# Transductive Zero-Shot Learning with Visual Structure Constraint

arXiv: https://arxiv.org/abs/1901.01570 · HTML: https://mlsanity.com/paper/1901.01570/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2019.

## Built on by (most cited first, 30 of 36)

- [Vision-Language Models in Remote Sensing: Current Progress and Future Trends](https://mlsanity.com/paper/2305.05726/) (2023)
- [PromptKD: Unsupervised Prompt Distillation for Vision-Language Models](https://mlsanity.com/paper/2403.02781/) (2024)
- [Evolving Semantic Prototype Improves Generative Zero-Shot Learning](https://mlsanity.com/paper/2306.06931/) (2023)
- [Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data](https://mlsanity.com/paper/2406.10502/) (2024)
- [Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning](https://mlsanity.com/paper/2404.07713/) (2024)
- [Zero-shot Node Classification with Decomposed Graph Prototype Network](https://mlsanity.com/paper/2106.08022/) (2021)
- [Learning from Multiple Noisy Partial Labelers](https://mlsanity.com/paper/2106.04530/) (2021)
- [Towards Novel Target Discovery Through Open-Set Domain Adaptation](https://mlsanity.com/paper/2105.02432/) (2021)
- [Hardness Sampling for Self-Training Based Transductive Zero-Shot Learning](https://mlsanity.com/paper/2106.00264/) (2021)
- [Attribute-Modulated Generative Meta Learning for Zero-Shot Classification](https://mlsanity.com/paper/2104.10857/) (2021)
- [Prototype Completion for Few-Shot Learning](https://mlsanity.com/paper/2108.05010/) (2021)
- [Bi-directional Distribution Alignment for Transductive Zero-Shot Learning](https://mlsanity.com/paper/2303.08698/) (2023)
- [Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains](https://mlsanity.com/paper/2107.07497/) (2021)
- [A Near-Linear Time Algorithm for the Chamfer Distance](https://mlsanity.com/paper/2307.03043/) (2023)
- [GenZSL: Generative Zero-Shot Learning Via Inductive Variational Autoencoder](https://mlsanity.com/paper/2505.11882/) (2025)
- [Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification](https://mlsanity.com/paper/2112.12927/) (2021)
- ["Zero-Shot" Point Cloud Upsampling](https://mlsanity.com/paper/2106.13765/) (2021)
- [An Iterative Co-Training Transductive Framework for Zero Shot Learning](https://mlsanity.com/paper/2203.16041/) (2022)
- [PROTOtypical Logic Tensor Networks (PROTO-LTN) for Zero Shot Learning](https://mlsanity.com/paper/2207.00433/) (2022)
- [Evolutionary Generalized Zero-Shot Learning](https://mlsanity.com/paper/2211.13174/) (2022)
- [HardBoost: Boosting Zero-Shot Learning with Hard Classes](https://mlsanity.com/paper/2201.05479/) (2022)
- [MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning](https://mlsanity.com/paper/2203.01482/) (2022)
- [Joint one-sided synthetic unpaired image translation and segmentation for colorectal cancer prevention](https://mlsanity.com/paper/2307.11253/) (2023)
- [Generate, Transduct, Adapt: Iterative Transduction with VLMs](https://mlsanity.com/paper/2501.06031/) (2025)
- [Transductive Zero-Shot Learning by Decoupled Feature Generation](https://mlsanity.com/paper/2102.03266/) (2021)
- [Consistency-Driven Calibration and Matching for Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2506.19558/) (2025)
- [External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery](https://mlsanity.com/paper/2107.10504/) (2021)
- [Structure-Aware Feature Generation for Zero-Shot Learning](https://mlsanity.com/paper/2108.07032/) (2021)
- [KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification](https://mlsanity.com/paper/2308.08563/) (2023)
- [Improved Feature Generating Framework for Transductive Zero-shot Learning](https://mlsanity.com/paper/2412.18282/) (2024)

## Abstract (arXiv)

To recognize objects of the unseen classes, most existing Zero-Shot Learning(ZSL) methods first learn a compatible projection function between the common semantic space and the visual space based on the data of source seen classes, then directly apply it to the target unseen classes. However, in real scenarios, the data distribution between the source and target domain might not match well, thus causing the well-known domain shift problem. Based on the observation that visual features of test instances can be separated into different clusters, we propose a new visual structure constraint on class centers for transductive ZSL, to improve the generality of the projection function (i.e. alleviate the above domain shift problem). Specifically, three different strategies (symmetric Chamfer-distance, Bipartite matching distance, and Wasserstein distance) are adopted to align the projected unseen semantic centers and visual cluster centers of test instances. We also propose a new training strategy to handle the real cases where many unrelated images exist in the test dataset, which is not considered in previous methods. Experiments on many widely used datasets demonstrate that the proposed visual structure constraint can bring substantial performance gain consistently and achieve state-of-the-art results. The source code is available at urlhttps://github.com/raywzy/VSC.