Search Results for author:
Found 24 papers, 7 papers with code
SpikeGen: Generative Framework for Visual Spike Stream Processing
These models not only facilitate the conditional fusion of existing information from both spike and RGB modalities but also enable the conditional generation based on latent priors.
NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment
A total of 211 participants have registered in the structure track.
MoLe-VLA: Dynamic Layer-skipping Vision Language Action Model via Mixture-of-Layers for Efficient Robot Manipulation
Additionally, to compensate for the cognitive ability of LLMs lost in MoLe, we devise a Cognition Self-Knowledge Distillation (CogKD) framework.
FBQuant: FeedBack Quantization for Large Language Models
Deploying Large Language Models (LLMs) on edge devices is increasingly important, as it eliminates reliance on network connections, reduces expensive API calls, and enhances user privacy.
Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data
In order to promote the proposal of novel Generative AI methods and synthetic data, and investigate the application of synthetic data to better train face recognition systems, we introduce the 2nd FRCSyn-onGoing challenge, based on the 2nd Face Recognition Challenge in the Era of Synthetic Data (FRCSyn), originally launched at CVPR 2024.
EVA: An Embodied World Model for Future Video Anticipation
It leverages the complementary strengths of pre-trained vision-language and video generation models, enabling them to function as a world model in embodied scenarios.
PAT: Pruning-Aware Tuning for Large Language Models
Since the model fine-tuning refines the general and chaotic knowledge in pre-trained models, we aim to incorporate structural pruning with the fine-tuning, and propose the Pruning-Aware Tuning (PAT) paradigm to eliminate model redundancy while preserving the model performance to the maximum extend.
FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models
Recent research has demonstrated that Feed-Forward Networks (FFNs) in Large Language Models (LLMs) play a pivotal role in storing diverse linguistic and factual knowledge.
MMTrail: A Multimodal Trailer Video Dataset with Language and Music Descriptions
To fulfill this gap, we present MMTrail, a large-scale multi-modality video-language dataset incorporating more than 20M trailer clips with visual captions, and 2M high-quality clips with multimodal captions.
Implicit Neural Image Field for Biological Microscopy Image Compression
The rapid pace of innovation in biological microscopy imaging has led to large images, putting pressure on data storage and impeding efficient sharing, management, and visualization.
Decomposing the Neurons: Activation Sparsity via Mixture of Experts for Continual Test Time Adaptation
Continual Test-Time Adaptation (CTTA), which aims to adapt the pre-trained model to ever-evolving target domains, emerges as an important task for vision models.
Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data
Federated learning (FL) offers a privacy-centric distributed learning framework, enabling model training on individual clients and central aggregation without necessitating data exchange.
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.
Full reference image quality assessment
Full-Reference 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.
