1 citations · 1 across the 4 of their papers we have counts for
6 papers
From Transformation to Target State: Rethinking Query Representation for Zero-Shot Composed Image Retrieval
Yihe Zhao, Songhe Feng
Composed image retrieval (CIR) aims to retrieve a desired target image from a query consisting of a reference image and a modification text. This task exhibits an unusual represent…
WARM-CAT: Warm-Started Test-Time Comprehensive Knowledge Accumulation for Compositional Zero-Shot Learning
Xudong Yan, Songhe Feng, Jiaxin Wang +2
Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute-object compositions based on the knowledge learned from seen ones. Existing methods suffer from performanc…
Bridging Modalities via Progressive Re-alignment for Multimodal Test-Time Adaptation
Jiacheng Li, Songhe Feng
Test-time adaptation (TTA) enables online model adaptation using only unlabeled test data, aiming to bridge the gap between source and target distributions. However, in multimodal…
TOMCAT: Test-time Comprehensive Knowledge Accumulation for Compositional Zero-Shot Learning
Xudong Yan, Songhe Feng
Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute-object compositions based on the knowledge learned from seen ones. Existing methods suffer from performanc…
Leveraging MLLM Embeddings and Attribute Smoothing for Compositional Zero-Shot Learning
Xudong Yan, Songhe Feng, Yang Zhang +3
Compositional zero-shot learning (CZSL) aims to recognize novel compositions of attributes and objects learned from seen compositions. Previous works disentangle attributes and obj…
Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization
Yanan Wu, Zhixiang Chi, Yang Wang +2
Test-time domain adaptation aims to adapt the model trained on source domains to unseen target domains using a few unlabeled images. Emerging research has shown that the label and…