activity
20202023
most citedLearning to Infer Unseen Single-/Multi-Attribute-Object Compositions with Graph Networks

7 citations · 11 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2023★ 1 cited

FFINet: Future Feedback Interaction Network for Motion Forecasting

Miao Kang, Shengqi Wang, Sanping Zhou +3

Motion forecasting plays a crucial role in autonomous driving, with the aim of predicting the future reasonable motions of traffic agents. Most existing methods mainly model the hi…

cs.CV2023

MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering

Jingjing Jiang, Nanning Zheng

Recently, finetuning pretrained Vision-Language Models (VLMs) has been a prevailing paradigm for achieving state-of-the-art performance in Visual Question Answering (VQA). However,…

cs.CV2022★ 3 cited

Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering

Jingjing Jiang, Ziyi Liu, Nanning Zheng

Benefiting from large-scale pretrained vision language models (VLMs), the performance of visual question answering (VQA) has approached human oracles. However, finetuning such mode…

cs.CV2021

LiVLR: A Lightweight Visual-Linguistic Reasoning Framework for Video Question Answering

Jingjing Jiang, Ziyi Liu, Nanning Zheng

Video Question Answering (VideoQA), aiming to correctly answer the given question based on understanding multi-modal video content, is challenging due to the rich video content. Fr…

cs.CV2021

X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering

Jingjing Jiang, Ziyi Liu, Yifan Liu +2

Encouraging progress has been made towards Visual Question Answering (VQA) in recent years, but it is still challenging to enable VQA models to adaptively generalize to out-of-dist…

cs.CV2020★ 7 cited

Learning to Infer Unseen Single-/Multi-Attribute-Object Compositions with Graph Networks

Hui Chen, Jingjing Jiang, Nanning Zheng

Inferring the unseen attribute-object composition is critical to make machines learn to decompose and compose complex concepts like people. Most existing methods are limited to the…