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Found 24 papers, 10 papers with code
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.
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.
$β$-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.
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.
MotionChain: Conversational Motion Controllers via Multimodal Prompts
However, this proficiency remains largely unexplored in other multimodal generative models, particularly in human motion models.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
When Hyperspectral Image Classification Meets Diffusion Models: An Unsupervised Feature Learning Framework
Learning effective spectral-spatial features is important for the hyperspectral image (HSI) classification task, but the majority of existing HSI classification methods still suffer from modeling complex spectral-spatial relations and characterizing low-level details and high-level semantics comprehensively.
VQ-NeRF: Vector Quantization Enhances Implicit Neural Representations
Recent advancements in implicit neural representations have contributed to high-fidelity surface reconstruction and photorealistic novel view synthesis.
Rethinking of Feature Interaction for Multi-task Learning on Dense Prediction
In this work, we propose to learn a comprehensive intermediate feature globally from both task-generic and task-specific features, we reveal an important fact that this intermediate feature, namely the bridge feature, is a good solution to the above issues.
MovieLLM: Enhancing Long Video Understanding with AI-Generated Movies
The development of multimodal models has marked a significant step forward in how machines understand videos.
