for Domain Adaptive Retrieval](https://mlsanity.com/paper/2512.04524/) (2025)
- [CEZSAR: A Contrastive Embedding Method for Zero-Shot Action Recognition](https://mlsanity.com/paper/2605.01165/) (2026)

## Abstract (arXiv)

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.