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Found 57 papers, 27 papers with code
Learning Progressive Joint Propagation for Human Motion Prediction
Last, in order to incorporate a general motion space for high-quality prediction, we build a memory-based dictionary, which aims to preserve the global motion patterns in training data to guide the predictions.
A Text-Based Knowledge-Embedded Soft Sensing Modeling Approach for General Industrial Process Tasks Based on Large Language Model
To address these challenges, we propose a general framework named LLM-TKESS (large language model for text-based knowledge-embedded soft sensing), harnessing the powerful general problem-solving capabilities, cross-modal knowledge transfer abilities, and few-shot capabilities of LLM for enhanced soft sensing modeling.
A Soft Sensor Method with Uncertainty-Awareness and Self-Explanation Based on Large Language Models Enhanced by Domain Knowledge Retrieval
To achieve this, we propose a novel framework called the Few-shot Uncertainty-aware and self-Explaining Soft Sensor (LLM-FUESS), which includes the Zero-shot Auxiliary Variable Selector (LLM-ZAVS) and the Uncertainty-aware Few-shot Soft Sensor (LLM-UFSS).
White-Box Diffusion Transformer for single-cell RNA-seq generation
Our White-Box Diffusion Transformer combines the generative capabilities of Diffusion model with the mathematical interpretability of White-Box transformer.
Adaptive Caching for Faster Video Generation with Diffusion Transformers
Generating temporally-consistent high-fidelity videos can be computationally expensive, especially over longer temporal spans.
MarDini: Masked Autoregressive Diffusion for Video Generation at Scale
We introduce MarDini, a new family of video diffusion models that integrate the advantages of masked auto-regression (MAR) into a unified diffusion model (DM) framework.
scRDiT: Generating single-cell RNA-seq data by diffusion transformers and accelerating sampling
The method is a neural network constructed based on Denoising Diffusion Probabilistic Models (DDPMs) and Diffusion Transformers (DiTs).
Graph Analysis Using a GPU-based Parallel Algorithm: Quantum Clustering
The article introduces a new method for applying Quantum Clustering to graph structures.
Hierarchical Integration Diffusion Model for Realistic Image Deblurring
Specifically, we perform the DM in a highly compacted latent space to generate the prior feature for the deblurring process.
ShadowFormer: Global Context Helps Image Shadow Removal
It is still challenging for the deep shadow removal model to exploit the global contextual correlation between shadow and non-shadow regions.
Ranked #1 on
Shadow Removal
on ISTD
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.
Boosting Video Super Resolution with Patch-Based Temporal Redundancy Optimization
We develop two simple yet effective plug and play methods to improve the performance of existing local and non-local propagation-based VSR algorithms on widely-used public videos.
Dynamic Proposals for Efficient Object Detection
Object detection is a basic computer vision task to loccalize and categorize objects in a given image.
Residual Local Feature Network for Efficient Super-Resolution
Moreover, we revisit the popular contrastive loss and observe that the selection of intermediate features of its feature extractor has great influence on the performance.
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.
Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution
Besides, FMEN-S achieves the lowest memory consumption and the second shortest runtime in NTIRE 2022 challenge on efficient super-resolution.
Boosting Lightweight Single Image Super-resolution via Joint-distillation
The joint-distillation learning consists of internal self-distillation and external mutual learning.
Retrieval-Augmented Transformer-XL for Close-Domain Dialog Generation
Transformer-based models have demonstrated excellent capabilities of capturing patterns and structures in natural language generation and achieved state-of-the-art results in many tasks.
Understanding Neural Networks with Logarithm Determinant Entropy Estimator
Understanding the informative behaviour of deep neural networks is challenged by misused estimators and the complexity of network structure, which leads to inconsistent observations and diversified interpretation.
Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis
In this paper, we explore open-domain sketch-to-photo translation, which aims to synthesize a realistic photo from a freehand sketch with its class label, even if the sketches of that class are missing in the training data.
Ranked #1 on
Sketch-to-Image Translation
on Scribble
Progressive Temporal Feature Alignment Network for Video Inpainting
To achieve this goal, it is necessary to find correspondences from neighbouring frames to faithfully hallucinate the unknown content.
A Unified 3D Human Motion Synthesis Model via Conditional Variational Auto-Encoder
Notably, by considering this problem as a conditional generation process, we estimate a parametric distribution of the missing regions based on the input conditions, from which to sample and synthesize the full motion series.
CompFeat: Comprehensive Feature Aggregation for Video Instance Segmentation
Video instance segmentation is a complex task in which we need to detect, segment, and track each object for any given video.
Ranked #32 on
Video Instance Segmentation
on YouTube-VIS validation
Neural Sparse Representation for Image Restoration
Inspired by the robustness and efficiency of sparse representation in sparse coding based image restoration models, we investigate the sparsity of neurons in deep networks.
Outlier Detection Using a Novel method: Quantum Clustering
This approach, called Quantum Clustering (QC), deals with unlabeled data processing and constructs a potential function to find the centroids of clusters and the outliers.
Quantum-Classical Machine learning by Hybrid Tensor Networks
In this work, we propose the quantum-classical hybrid tensor networks (HTN) which combine tensor networks with classical neural networks in a uniform deep learning framework to overcome the limitations of regular tensor networks in machine learning.
Unsupervised Low-light Image Enhancement with Decoupled Networks
In this paper, we tackle the problem of enhancing real-world low-light images with significant noise in an unsupervised fashion.
Pyramid Attention Networks for Image Restoration
Self-similarity refers to the image prior widely used in image restoration algorithms that small but similar patterns tend to occur at different locations and scales.
Scale-wise Convolution for Image Restoration
In this paper, we show that properly modeling scale-invariance into neural networks can bring significant benefits to image restoration performance.
DAVID: Dual-Attentional Video Deblurring
To train and evaluate on more diverse blur severity levels, we propose a Challenging DVD dataset generated from the raw DVD video set by pooling frames with different temporal windows.
Information Bottleneck Theory on Convolutional Neural Networks
Recent years, many researches attempt to open the black box of deep neural networks and propose a various of theories to understand it.
EnlightenGAN: Deep Light Enhancement without Paired Supervision
Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data?
Generative Tensor Network Classification Model for Supervised Machine Learning
By investigating the distances in the many-body Hilbert space, we find that (a) the samples are naturally clustering in such a space; and (b) bounding the bond dimensions of the TN's to finite values corresponds to removing redundant information in the image recognition.
Connecting Image Denoising and High-Level Vision Tasks via Deep Learning
Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network via back-propagation.
U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting
This paper studies the challenging problem of fingerprint image denoising and inpainting.
Non-Local Recurrent Network for Image Restoration
The main contributions of this work are: (1) Unlike existing methods that measure self-similarity in an isolated manner, the proposed non-local module can be flexibly integrated into existing deep networks for end-to-end training to capture deep feature correlation between each location and its neighborhood.
Ranked #1 on
Grayscale Image Denoising
on Set12 sigma30
Image Super-Resolution via Dual-State Recurrent Networks
Advances in image super-resolution (SR) have recently benefited significantly from rapid developments in deep neural networks.
Ranked #48 on
Image Super-Resolution
on BSD100 - 4x upscaling
Survey of Face Detection on Low-quality Images
However, previous proposed models are mostly trained and tested on good-quality images which are not always the case for practical applications like surveillance systems.
Learning Simple Thresholded Features with Sparse Support Recovery
The thresholded feature has recently emerged as an extremely efficient, yet rough empirical approximation, of the time-consuming sparse coding inference process.
Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures
The resemblance between the methods used in studying quantum-many body physics and in machine learning has drawn considerable attention.
Enhance Visual Recognition under Adverse Conditions via Deep Networks
Visual recognition under adverse conditions is a very important and challenging problem of high practical value, due to the ubiquitous existence of quality distortions during image acquisition, transmission, or storage.
Improving Object Detection from Scratch via Gated Feature Reuse
In this paper, we present a simple and parameter-efficient drop-in module for one-stage object detectors like SSD when learning from scratch (i. e., without pre-trained models).
Machine Learning by Unitary Tensor Network of Hierarchical Tree Structure
We study the quantum features of the TN states, including quantum entanglement and fidelity.
Robust Video Super-Resolution With Learned Temporal Dynamics
Second, we reduce the complexity of motion between neighboring frames using a spatial alignment network that is much more robust and efficient than competing alignment methods and can be jointly trained with the temporal adaptive network in an end-to-end manner.
Robust Emotion Recognition from Low Quality and Low Bit Rate Video: A Deep Learning Approach
Emotion recognition from facial expressions is tremendously useful, especially when coupled with smart devices and wireless multimedia applications.
Learning audio sequence representations for acoustic event classification
Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC.
When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach
Conventionally, image denoising and high-level vision tasks are handled separately in computer vision.
Understanding Convolution for Semantic Segmentation
This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution operation.
Ranked #20 on
Semantic Segmentation
on PASCAL VOC 2012 test
Learning a Mixture of Deep Networks for Single Image Super-Resolution
This paper proposes the method of learning a mixture of SR inference modules in a unified framework to tackle this problem.
Robust Single Image Super-Resolution via Deep Networks With Sparse Prior
We demonstrate that a sparse coding model particularly designed for SR can be incarnated as a neural network with the merit of end-to-end optimization over training data.
D3: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images
In this paper, we design a Deep Dual-Domain (D3) based fast restoration model to remove artifacts of JPEG compressed images.
$\mathbf{D^3}$: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images
In this paper, we design a Deep Dual-Domain ($\mathbf{D^3}$) based fast restoration model to remove artifacts of JPEG compressed images.
Brain-Inspired Deep Networks for Image Aesthetics Assessment
Image aesthetics assessment has been challenging due to its subjective nature.
Studying Very Low Resolution Recognition Using Deep Networks
Visual recognition research often assumes a sufficient resolution of the region of interest (ROI).
Deep Networks for Image Super-Resolution with Sparse Prior
We show that a sparse coding model particularly designed for super-resolution can be incarnated as a neural network, and trained in a cascaded structure from end to end.
Frequency-Based 3D Reconstruction of Transparent and Specular Objects
3D reconstruction of transparent and specular objects is a very challenging topic in computer vision.
