Search Results for author:
Found 29 papers, 12 papers with code
Scene123: One Prompt to 3D Scene Generation via Video-Assisted and Consistency-Enhanced MAE
As Artificial Intelligence Generated Content (AIGC) advances, a variety of methods have been developed to generate text, images, videos, and 3D objects from single or multimodal inputs, contributing efforts to emulate human-like cognitive content creation.
Lightweight Model Pre-training via Language Guided Knowledge Distillation
In this paper, for the first time, we introduce language guidance to the distillation process and propose a new method named Language-Guided Distillation (LGD) system, which uses category names of the target downstream task to help refine the knowledge transferred between the teacher and student.
DualMamba: A Lightweight Spectral-Spatial Mamba-Convolution Network for Hyperspectral Image Classification
Finally, the adaptive global-local fusion is proposed to dynamically combine global Mamba features and local convolution features for a global-local spectral-spatial representation.
Adapter-X: A Novel General Parameter-Efficient Fine-Tuning Framework for Vision
Parameter-efficient fine-tuning (PEFT) has become increasingly important as foundation models continue to grow in both popularity and size.
MotionChain: Conversational Motion Controllers via Multimodal Prompts
However, this proficiency remains largely unexplored in other multimodal generative models, particularly in human motion models.
MovieLLM: Enhancing Long Video Understanding with AI-Generated Movies
Development of multimodal models has marked a significant step forward in how machines understand videos.
LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding Reasoning and Planning
Recent progress in Large Multimodal Models (LMM) has opened up great possibilities for various applications in the field of human-machine interactions.
BridgeNet: Comprehensive and Effective Feature Interactions via Bridge Feature for Multi-task Dense Predictions
Multi-task dense prediction aims at handling multiple pixel-wise prediction tasks within a unified network simultaneously for visual scene understanding.
LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning
However, developing LMMs that can comprehend, reason, and plan in complex and diverse 3D environments remains a challenging topic, especially considering the demand for understanding permutation-invariant point cloud 3D representations of the 3D scene.
ShapeGPT: 3D Shape Generation with A Unified Multi-modal Language Model
The advent of large language models, enabling flexibility through instruction-driven approaches, has revolutionized many traditional generative tasks, but large models for 3D data, particularly in comprehensively handling 3D shapes with other modalities, are still under-explored.
Exploring Multi-Timestep Multi-Stage Diffusion Features for Hyperspectral Image Classification
To address this issue, we propose a novel diffusion-based feature learning framework that explores Multi-Timestep Multi-Stage Diffusion features for HSI classification for the first time, called MTMSD.
Boost Vision Transformer with GPU-Friendly Sparsity and Quantization
Moreover, GPUSQ-ViT can boost actual deployment performance by 1. 39-1. 79 times and 3. 22-3. 43 times of latency and throughput on A100 GPU, and 1. 57-1. 69 times and 2. 11-2. 51 times improvement of latency and throughput on AGX Orin.
What Makes for Effective Few-shot Point Cloud Classification?
Due to the emergence of powerful computing resources and large-scale annotated datasets, deep learning has seen wide applications in our daily life.
Performance-aware Approximation of Global Channel Pruning for Multitask CNNs
Global channel pruning (GCP) aims to remove a subset of channels (filters) across different layers from a deep model without hurting the performance.
A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search For Hyperspectral Image Classification
To address the above issues, we propose a multi-stage search architecture in order to overcome asymmetric spectral-spatial dimensions and capture significant features.
Hyperspectral Image Classification
Neural Architecture Search
JNDMix: JND-Based Data Augmentation for No-reference Image Quality Assessment
Despite substantial progress in no-reference image quality assessment (NR-IQA), previous training models often suffer from over-fitting due to the limited scale of used datasets, resulting in model performance bottlenecks.
A Large-Scale Outdoor Multi-modal Dataset and Benchmark for Novel View Synthesis and Implicit Scene Reconstruction
Meanwhile, a new benchmark for several outdoor NeRF-based tasks is established, such as novel view synthesis, surface reconstruction, and multi-modal NeRF.
Instance-aware Model Ensemble With Distillation For Unsupervised Domain Adaptation
To enhance the model, adaptability between domains and reduce the computational cost when deploying the ensemble model, we propose a novel framework, namely Instance aware Model Ensemble With Distillation, IMED, which fuses multiple UDA component models adaptively according to different instances and distills these components into a small model.
Efficient Joint-Dimensional Search with Solution Space Regularization for Real-Time Semantic Segmentation
In this paper, we intend to search an optimal network structure that can run in real-time for this problem.
Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image Classification
Few-shot fine-grained learning aims to classify a query image into one of a set of support categories with fine-grained differences.
$β$-DARTS: Beta-Decay Regularization for Differentiable Architecture Search
Neural Architecture Search~(NAS) has attracted increasingly more attention in recent years because of its capability to design deep neural networks automatically.
b-DARTS: Beta-Decay Regularization for Differentiable Architecture Search
Neural Architecture Search (NAS) has attracted increasingly more attention in recent years because of its capability to design deep neural network automatically.
Point Cloud Instance Segmentation with Semi-supervised Bounding-Box Mining
For stage two, the bounding box proposals with SPCR are grouped into some subsets, and the instance masks are mined inside each subset with a novel semantic propagation module and a property consistency graph module.
Densely Semantic Enhancement for Domain Adaptive Region-free Detectors
Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data.
Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification
The goal of few-shot fine-grained image classification is to recognize rarely seen fine-grained objects in the query set, given only a few samples of this class in the support set.
EADNet: Efficient Asymmetric Dilated Network for Semantic Segmentation
Due to real-time image semantic segmentation needs on power constrained edge devices, there has been an increasing desire to design lightweight semantic segmentation neural network, to simultaneously reduce computational cost and increase inference speed.
PIDNet: An Efficient Network for Dynamic Pedestrian Intrusion Detection
Vision-based dynamic pedestrian intrusion detection (PID), judging whether pedestrians intrude an area-of-interest (AoI) by a moving camera, is an important task in mobile surveillance.
Coarse-to-Fine Gaze Redirection with Numerical and Pictorial Guidance
In this paper, we propose to alleviate these problems by means of a novel gaze redirection framework which exploits both a numerical and a pictorial direction guidance, jointly with a coarse-to-fine learning strategy.
