15 citations · 29 across the 10 of their papers we have counts for
10 papers
A Causal Inspired Early-Branching Structure for Domain Generalization
Liang Chen, Yong Zhang, Yibing Song +2
Learning domain-invariant semantic representations is crucial for achieving domain generalization (DG), where a model is required to perform well on unseen target domains. One crit…
Speed Co-Augmentation for Unsupervised Audio-Visual Pre-training
Jiangliu Wang, Jianbo Jiao, Yibing Song +5
This work aims to improve unsupervised audio-visual pre-training. Inspired by the efficacy of data augmentation in visual contrastive learning, we propose a novel speed co-augmenta…
Domain Generalization via Rationale Invariance
Liang Chen, Yong Zhang, Yibing Song +2
This paper offers a new perspective to ease the challenge of domain generalization, which involves maintaining robust results even in unseen environments. Our design focuses on the…
Advancing Visual Grounding with Scene Knowledge: Benchmark and Method
Zhihong Chen, Ruifei Zhang, Yibing Song +2
Visual grounding (VG) aims to establish fine-grained alignment between vision and language. Ideally, it can be a testbed for vision-and-language models to evaluate their understand…
Bridging Vision and Language Encoders: Parameter-Efficient Tuning for Referring Image Segmentation
Zunnan Xu, Zhihong Chen, Yong Zhang +3
Parameter Efficient Tuning (PET) has gained attention for reducing the number of parameters while maintaining performance and providing better hardware resource savings, but few st…
Evolving Semantic Prototype Improves Generative Zero-Shot Learning
Shiming Chen, Wenjin Hou, Ziming Hong +5
In zero-shot learning (ZSL), generative methods synthesize class-related sample features based on predefined semantic prototypes. They advance the ZSL performance by synthesizing u…