5 papers
Intra-Modal Neighbors Never Lie: Rectifying Inter-Modal Noisy Correspondence via Graph-Based Intra-Modal Reasoning
Yang Liu, Wentao Feng, Shu-Dong Huang +2
Large-scale web-harvested datasets have fueled the progress of cross-modal retrieval but inevitably suffer from noisy correspondence, which severely degrades model generalization.…
PCSR: Pseudo-label Consistency-Guided Sample Refinement for Noisy Correspondence Learning
Zhuoyao Liu, Yang Liu, Wentao Feng +1
Cross-modal retrieval aims to align different modalities via semantic similarity. However, existing methods often assume that image-text pairs are perfectly aligned, overlooking No…
HyperNAS: Enhancing Architecture Representation for NAS Predictor via Hypernetwork
Jindi Lv, Yuhao Zhou, Yuxin Tian +3
Time-intensive performance evaluations significantly impede progress in Neural Architecture Search (NAS). To address this, neural predictors leverage surrogate models trained on pr…
Aligning Information Capacity Between Vision and Language via Dense-to-Sparse Feature Distillation for Image-Text Matching
Yang Liu, Wentao Feng, Zhuoyao Liu +2
Enabling Visual Semantic Models to effectively handle multi-view description matching has been a longstanding challenge. Existing methods typically learn a set of embeddings to fin…
GPT-NAS: Evolutionary Neural Architecture Search with the Generative Pre-Trained Model
Caiyang Yu, Xianggen Liu, Yifan Wang +5
Neural Architecture Search (NAS) has emerged as one of the effective methods to design the optimal neural network architecture automatically. Although neural architectures have ach…