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Found 40 papers, 16 papers with code
GrassNet: State Space Model Meets Graph Neural Network
To overcome these issues, in this paper, we propose Graph State Space Network (GrassNet), a novel graph neural network with theoretical support that provides a simple yet effective scheme for designing and learning arbitrary graph spectral filters.
Single Image Dehazing Using Scene Depth Ordering
In this paper, we propose a depth order guided single image dehazing method, which utilizes depth order in hazy images to guide the dehazing process to achieve a similar depth perception in corresponding dehazing results.
STAR: Scale-wise Text-to-image generation via Auto-Regressive representations
We present STAR, a text-to-image model that employs scale-wise auto-regressive paradigm.
MotionClone: Training-Free Motion Cloning for Controllable Video Generation
Motion-based controllable video generation offers the potential for creating captivating visual content.
DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment
Specifically, DFA-GNN extends the principles of DFA to adapt to graph data and unique architecture of GNNs, which incorporates the information of graph topology into the feedback links to accommodate the non-Euclidean characteristics of graph data.
Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey
This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs.
Seed Optimization with Frozen Generator for Superior Zero-shot Low-light Enhancement
In this work, we observe that the generators, which are pre-trained on massive natural images, inherently hold the promising potential for superior low-light image enhancement against varying scenarios. Specifically, we embed a pre-trained generator to Retinex model to produce reflectance maps with enhanced detail and vividness, thereby recovering features degraded by low-light conditions. Taking one step further, we introduce a novel optimization strategy, which backpropagates the gradients to the input seeds rather than the parameters of the low-light enhancement model, thus intactly retaining the generative knowledge learned from natural images and achieving faster convergence speed.
Masked Pre-training Enables Universal Zero-shot Denoiser
In this work, we observe that model trained on vast general images via masking strategy, has been naturally embedded with their distribution knowledge, thus spontaneously attains the underlying potential for strong image denoising.
Stronger Fewer & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation
Driven by the motivation that Leveraging Stronger pre-trained models and Fewer trainable parameters for Superior generalizability we introduce a robust fine-tuning approach namely "Rein" to parameter-efficiently harness VFMs for DGSS.
Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation
Driven by the motivation that Leveraging Stronger pre-trained models and Fewer trainable parameters for Superior generalizability, we introduce a robust fine-tuning approach, namely Rein, to parameter-efficiently harness VFMs for DGSS.
Ranked #1 on
Domain Adaptation
on GTAV to Cityscapes+Mapillary
Transmission line condition prediction based on semi-supervised learning
Transmission line state assessment and prediction are of great significance for the rational formulation of operation and maintenance strategy and improvement of operation and maintenance level.
Disentangle then Parse:Night-time Semantic Segmentation with Illumination Disentanglement
2) Based on the observation that the illumination component can serve as a cue for some semantically confused regions, we further introduce an Illumination-Aware Parser (IAParser) to explicitly learn the correlation between semantics and lighting, and aggregate the illumination features to yield more precise predictions.
Bridging the Gap: Multi-Level Cross-Modality Joint Alignment for Visible-Infrared Person Re-Identification
Visible-Infrared person Re-IDentification (VI-ReID) is a challenging cross-modality image retrieval task that aims to match pedestrians' images across visible and infrared cameras.
Cross-Modality Person Re-identification
Image Classification
+4
FreeDrag: Feature Dragging for Reliable Point-based Image Editing
To serve the intricate and varied demands of image editing, precise and flexible manipulation in image content is indispensable.
NPS: A Framework for Accurate Program Sampling Using Graph Neural Network
Furthermore, NPS shows higher accuracy and generality than the state-of-the-art GNN approach in code behavior learning, enabling the generation of high-quality execution embeddings.
The Cascaded Forward Algorithm for Neural Network Training
Backpropagation algorithm has been widely used as a mainstream learning procedure for neural networks in the past decade, and has played a significant role in the development of deep learning.
Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges
The former focuses on collaboration modules and efficiency, and the latter is devoted to addressing the problems in actual application.
Disentangle then Parse: Night-time Semantic Segmentation with Illumination Disentanglement
2) Based on the observation that the illumination component can serve as a cue for some semantically confused regions, we further introduce an Illumination-Aware Parser (IAParser) to explicitly learn the correlation between semantics and lighting, and aggregate the illumination features to yield more precise predictions.
Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
In this work, we resort to data mixing to establish a deliberated domain bridging (DDB) for DASS, through which the joint distributions of source and target domains are aligned and interacted with each in the intermediate space.
Ranked #1 on
Domain Adaptation
on GTAV+Synscapes to Cityscapes
Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation
Such NWD can be coupled with the classifier to serve as a discriminator satisfying the K-Lipschitz constraint without the requirements of additional weight clipping or gradient penalty strategy.
Ranked #2 on
Domain Adaptation
on ImageCLEF-DA
Boundary Corrected Multi-scale Fusion Network for Real-time Semantic Segmentation
Image semantic segmentation aims at the pixel-level classification of images, which has requirements for both accuracy and speed in practical application.
LighTN: Light-weight Transformer Network for Performance-overhead Tradeoff in Point Cloud Downsampling
Compared with traditional task-irrelevant downsampling methods, task-oriented neural networks have shown improved performance in point cloud downsampling range.
2D+3D facial expression recognition via embedded tensor manifold regularization
In this paper, a novel approach via embedded tensor manifold regularization for 2D+3D facial expression recognition (FERETMR) is proposed.
3D Facial Expression Recognition
Dimensionality Reduction
+1
Prototype Guided Network for Anomaly Segmentation
In the model, prototypes are used to model the hierarchical category semantic information and distinguish OOD pixels.
GLAN: A Graph-based Linear Assignment Network
The experimental results on a synthetic dataset reveal that our method outperforms state-of-the-art baselines and achieves consistently high accuracy with the increment of the problem size.
Camera-aware Style Separation and Contrastive Learning for Unsupervised Person Re-identification
Unsupervised person re-identification (ReID) is a challenging task without data annotation to guide discriminative learning.
MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared Person Re-Identification
The RGB-infrared cross-modality person re-identification (ReID) task aims to recognize the images of the same identity between the visible modality and the infrared modality.
Cross-Modality Person Re-identification
Person Re-Identification
CMTR: Cross-modality Transformer for Visible-infrared Person Re-identification
The existing convolutional neural network-based methods mainly face the problem of insufficient perception of modalities' information, and can not learn good discriminative modality-invariant embeddings for identities, which limits their performance.
Cross-Modality Person Re-identification
Person Re-Identification
Joint Graph Learning and Matching for Semantic Feature Correspondence
In this paper, we propose a joint \emph{graph learning and matching} network, named GLAM, to explore reliable graph structures for boosting graph matching.
Time-Frequency Analysis based Deep Interference Classification for Frequency Hopping System
Considering the possibility of presence multiple interferences in the frequency hopping system, in order to fully extract effective features of the interferences from the received signals, the linear and bilinear transform based composite time-frequency analysis method is adopted.
GM-MLIC: Graph Matching based Multi-Label Image Classification
Multi-Label Image Classification (MLIC) aims to predict a set of labels that present in an image.
Attention Models for Point Clouds in Deep Learning: A Survey
Recently, the advancement of 3D point clouds in deep learning has attracted intensive research in different application domains such as computer vision and robotic tasks.
FASG: Feature Aggregation Self-training GCN for Semi-supervised Node Classification
Recently, Graph Convolutioal Networks (GCNs) have achieved significant success in many graph-based learning tasks, especially for node classification, due to its excellent ability in representation learning.
EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising
In this paper, we propose the Edge enhancement based Densely connected Convolutional Neural Network (EDCNN).
Ranked #1 on
Denoising
on AAPM
Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review
Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing.
Time-Frequency Analysis based Blind Modulation Classification for Multiple-Antenna Systems
Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time-frequency analysis method based on the windowed short-time Fourier transform is used to analyse the time-frequency characteristics of time-domain modulated signals.
HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization
Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct.
Domain Adaptive Attention Learning for Unsupervised Person Re-Identification
In this approach, a domain adaptive attention model is learned to separate the feature map into domain-shared part and domain-specific part.
Robust Object Tracking Based on Temporal and Spatial Deep Networks
Our deep architecture contains three networks, a Feature Net, a Temporal Net, and a Spatial Net.
Compositional Belief Update
We also explore other compositional belief change operators: erasure is developed as a dual operator to update; we show that a forget operator is definable in terms of update; and we give a definition of the compositional revision operator.
