5 citations · 7 across the 8 of their papers we have counts for
6 papers · 1 filter
DA-Mamba: Learning Domain-Aware State Space Model for Global-Local Alignment in Domain Adaptive Object Detection
Haochen Li, Rui Zhang, Hantao Yao +5
Domain Adaptive Object Detection (DAOD) aims to transfer detectors from a labeled source domain to an unlabeled target domain. Existing DAOD methods employ multi-granularity featur…
DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection
Haochen Li, Rui Zhang, Hantao Yao +7
Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. As the visual-language models (VLMs) can…
SPDiffusion: Semantic Protection Diffusion Models for Multi-concept Text-to-image Generation
Yang Zhang, Rui Zhang, Xuecheng Nie +6
Recent text-to-image models have achieved impressive results in generating high-quality images. However, when tasked with multi-concept generation creating images that contain mult…
Prompt-based Visual Alignment for Zero-shot Policy Transfer
Haihan Gao, Rui Zhang, Qi Yi +13
Overfitting in RL has become one of the main obstacles to applications in reinforcement learning(RL). Existing methods do not provide explicit semantic constrain for the feature ex…
Learning Domain-Aware Detection Head with Prompt Tuning
Haochen Li, Rui Zhang, Hantao Yao +5
Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. However, existing methods focus on reduci…
Hybrid CNN-Transformer Model For Facial Affect Recognition In the ABAW4 Challenge
Lingfeng Wang, Haocheng Li, Chunyin Liu
This paper describes our submission to the fourth Affective Behavior Analysis (ABAW) competition. We proposed a hybrid CNN-Transformer model for the Multi-Task-Learning (MTL) and L…