95 citations · 246 across the 15 of their papers we have counts for
15 papers
Unsupervised Discovery of Interpretable Directions in h-space of Pre-trained Diffusion Models
Zijian Zhang, Luping Liu, Zhijie Lin +2
We propose the first unsupervised and learning-based method to identify interpretable directions in h-space of pre-trained diffusion models. Our method is derived from an existing…
Detector Guidance for Multi-Object Text-to-Image Generation
Luping Liu, Zijian Zhang, Yi Ren +3
Diffusion models have demonstrated impressive performance in text-to-image generation. They utilize a text encoder and cross-attention blocks to infuse textual information into ima…
ShiftDDPMs: Exploring Conditional Diffusion Models by Shifting Diffusion Trajectories
Zijian Zhang, Zhou Zhao, Jun Yu +1
Diffusion models have recently exhibited remarkable abilities to synthesize striking image samples since the introduction of denoising diffusion probabilistic models (DDPMs). Their…
Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Models
Zijian Zhang, Zhou Zhao, Zhijie Lin
Diffusion Probabilistic Models (DPMs) have shown a powerful capacity of generating high-quality image samples. Recently, diffusion autoencoders (Diff-AE) have been proposed to expl…
Improving Visual-Semantic Embedding with Adaptive Pooling and Optimization Objective
Zijian Zhang, Chang Shu, Ya Xiao +7
Visual-Semantic Embedding (VSE) aims to learn an embedding space where related visual and semantic instances are close to each other. Recent VSE models tend to design complex struc…
Towards Explainability in NLP: Analyzing and Calculating Word Saliency through Word Properties
Jialiang Dong, Zhitao Guan, Longfei Wu +2
The wide use of black-box models in natural language processing brings great challenges to the understanding of the decision basis, the trustworthiness of the prediction results, a…
FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop
Zijian Zhang, Koustav Rudra, Avishek Anand
Fact-checking on the Web has become the main mechanism through which we detect the credibility of the news or information. Existing fact-checkers verify the authenticity of the inf…
ICAF: Iterative Contrastive Alignment Framework for Multimodal Abstractive Summarization
Zijian Zhang, Chang Shu, Youxin Chen +3
Integrating multimodal knowledge for abstractive summarization task is a work-in-progress research area, with present techniques inheriting fusion-then-generation paradigm. Due to…
Position-based Contributive Embeddings for Aspect-Based Sentiment Analysis
Zijian Zhang, Chenxin Zhang, Jiangfeng Li +1
Aspect-based sentiment analysis (ABSA), exploring sentiment polarity of aspect-given sentence, is a fine-grained task in the field of nature language processing. Previously researc…
Learnt Sparsification for Interpretable Graph Neural Networks
Mandeep Rathee, Zijian Zhang, Thorben Funke +2
Graph neural networks (GNNs) have achieved great success on various tasks and fields that require relational modeling. GNNs aggregate node features using the graph structure as ind…
Explain and Predict, and then Predict Again
Zijian Zhang, Koustav Rudra, Avishek Anand
A desirable property of learning systems is to be both effective and interpretable. Towards this goal, recent models have been proposed that first generate an extractive explanatio…
Convolutional Hierarchical Attention Network for Query-Focused Video Summarization
Shuwen Xiao, Zhou Zhao, Zijian Zhang +2
Previous approaches for video summarization mainly concentrate on finding the most diverse and representative visual contents as video summary without considering the user's prefer…
XDeep: An Interpretation Tool for Deep Neural Networks
Fan Yang, Zijian Zhang, Haofan Wang +2
XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers. Overall, XDeep takes a trained deep neural network (DNN) as the in…
Contextual Local Explanation for Black Box Classifiers
Zijian Zhang, Fan Yang, Haofan Wang +1
We introduce a new model-agnostic explanation technique which explains the prediction of any classifier called CLE. CLE gives an faithful and interpretable explanation to the predi…
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
Haofan Wang, Zifan Wang, Mengnan Du +5
Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions. In this paper, w…