23 citations · 36 across the 8 of their papers we have counts for
8 papers
JAB: Joint Adversarial Prompting and Belief Augmentation
Ninareh Mehrabi, Palash Goyal, Anil Ramakrishna +6
With the recent surge of language models in different applications, attention to safety and robustness of these models has gained significant importance. Here we introduce a joint…
Scalable and Accurate Self-supervised Multimodal Representation Learning without Aligned Video and Text Data
Vladislav Lialin, Stephen Rawls, David Chan +3
Scaling up weakly-supervised datasets has shown to be highly effective in the image-text domain and has contributed to most of the recent state-of-the-art computer vision and multi…
Disentangled Action Recognition with Knowledge Bases
Zhekun Luo, Shalini Ghosh, Devin Guillory +3
Action in video usually involves the interaction of human with objects. Action labels are typically composed of various combinations of verbs and nouns, but we may not have trainin…
Regularize, Expand and Compress: Multi-task based Lifelong Learning via NonExpansive AutoML
Jie Zhang, Junting Zhang, Shalini Ghosh +4
Lifelong learning, the problem of continual learning where tasks arrive in sequence, has been lately attracting more attention in the computer vision community. The aim of lifelong…
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention
Shalini Ghosh, Giedrius Burachas, Arijit Ray +1
In this paper, we present a novel approach for the task of eXplainable Question Answering (XQA), i.e., generating natural language (NL) explanations for the Visual Question Answeri…
MICIK: MIning Cross-Layer Inherent Similarity Knowledge for Deep Model Compression
Jie Zhang, Xiaolong Wang, Dawei Li +3
State-of-the-art deep model compression methods exploit the low-rank approximation and sparsity pruning to remove redundant parameters from a learned hidden layer. However, they pr…