10.1k citations · 12.9k across the 89 of their papers we have counts for
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One-Shot Pruning for Fast-adapting Pre-trained Models on Devices
Haiyan Zhao, Guodong Long
Large-scale pre-trained models have been remarkably successful in resolving downstream tasks. Nonetheless, deploying these models on low-capability devices still requires an effect…
Style-Aware Contrastive Learning for Multi-Style Image Captioning
Yucheng Zhou, Guodong Long
Existing multi-style image captioning methods show promising results in generating a caption with accurate visual content and desired linguistic style. However, existing methods ov…
Improving Cross-modal Alignment for Text-Guided Image Inpainting
Yucheng Zhou, Guodong Long
Text-guided image inpainting (TGII) aims to restore missing regions based on a given text in a damaged image. Existing methods are based on a strong vision encoder and a cross-moda…
Multimodal Event Transformer for Image-guided Story Ending Generation
Yucheng Zhou, Guodong Long
Image-guided story ending generation (IgSEG) is to generate a story ending based on given story plots and ending image. Existing methods focus on cross-modal feature fusion but ove…
Task Aligned Generative Meta-learning for Zero-shot Learning
Zhe Liu, Yun Li, Lina Yao +2
Zero-shot learning (ZSL) refers to the problem of learning to classify instances from the novel classes (unseen) that are absent in the training set (seen). Most ZSL methods infer…
Isometric Propagation Network for Generalized Zero-shot Learning
Lu Liu, Tianyi Zhou, Guodong Long +3
Zero-shot learning (ZSL) aims to classify images of an unseen class only based on a few attributes describing that class but no access to any training sample. A popular strategy is…
Confusable Learning for Large-class Few-Shot Classification
Bingcong Li, Bo Han, Zhuowei Wang +2
Few-shot image classification is challenging due to the lack of ample samples in each class. Such a challenge becomes even tougher when the number of classes is very large, i.e., t…
Attribute Propagation Network for Graph Zero-shot Learning
Lu Liu, Tianyi Zhou, Guodong Long +2
The goal of zero-shot learning (ZSL) is to train a model to classify samples of classes that were not seen during training. To address this challenging task, most ZSL methods relat…
Multi-modality Sensor Data Classification with Selective Attention
Xiang Zhang, Lina Yao, Chaoran Huang +4
Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment. H…
Dynamic Concept Composition for Zero-Example Event Detection
Xiaojun Chang, Yi Yang, Guodong Long +2
In this paper, we focus on automatically detecting events in unconstrained videos without the use of any visual training exemplars. In principle, zero-shot learning makes it possib…