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Found 12 papers, 2 papers with code
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Synthetic data is gaining increasing relevance for training machine learning models.
Intuition-aware Mixture-of-Rank-1-Experts for Parameter Efficient Finetuning
Large Language Models (LLMs) have demonstrated significant potential in performing multiple tasks in multimedia applications, ranging from content generation to interactive entertainment, and artistic creation.
VeCAF: Vision-language Collaborative Active Finetuning with Training Objective Awareness
Finetuning a pretrained vision model (PVM) is a common technique for learning downstream vision tasks.
Efficient Deweather Mixture-of-Experts with Uncertainty-aware Feature-wise Linear Modulation
In this work, we propose an efficient MoE architecture with weight sharing across the experts.
M$^{2}$Chat: Empowering VLM for Multimodal LLM Interleaved Text-Image Generation
Moreover, to further enhance the effectiveness of $M^{3}Adapter$ while preserving the coherence of semantic context comprehension, we introduce a two-stage $M^{3}FT$ fine-tuning strategy.
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions.
Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation
Multimodal learning has seen great success mining data features from multiple modalities with remarkable model performance improvement.
BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks
In order to find them, we further propose a LiDAR-guided sampling strategy to leverage the statistical distribution of LiDAR to determine the heights of local slices.
Cloud-Device Collaborative Adaptation to Continual Changing Environments in the Real-world
To enable the device model to deal with changing environments, we propose a new learning paradigm of Cloud-Device Collaborative Continual Adaptation, which encourages collaboration between cloud and device and improves the generalization of the device model.
BEVUDA: Multi-geometric Space Alignments for Domain Adaptive BEV 3D Object Detection
In this paper, we propose a Multi-space Alignment Teacher-Student (MATS) framework to ease the domain shift accumulation, which consists of a Depth-Aware Teacher (DAT) and a Geometric-space Aligned Student (GAS) model.
Image Quality Assessment with Gradient Siamese Network
In this work, we introduce Gradient Siamese Network (GSN) for image quality assessment.
Multi-Frames Temporal Abnormal Clues Learning Method for Face Anti-Spoofing
Face anti-spoofing researches are widely used in face recognition and has received more attention from industry and academics.
