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Found 38 papers, 18 papers with code
FACTUAL: A Novel Framework for Contrastive Learning Based Robust SAR Image Classification
FACTUAL consists of two components: (1) Differing from existing works, a novel perturbation scheme that incorporates realistic physical adversarial attacks (such as OTSA) to build a supervised adversarial pre-training network.
VersaT2I: Improving Text-to-Image Models with Versatile Reward
Recent text-to-image (T2I) models have benefited from large-scale and high-quality data, demonstrating impressive performance.
Uncertainty-Aware SAR ATR: Defending Against Adversarial Attacks via Bayesian Neural Networks
Adversarial attacks have demonstrated the vulnerability of Machine Learning (ML) image classifiers in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems.
Hierarchical Auto-Organizing System for Open-Ended Multi-Agent Navigation
To assess organizational behavior, we design a series of navigation tasks in the Minecraft environment, which includes searching and exploring.
A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification
Our experimental results on benchmark grayscale image datasets demonstrate the effectiveness of the proposed model, achieving vastly lower latency (up to 16$\times$ less) and competitive or leading performance compared to other state-of-the-art image classification models on various domain-specific grayscale image classification datasets.
Ranked #16 on
Image Classification
on Fashion-MNIST
SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
Our SegMamba, in contrast to Transformer-based methods, excels in whole volume feature modeling from a state space model standpoint, maintaining superior processing speed, even with volume features at a resolution of {$64\times 64\times 64$}.
VQCNIR: Clearer Night Image Restoration with Vector-Quantized Codebook
In this paper, we propose Clearer Night Image Restoration with Vector-Quantized Codebook (VQCNIR) to achieve remarkable and consistent restoration outcomes on real-world and synthetic benchmarks.
Benchmarking Deep Learning Classifiers for SAR Automatic Target Recognition
Synthetic Aperture Radar SAR Automatic Target Recognition ATR is a key technique of remote-sensing image recognition which can be supported by deep neural networks The existing works of SAR ATR mostly focus on improving the accuracy of the target recognition while ignoring the systems performance in terms of speed and storage which is critical to real-world applications of SAR ATR For decision-makers aiming to identify a proper deep learning model to deploy in a SAR ATR system it is important to understand the performance of different candidate deep learning models and determine the best model accordingly This paper comprehensively benchmarks several advanced deep learning models for SAR ATR with multiple distinct SAR imagery datasets Specifically we train and test five SAR image classifiers based on Residual Neural Networks ResNet18 ResNet34 ResNet50 Graph Neural Network GNN and Vision Transformer for Small-Sized Datasets (SS-ViT) We select three datasets MSTAR GBSAR and SynthWakeSAR that offer heterogeneity We evaluate and compare the five classifiers concerning their classification accuracy runtime performance in terms of inference throughput and analytical performance in terms of number of parameters number of layers model size and number of operations Experimental results show that the GNN classifier outperforms with respect to throughput and latency However it is also shown that no clear model winner emerges from all of our chosen metrics and a one model rules all case is doubtful in the domain of SAR ATR
Realistic Scatterer Based Adversarial Attacks on SAR Image Classifiers
Instead, adversarial attacks should be able to be implemented by physical actions, for example, placing additional false objects as scatterers around the on-ground target to perturb the SAR image and fool the SAR ATR.
See and Think: Embodied Agent in Virtual Environment
Vision perception involves the interpretation of visual information in the environment, which is then integrated into the LLMs component with agent state and task instruction.
Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model
These priors are subsequently utilized by RGformer to guide the decomposition of image features into their respective reflectance and illumination components.
Integrating View Conditions for Image Synthesis
In the field of image processing, applying intricate semantic modifications within existing images remains an enduring challenge.
Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streaks
In the real world, image degradations caused by rain often exhibit a combination of rain streaks and raindrops, thereby increasing the challenges of recovering the underlying clean image.
MovieChat: From Dense Token to Sparse Memory for Long Video Understanding
Recently, integrating video foundation models and large language models to build a video understanding system can overcome the limitations of specific pre-defined vision tasks.
Video-based Generative Performance Benchmarking (Consistency)
Video-based Generative Performance Benchmarking (Contextual Understanding)
+10
MPM: A Unified 2D-3D Human Pose Representation via Masked Pose Modeling
In this paper, we propose MPM, a unified 2D-3D human pose representation framework via masked pose modeling.
NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer
Extensive experiments on several synthetic and real-world datasets demonstrate the superiority of our NightHazeFormer over state-of-the-art nighttime haze removal methods in terms of both visually and quantitatively.
Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement
In this work, we propose the Five A$^{+}$ Network (FA$^{+}$Net), a highly efficient and lightweight real-time underwater image enhancement network with only $\sim$ 9k parameters and $\sim$ 0. 01s processing time.
DEHRFormer: Real-time Transformer for Depth Estimation and Haze Removal from Varicolored Haze Scenes
Varicolored haze caused by chromatic casts poses haze removal and depth estimation challenges.
RSFDM-Net: Real-time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement
Underwater images typically experience mixed degradations of brightness and structure caused by the absorption and scattering of light by suspended particles.
Adverse Weather Removal with Codebook Priors
Inspired by recent advancements in codebook and vector quantization (VQ) techniques, we present a novel Adverse Weather Removal network with Codebook Priors (AWRCP) to address the problem of unified adverse weather removal.
Dual-former: Hybrid Self-attention Transformer for Efficient Image Restoration
Recently, image restoration transformers have achieved comparable performance with previous state-of-the-art CNNs.
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
The homepage of this challenge is at https://github. com/RenYang-home/AIM22_CompressSR.
SnowFormer: Context Interaction Transformer with Scale-awareness for Single Image Desnowing
Due to various and complicated snow degradations, single image desnowing is a challenging image restoration task.
Towards Real-time High-Definition Image Snow Removal: Efficient Pyramid Network with Asymmetrical Encoder-decoder Architecture
In winter scenes, the degradation of images taken under snow can be pretty complex, where the spatial distribution of snowy degradation is varied from image to image.
MSP-Former: Multi-Scale Projection Transformer for Single Image Desnowing
Snow removal causes challenges due to its characteristic of complex degradations.
Prior-Guided One-shot Neural Architecture Search
In this paper, we present Prior-Guided One-shot NAS (PGONAS) to strengthen the ranking correlation of supernets.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Towards Efficient Single Image Dehazing and Desnowing
A single expert network efficiently addresses specific degradation in nasty winter scenes relying on the compact architecture and three novel components.
Self-Calibrated Efficient Transformer for Lightweight Super-Resolution
Recently, deep learning has been successfully applied to the single-image super-resolution (SISR) with remarkable performance.
Underwater Light Field Retention : Neural Rendering for Underwater Imaging
To this end, we propose a neural rendering method for underwater imaging, dubbed UWNR (Underwater Neural Rendering).
Mutual Learning for Domain Adaptation: Self-distillation Image Dehazing Network with Sample-cycle
Specifically, we first devise two siamese networks: a teacher network in the synthetic domain and a student network in the real domain, and then optimize them in a mutual learning manner by leveraging EMA and joint loss.
Perceiving and Modeling Density is All You Need for Image Dehazing
However, due to the paradox caused by the variation of real captured haze and the fixed degradation parameters of the current networks, the generalization ability of recent dehazing methods on real-world hazy images is not ideal. To address the problem of modeling real-world haze degradation, we propose to solve this problem by perceiving and modeling density for uneven haze distribution.
Ranked #5 on
Image Dehazing
on Haze4k
Efficient Re-parameterization Residual Attention Network For Nonhomogeneous Image Dehazing
This paper proposes an end-to-end Efficient Re-parameterizationResidual Attention Network(ERRA-Net) to directly restore the nonhomogeneous hazy image.
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization
We study the asymmetric low-rank factorization problem: \[\min_{\mathbf{U} \in \mathbb{R}^{m \times d}, \mathbf{V} \in \mathbb{R}^{n \times d}} \frac{1}{2}\|\mathbf{U}\mathbf{V}^\top -\mathbf{\Sigma}\|_F^2\] where $\mathbf{\Sigma}$ is a given matrix of size $m \times n$ and rank $d$.
Aligning Videos in Space and Time
During training, given a pair of videos, we compute cycles that connect patches in a given frame in the first video by matching through frames in the second video.
DEED: A General Quantization Scheme for Communication Efficiency in Bits
In the infrequent communication setting, DEED combined with Federated averaging requires a smaller total number of bits than Federated Averaging.
Graph Star Net for Generalized Multi-Task Learning
In this work, we present graph star net (GraphStar), a novel and unified graph neural net architecture which utilizes message-passing relay and attention mechanism for multiple prediction tasks - node classification, graph classification and link prediction.
Ranked #1 on
Link Prediction
on Pubmed (biased evaluation)
Interpretable Intuitive Physics Model
In order to demonstrate that our system models these underlying physical properties, we train our model on collisions of different shapes (cube, cone, cylinder, spheres etc.)
