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Found 37 papers, 20 papers with code
Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEs
In this paper, we present DoSSR, a Domain Shift diffusion-based SR model that capitalizes on the generative powers of pretrained diffusion models while significantly enhancing efficiency by initiating the diffusion process with low-resolution (LR) images.
Pose Magic: Efficient and Temporally Consistent Human Pose Estimation with a Hybrid Mamba-GCN Network
This architecture introduces local enhancement with GCN by capturing relationships between neighboring joints, thus producing new representations to complement Mamba's outputs.
SMPLX-Lite: A Realistic and Drivable Avatar Benchmark with Rich Geometry and Texture Annotations
We present SMPLX-Lite dataset, the most comprehensive clothing avatar dataset with multi-view RGB sequences, keypoints annotations, textured scanned meshes, and textured SMPLX-Lite-D models.
BDC-Occ: Binarized Deep Convolution Unit For Binarized Occupancy Network
To bridge these gaps, we propose a novel binarized deep convolution (BDC) unit that effectively enhances performance while increasing the number of binarized convolutional layers.
Modeling User Fatigue for Sequential Recommendation
In this paper, we propose to model user Fatigue in interest learning for sequential Recommendations (FRec).
OccGaussian: 3D Gaussian Splatting for Occluded Human Rendering
OccGaussian initializes 3D Gaussian distributions in the canonical space, and we perform occlusion feature query at occluded regions, the aggregated pixel-align feature is extracted to compensate for the missing information.
UV Gaussians: Joint Learning of Mesh Deformation and Gaussian Textures for Human Avatar Modeling
Therefore, we propose UV Gaussians, which models the 3D human body by jointly learning mesh deformations and 2D UV-space Gaussian textures.
DiffVein: A Unified Diffusion Network for Finger Vein Segmentation and Authentication
To fill this gap, we introduce DiffVein, a unified diffusion model-based framework which simultaneously addresses vein segmentation and authentication tasks.
UV-SAM: Adapting Segment Anything Model for Urban Village Identification
Urban villages, defined as informal residential areas in or around urban centers, are characterized by inadequate infrastructures and poor living conditions, closely related to the Sustainable Development Goals (SDGs) on poverty, adequate housing, and sustainable cities.
LLM-Powered Hierarchical Language Agent for Real-time Human-AI Coordination
AI agents powered by Large Language Models (LLMs) have made significant advances, enabling them to assist humans in diverse complex tasks and leading to a revolution in human-AI coordination.
DSR-Diff: Depth Map Super-Resolution with Diffusion Model
Color-guided depth map super-resolution (CDSR) improve the spatial resolution of a low-quality depth map with the corresponding high-quality color map, benefiting various applications such as 3D reconstruction, virtual reality, and augmented reality.
EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities
Existing agent modeling typically employs predetermined rules or learning-based neural networks for decision-making.
CLIP-based Synergistic Knowledge Transfer for Text-based Person Retrieval
Text-based Person Retrieval (TPR) aims to retrieve the target person images given a textual query.
Elucidating the solution space of extended reverse-time SDE for diffusion models
In this work, we formulate the sampling process as an extended reverse-time SDE (ER SDE), unifying prior explorations into ODEs and SDEs.
LLA-FLOW: A Lightweight Local Aggregation on Cost Volume for Optical Flow Estimation
Lack of texture often causes ambiguity in matching, and handling this issue is an important challenge in optical flow estimation.
IRGen: Generative Modeling for Image Retrieval
While generative modeling has become prevalent across numerous research fields, its integration into the realm of image retrieval remains largely unexplored and underjustified.
Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS "Cold-Start"
In the first step, we train different sub-predictors on different types of available low-fidelity information to extract beneficial knowledge as low-fidelity experts.
Meta-Learning based Degradation Representation for Blind Super-Resolution
The most of CNN based super-resolution (SR) methods assume that the degradation is known (\eg, bicubic).
SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization
In addition, we propose a background-attentional adaptive instance normalization (BAIN) to achieve an attention-weighted background feature distribution according to the foreground-background feature similarity.
STDAN: Deformable Attention Network for Space-Time Video Super-Resolution
Second, we put forward a spatial-temporal deformable feature aggregation (STDFA) module, in which spatial and temporal contexts in dynamic video frames are adaptively captured and aggregated to enhance SR reconstruction.
Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-based Super-Resolution
To improve matching efficiency, we design a novel Embedded PatchMacth scheme with random samples propagation, which involves end-to-end training with asymptotic linear computational cost to the input size.
Efficient Non-Local Contrastive Attention for Image Super-Resolution
To demonstrate the effectiveness of ENLCA, we build an architecture called Efficient Non-Local Contrastive Network (ENLCN) by adding a few of our modules in a simple backbone.
APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation
In practice, it can adaptively generate multiple class-agnostic prototypes for query images and learn feature alignment in a self-contrastive manner.
Ranked #48 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Group Fisher Pruning for Practical Network Compression
Our method can be used to prune any structures including those with coupled channels.
Ranked #4 on
Network Pruning
on ImageNet
ER-IQA: Boosting Perceptual Quality Assessment Using External Reference Images
In this paper, we introduce a brand new scheme, namely external-reference image quality assessment (ER-IQA), by introducing external reference images to bridge the gap between FR and NR-IQA.
SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes
To this end, we generate self-contrastive background prototypes directly from the query image, with which we enable the construction of complete sample pairs and thus a complementary and auxiliary segmentation task to achieve the training of a better segmentation model.
A region-based descriptor network for uniformly sampled keypoints
By the new descriptor, we can obtain more high confidence matching points without extremum operation.
Towards Impartial Multi-task Learning
Multi-task learning (MTL) has been widely used in representation learning.
Attention Cube Network for Image Restoration
The adaptive spatial attention branch (ASAB) and the adaptive channel attention branch (ACAB) constitute the adaptive dual attention module (ADAM), which can capture the long-range spatial and channel-wise contextual information to expand the receptive field and distinguish different types of information for more effective feature representations.
Noise-Sampling Cross Entropy Loss: Improving Disparity Regression Via Cost Volume Aware Regularizer
Recent end-to-end deep neural networks for disparity regression have achieved the state-of-the-art performance.
Real-MFF: A Large Realistic Multi-focus Image Dataset with Ground Truth
In this letter, we introduce a large and realistic multi-focus dataset called Real-MFF, which contains 710 pairs of source images with corresponding ground truth images.
XSepConv: Extremely Separated Convolution
Depthwise convolution has gradually become an indispensable operation for modern efficient neural networks and larger kernel sizes ($\ge5$) have been applied to it recently.
An α-Matte Boundary Defocus Model Based Cascaded Network for Multi-focus Image Fusion
Based on this {\alpha}-matte defocus model and the generated data, a cascaded boundary aware convolutional network termed MMF-Net is proposed and trained, aiming to achieve clearer fusion results around the FDB.
LCSCNet: Linear Compressing Based Skip-Connecting Network for Image Super-Resolution
In this paper, we develop a concise but efficient network architecture called linear compressing based skip-connecting network (LCSCNet) for image super-resolution.
Ranked #17 on
Image Super-Resolution
on Set14 - 3x upscaling
Boundary Aware Multi-Focus Image Fusion Using Deep Neural Network
Since it is usually difficult to capture an all-in-focus image of a 3D scene directly, various multi-focus image fusion methods are employed to generate it from several images focusing at different depths.
Lightweight Feature Fusion Network for Single Image Super-Resolution
Specifically, a spindle block is composed of a dimension extension unit, a feature exploration unit and a feature refinement unit.
Ranked #14 on
Image Super-Resolution
on Manga109 - 3x upscaling
Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network
With the motivation of various convolutional neural network(CNN) structures succeeded in single image super-resolution(SISR) task, an end-to-end convolutional neural network is proposed to reconstruct the high resolution(HR) optical flow field from initial LR optical flow with the guidence of the first frame used in optical flow estimation.
