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Found 28 papers, 17 papers with code
REO-VLM: Transforming VLM to Meet Regression Challenges in Earth Observation
The rapid evolution of Vision Language Models (VLMs) has catalyzed significant advancements in artificial intelligence, expanding research across various disciplines, including Earth Observation (EO).
Efficient Adaptation of Pre-trained Vision Transformer via Householder Transformation
A common strategy for Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViTs) involves adapting the model to downstream tasks by learning a low-rank adaptation matrix.
Advancements in Visual Language Models for Remote Sensing: Datasets, Capabilities, and Enhancement Techniques
Recently, the remarkable success of ChatGPT has sparked a renewed wave of interest in artificial intelligence (AI), and the advancements in visual language models (VLMs) have pushed this enthusiasm to new heights.
TG-LLaVA: Text Guided LLaVA via Learnable Latent Embeddings
In contrast, we propose Text Guided LLaVA (TG-LLaVA) in this paper, which optimizes VLMs by guiding the vision encoder with text, offering a new and orthogonal optimization direction.
3D-RCNet: Learning from Transformer to Build a 3D Relational ConvNet for Hyperspectral Image Classification
To overcome these challenges, we propose a 3D relational ConvNet named 3D-RCNet, which inherits both strengths of ConvNet and ViT, resulting in high performance in HSI classification.
OVA-DETR: Open Vocabulary Aerial Object Detection Using Image-Text Alignment and Fusion
Then, we propose Bidirectional Vision-Language Fusion (Bi-VLF), which includes a dual-attention fusion encoder and a multi-level text-guided Fusion Decoder.
Bridging Sensor Gaps via Attention Gated Tuning for Hyperspectral Image Classification
In this paper, we propose a novel Attention-Gated Tuning (AGT) strategy and a triplet-structured transformer model, Tri-Former, to address this issue.
Computational Efficiency
Hyperspectral Image Classification
+2
NAR-Former V2: Rethinking Transformer for Universal Neural Network Representation Learning
It can learn efficient representations from both cell-structured networks and entire networks.
Teacher Agent: A Knowledge Distillation-Free Framework for Rehearsal-based Video Incremental Learning
Rehearsal-based video incremental learning often employs knowledge distillation to mitigate catastrophic forgetting of previously learned data.
NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction
In this paper, we propose a neural architecture representation model that can be used to estimate these attributes holistically.
Fcaformer: Forward Cross Attention in Hybrid Vision Transformer
Currently, one main research line in designing a more efficient vision transformer is reducing the computational cost of self attention modules by adopting sparse attention or using local attention windows.
ParCNetV2: Oversized Kernel with Enhanced Attention
Specifically, we propose a new convolutional neural network, ParCNetV2, that extends position-aware circular convolution (ParCNet) with oversized convolutions and bifurcate gate units to enhance attention.
Fast-ParC: Capturing Position Aware Global Feature for ConvNets and ViTs
Transformer models have made tremendous progress in various fields in recent years.
ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer
Experiment results show that the proposed ParC-Net achieves better performance than popular light-weight ConvNets and vision transformer based models in common vision tasks and datasets, while having fewer parameters and faster inference speed.
Ranked #817 on
Image Classification
on ImageNet
Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image Classification
Compared with search spaces proposed in previous works, the proposed hybrid search space is more aligned with the characteristic of HSI data, that is, HSIs have a relatively low spatial resolution and an extremely high spectral resolution.
Connecting Compression Spaces with Transformer for Approximate Nearest Neighbor Search
Specifically, based on transformer, we propose a new network structure to compress the feature into a low dimensional space, and an inhomogeneous neighborhood relationship preserving (INRP) loss that aims to maintain high search accuracy.
Pseudo-LiDAR Based Road Detection
Specifically, we exploit pseudo-LiDAR using depth estimation, and propose a feature fusion network where RGB and learned depth information are fused for improved road detection.
3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image Classification
Correspondingly, different models need to be designed for different datasets, which further increases the workload of designing architectures; 2) the mainstream framework is a patch-to-pixel framework.
Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration
For the inner search space, we propose a layer-wise architecture sharing strategy (LWAS), resulting in more flexible architectures and better performance.
Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning
In contrast to previous approaches, we do not impose restrictions over the source data sets, in which they do not have to be collected by the same sensors as the target data sets.
Hyperspectral Image Classification with Spatial Consistence Using Fully Convolutional Spatial Propagation Network
However, the existing CNN-based models operate at the patch-level, in which pixel is separately classified into classes using a patch of images around it.
Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning
Hyperspectral image(HSI) classification has been improved with convolutional neural network(CNN) in very recent years.
Gradient Information Guided Deraining with A Novel Network and Adversarial Training
However, the existing methods usually do not have good generalization ability, which leads to the fact that almost all of existing methods have a satisfied performance on removing a specific type of rain streaks, but may have a relatively poor performance on other types of rain streaks.
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation
This paper tackles the problem of video object segmentation.
Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising
We also present analysis on the architectures found by NAS.
Exploiting temporal consistency for real-time video depth estimation
The temporal consistency loss is combined with the spatial loss to update the model in an end-to-end fashion.
Ranked #5 on
Monocular Depth Estimation
on Mid-Air Dataset
RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks
Different from RGB videos, depth data in RGB-D videos provide key complementary information for tristimulus visual data which potentially could achieve accuracy improvement for action recognition.
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network
Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification.
