466 citations · 2.6k across the 60 of their papers we have counts for
15 papers · 1 filter
Auto-Encoding Scene Graphs for Image Captioning
Xu Yang, Kaihua Tang, Hanwang Zhang +1
We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoder-decoder image captioning framework for more human-like captions. Intuitive…
Learning to Compose Dynamic Tree Structures for Visual Contexts
Kaihua Tang, Hanwang Zhang, Baoyuan Wu +2
We propose to compose dynamic tree structures that place the objects in an image into a visual context, helping visual reasoning tasks such as scene graph generation and visual Q&A…
Learning to Assemble Neural Module Tree Networks for Visual Grounding
Daqing Liu, Hanwang Zhang, Feng Wu +1
Visual grounding, a task to ground (i.e., localize) natural language in images, essentially requires composite visual reasoning. However, existing methods over-simplify the composi…
Recursive Visual Attention in Visual Dialog
Yulei Niu, Hanwang Zhang, Manli Zhang +3
Visual dialog is a challenging vision-language task, which requires the agent to answer multi-round questions about an image. It typically needs to address two major problems: (1)…
Counterfactual Critic Multi-Agent Training for Scene Graph Generation
Long Chen, Hanwang Zhang, Jun Xiao +3
Scene graphs -- objects as nodes and visual relationships as edges -- describe the whereabouts and interactions of the things and stuff in an image for comprehensive scene understa…
Explainable and Explicit Visual Reasoning over Scene Graphs
Jiaxin Shi, Hanwang Zhang, Juanzi Li
We aim to dismantle the prevalent black-box neural architectures used in complex visual reasoning tasks, into the proposed eXplainable and eXplicit Neural Modules (XNMs), which adv…
Learning to Embed Sentences Using Attentive Recursive Trees
Jiaxin Shi, Lei Hou, Juanzi Li +2
Sentence embedding is an effective feature representation for most deep learning-based NLP tasks. One prevailing line of methods is using recursive latent tree-structured networks…
DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization
Jiaxin Shi, Chen Liang, Lei Hou +3
We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization. Given any document-summary pair, we estimate a salience scor…
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks
Hang Gao, Zheng Shou, Alireza Zareian +2
Deep neural networks suffer from over-fitting and catastrophic forgetting when trained with small data. One natural remedy for this problem is data augmentation, which has been rec…
Stochastic Dynamics for Video Infilling
Qiangeng Xu, Hanwang Zhang, Weiyue Wang +2
In this paper, we introduce a stochastic dynamics video infilling (SDVI) framework to generate frames between long intervals in a video. Our task differs from video interpolation w…
Context-Aware Visual Policy Network for Sequence-Level Image Captioning
Daqing Liu, Zheng-Jun Zha, Hanwang Zhang +2
Many vision-language tasks can be reduced to the problem of sequence prediction for natural language output. In particular, recent advances in image captioning use deep reinforceme…
Shuffle-Then-Assemble: Learning Object-Agnostic Visual Relationship Features
Xu Yang, Hanwang Zhang, Jianfei Cai
Due to the fact that it is prohibitively expensive to completely annotate visual relationships, i.e., the (obj1, rel, obj2) triplets, relationship models are inevitably biased to o…
Discrete Factorization Machines for Fast Feature-based Recommendation
Han Liu, Xiangnan He, Fuli Feng +3
User and item features of side information are crucial for accurate recommendation. However, the large number of feature dimensions, e.g., usually larger than 10^7, results in expe…
Learning to Guide Decoding for Image Captioning
Wenhao Jiang, Lin Ma, Xinpeng Chen +2
Recently, much advance has been made in image captioning, and an encoder-decoder framework has achieved outstanding performance for this task. In this paper, we propose an extensio…
Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder
Jingkuan Song, Hanwang Zhang, Xiangpeng Li +3
Existing video hash functions are built on three isolated stages: frame pooling, relaxed learning, and binarization, which have not adequately explored the temporal order of video…