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Works188 from public data
- 1,990
- Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly1,908
A new zero-shot learning dataset is proposed, the Animals with Attributes 2 (AWA2) dataset which is made publicly available both in terms of image features and the images themselves and compares and analyzes a significant number of the state-of-the-art methods in depth.
- Feature Generating Networks for Zero-Shot Learning1,068
A novel generative adversarial network (GAN) that synthesizes CNN features conditioned on class-level semantic information, offering a shortcut directly from a semantic descriptor of a class to a class-conditional feature distribution.
- Zero-Shot Learning — The Good, the Bad and the Ugly987
It is argued that it is time to take a step back and to analyze the status quo of the area, given the fact that there is no agreed upon zero-shot learning benchmark, and defines a new benchmark by unifying both the evaluation protocols and data splits.
- Learning Deep Representations of Fine-Grained Visual Descriptions933
This model achieves strong performance on zero-shot text-based image retrieval and significantly outperforms the attribute-based state-of-the-art for zero- shot classification on the Caltech-UCSD Birds 200-2011 dataset.
- Label-Embedding for Image Classification871
This work proposes to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors, and introduces a function that measures the compatibility between an image and a label embedding.
- Latent Embeddings for Zero-Shot Classification755
A novel latent embedding model for learning a compatibility function between image and class embeddings, in the context of zero-shot classification, that improves the state-of-the-art for various classembeddings consistently on three challenging publicly available datasets for the zero- shot setting.
- Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders716
This work proposes a model where a shared latent space of image features and class embeddings is learned by modality-specific aligned variational autoencoders, and align the distributions learned from images and from side-information to construct latent features that contain the essential multi-modal information associated with unseen classes.
- 711
- Generating Visual Explanations660
A new model is proposed that focuses on the discriminating properties of the visible object, jointly predicts a class label, and explains why the predicted label is appropriate for the image, and generates sentences that realize a global sentence property, such as class specificity.
- 645
- F-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning589
A conditional generative model that combines the strength of VAE and GANs and in addition, via an unconditional discriminator, learns the marginal feature distribution of unlabeled images is developed.
- Multimodal Explanations: Justifying Decisions and Pointing to the Evidence485
It is quantitatively shown that training with the textual explanations not only yields better textual justification models, but also better localizes the evidence that supports the decision, supporting the thesis that multimodal explanation models offer significant benefits over unimodal approaches.
- Textual Explanations for Self-Driving Vehicles481
A new approach to introspective explanations is proposed which uses a visual (spatial) attention model to train a convolutional network end-to-end from images to the vehicle control commands, and two approaches to attention alignment, strong- and weak-alignment are explored.
- 406
- 300
- Explainable AI: The New 42?300
Explainable AI is not a new field but the evolution of formal reasoning architectures to incorporate principled probabilistic reasoning helped address the capture and use of uncertain knowledge.
- Grounding Visual Explanations242
A phrase-critic model to refine generated candidate explanations augmented with flipped phrases to improve the textual explanation quality of fine-grained classification decisions on the CUB dataset by mentioning phrases that are grounded in the image.
- Semi-Supervised and Unsupervised Deep Visual Learning: A Survey228
This survey reviews the recent advanced deep learning algorithms on semi- supervised learning and unsupervised learning for visual recognition from a unified perspective and proposes a unified taxonomy to offer a holistic understanding of the state-of-the-art in these areas.
- Evaluating Weakly Supervised Object Localization Methods Right215
It is argued that WSOL task is ill-posed with only image-level labels, and a new evaluation protocol is proposed where full supervision is limited to only a small held-out set not overlapping with the test set.
- Exploiting Saliency for Object Segmentation from Image Level Labels211
This paper proposes using a saliency model as additional information and hereby exploit prior knowledge on the object extent and image statistics and shows how to combine both information sources in order to recover 80% of the fully supervised performance of pixel-wise semantic labelling.
- Learning Graph Embeddings for Compositional Zero-shot Learning205
A novel graph formulation called Compositional Graph Embedding (CGE) that learns image features, compositional classifiers and latent representations of visual primitives in an end-to-end manner and significantly outperforms the state of the art on MIT-States and UT-Zappos in the challenging generalized compositional zero-shot setting.
- 205
- Open World Compositional Zero-Shot Learning179
While the simple CZSL model achieves state-of-the-art performances in the closed world scenario, the feasibility scores boost the performance of the approach in the open world setting, clearly outperforming the previous state of the art.
- 176
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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