Source-linked AI summary
Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learning
Maunil R Vyas, Hemanth Venkateswara, Sethuraman Panchanathan
TL;DR
Zero-shot learning must recognize unseen classes from semantic information, while generative approaches remain vulnerable to seen-class overfitting and poor GZSL generalization. LsrGAN uses SR-Loss to transfer semantic relationships into generated visual features, and experiments on seven benchmarks report superior ZSL and GZSL performance.
Problem
Generative zero-shot models can overfit seen classes, producing poor generalized zero-shot performance, while clean semantic features are difficult and costly to obtain.
Method
LsrGAN explicitly transfers semantic relationships from seen to unseen classes through Semantic Regularized Loss, guiding generated visual features to mirror those relationships.
Results
Across seven benchmark datasets, LsrGAN outperforms previous state-of-the-art approaches under both zero-shot and generalized zero-shot learning.
Takeaways & Limitations
Semantic relationship regularization provides an explicit mechanism for transferring knowledge from similar seen classes to unseen classes in generative zero-shot learning.
Abstract
from arXiv · showhide
Zero-shot learning (ZSL) addresses the unseen class recognition problem by leveraging semantic information to transfer knowledge from seen classes to unseen classes. Generative models synthesize the unseen visual features and convert ZSL into a classical supervised learning problem. These generative models are trained using the seen classes and are expected to implicitly transfer the knowledge from seen to unseen classes. However, their performance is stymied by overfitting, which leads to substandard performance on Generalized Zero-Shot learning (GZSL). To address this concern, we propose the novel LsrGAN, a generative model that Leverages the Semantic Relationship between seen and unseen categories and explicitly performs knowledge transfer by incorporating a novel Semantic Regularized Loss (SR-Loss). The SR-loss guides the LsrGAN to generate visual features that mirror the semantic relationships between seen and unseen classes. Experiments on seven benchmark datasets, including the challenging Wikipedia text-based CUB and NABirds splits, and Attribute-based AWA, CUB, and SUN, demonstrates the superiority of the LsrGAN compared to previous state-of-the-art approaches under both ZSL and GZSL. Code is available at https: // github. com/ Maunil/ LsrGAN
1 Introduction
Zero-shot learning transfers semantic knowledge from seen classes to recognize unseen visual categories, but generative approaches face quality gaps and seen-class overfitting. LsrGAN addresses this by transferring semantic relationships through SR-Loss.
- Motivation: Zero-shot learning uses textual descriptions or attributes to transfer knowledge from seen classes to unseen categories.
- Motivation: Generative zero-shot models suffer from synthesized-feature quality gaps and seen-class bias, weakening generalized zero-shot performance.The paper also notes that clean semantic attributes can be labor-intensive and expensive to obtain.
- Proposed approach: The proposed model combines a generative approach with semantic knowledge from noisy text descriptions and semantic attributes.
- Proposed approach: LsrGAN transfers relationships between seen and unseen categories into generated visual features using the Semantic Regularized Loss.For an unseen class such as Dolphin, semantically similar seen classes provide indirect visual references.
- Evaluation: Experiments on seven benchmark datasets report that LsrGAN outperforms previous state-of-the-art approaches.
2 Related work
Earlier zero-shot methods mainly embed visual and semantic information, while generative methods synthesize unseen features but can remain biased toward seen classes. LsrGAN introduces semantic relationship regularization in GAN-based generation to improve generalization.
- Embedding methods: Embedding methods map between visual and semantic spaces, but commonly show bias toward seen classes and substandard generalized zero-shot performance.
- Generative methods: Generative methods synthesize unseen-class features using GANs or VAEs and have achieved state-of-the-art zero-shot results.
- LsrGAN: LsrGAN adds SR-Loss to GANs so inter-class relationships between seen and unseen classes guide unseen-feature generation.
- LsrGAN: The paper positions semantic relationship transfer within generative models as an approach not previously investigated with GANs.
3 Proposed Approach
LsrGAN hallucinates unseen image features from semantic descriptions using a conditional WGAN, while SR-Loss transfers inter-class semantic relationships into the generated visual feature space. Its discriminator aligns feature distributions, its classifier performs category-based alignment, and SR-Loss guides seen and unseen class representations using selected semantic neighbors.
- Model formulation: The model uses seen image features plus seen and unseen semantic features to hallucinate unseen-category image features for zero-shot classification.The generator is conditioned on semantic features and random noise, while the classifier covers seen and unseen categories.
- Feature generator: The conditional WGAN generates image features for seen and unseen categories, with visual pivot regularization aligning seen-class real and generated feature centers.The generator maps concatenated noise and category semantics to image features; visual pivot regularization is applied only where real seen features exist.
- Adversarial feature generation: The discriminator performs real/fake distribution alignment, while the softmax classifier performs category-based conditional alignment on real and generated features.The discriminator is trained with seen-category image features, and the classifier uses generated and real features for seen and unseen classes.
- Semantic relationship regularization: SR-Loss transfers inter-class relationships from semantic space to visual space by matching semantic similarities with similarities between generated class centers.Semantic similarity uses one semantic vector per category, whereas visual similarity uses class-center relationships rather than individual image features.
- Semantic relationship regularization: For each seen class, the method selects the top n_c semantically similar seen categories and trains the generator to preserve those relationships in visual features.The same regularization framework defines objectives for unseen categories using generated feature means; its stated time complexity is O(Bn_c|X|E) + O(C^2|T|E) + O(C^2 log n_cE).
4 Experiments
Experiments evaluate LsrGAN on attribute- and Wikipedia-based datasets using ZSL and GZSL metrics, with comparisons, confidence analysis, and stability tests. LsrGAN generally improves over prior generative approaches, particularly under GZSL, while ZSL performance is weaker on AWA.
- Datasets and setup: Experiments cover attribute-based AWA, CUB, and SUN datasets plus Wikipedia-based CUB and NAB splits, using standardized dataset settings.Attribute-based datasets use ResNet-101 features, while Wikipedia-based datasets use VPDE-net part features and TF-IDF semantic representations.
- Datasets and setup: ZSL uses Top-1 accuracy, while GZSL uses harmonic mean for attribute datasets and Seen-Unseen AUC for Wikipedia-based datasets.Recognition uses nearest-neighbor prediction for Wikipedia descriptions and the discriminator classifier for attribute-based recognition.
- ZSL and GZSL performance: LsrGAN achieves higher ZSL performance than previous state-of-the-art generative models across the attribute- and Wikipedia-based benchmarks.Reported improvements include 1.5%, 3.9%, 0.8%, and 0.44% on Wikipedia-based CUB and NAB splits, and 1.5% and 0.8% on attribute-based CUB and SUN.
- ZSL and GZSL performance: LsrGAN improves GZSL performance by 4.1%, 3.4%, 2.8%, and 0.6% on Wikipedia-based CUB and NAB splits, and by 0.7%, 1.4%, and 0.7% on attribute-based AWA, CUB, and SUN.The reported gains use harmonic mean for attribute datasets and AUC for Wikipedia-based datasets.
- ZSL and GZSL performance: Prior methods show large seen-to-unseen performance drops in GZSL, whereas SR-Loss explicitly transfers knowledge from similar seen classes to unseen classes.Examples on attribute-based CUB include drops from 40% to 1.7% for DAP, 55.8% to 23.9% for GAZSL, and 57.3% to 43.7% for F-GAN.
- Model analysis: Confidence analysis shows F-GAN favoring similar seen classes, while LsrGAN distributes confidence more evenly across seen and unseen classes, despite failing on mouse.The comparison uses top-three guesses for confusing seen and unseen classes on AWA.
- Model analysis: The model maintains stable performance across datasets during GZSL training, and SR-Loss parameters are evaluated through sensitivity analysis.The reported stability analysis tracks harmonic mean and epoch; parameter tuning covers ϵ, λ_sr, and n_c.
5 Conclusions
LsrGAN uses semantic relationships between seen and unseen classes to address seen-class overfitting in GZSL. Experiments on seven benchmarks show superior ZSL and GZSL performance.
- Contributions: LsrGAN addresses seen-class overfitting in GZSL by leveraging semantic relationships between seen and unseen classes.Its Semantic Regularized Loss performs explicit knowledge transfer from seen classes to unseen ones and guides generated visual features to mirror those relationships.
- Contributions: The Semantic Regularized Loss guides LsrGAN to generate visual features that mirror relationships between seen and unseen classes.This provides the mechanism for explicit knowledge transfer across the semantic and visual domains.
- Results: Seven benchmark experiments verify superior performance for LsrGAN under both ZSL and GZSL.The benchmarks include attribute- and Wikipedia-description-based datasets.
6 Appendix
The appendix analyzes SR-Loss complexity and summarizes LsrGAN training. Semantic and visual similarity computations contribute polynomial costs, while training alternates generator and discriminator updates for seen and unseen classes.
- Semantic similarity: The semantic similarity pipeline computes an O(n^2L) cosine-similarity matrix and retrieves K similar classes in O(n^2 log K).Its overall semantic-similarity cost is O(n^2L) + O(n^2 log K).
- Visual similarity: The visual similarity computation costs O(BKV) for a B × K cosine-similarity matrix and O(BKVE) overall across E epochs.B denotes batch size classes, K neighbour classes, and V the visual-feature length.
- SR-Loss complexity: For clean attributes, SR-Loss has complexity O(BKVE) + O(n^2L) + O(n^2 log K).The noisy-text formulation adds O(n^2LE), yielding O(BKVE) + O(n^2LE) + O(n^2 log KE).
- Complexity assessment: The overall noisy-text complexity is linear in E, K, B, V, and L and degree-2 polynomial with a logarithmic factor in total classes.The appendix states that the complexity is not exponential and the running-time cost is manageable.
- Training procedure: LsrGAN trains its generator and discriminator alternately with Adam in separate seen-class and unseen-class phases.The algorithm samples matching seen images and text, then unseen text and noise, while applying the corresponding discriminator, classifier, and generator losses.