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Found 31 papers, 11 papers with code
DMSSN: Distilled Mixed Spectral-Spatial Network for Hyperspectral Salient Object Detection
To address these challenges, we propose a novel approach termed the Distilled Mixed Spectral-Spatial Network (DMSSN), comprising a Distilled Spectral Encoding process and a Mixed Spectral-Spatial Transformer (MSST) feature extraction network.
Multi-step Temporal Modeling for UAV Tracking
This module leverages temporal information to refresh the template feature, yielding a more precise correlation map.
PointGL: A Simple Global-Local Framework for Efficient Point Cloud Analysis
Efficient analysis of point clouds holds paramount significance in real-world 3D applications.
MsSVT++: Mixed-scale Sparse Voxel Transformer with Center Voting for 3D Object Detection
To mitigate the computational complexity associated with applying a window-based transformer in 3D voxel space, we introduce a novel Chessboard Sampling strategy and implement voxel sampling and gathering operations sparsely using a hash map.
MetaSeg: Content-Aware Meta-Net for Omni-Supervised Semantic Segmentation
Noisy labels, inevitably existing in pseudo segmentation labels generated from weak object-level annotations, severely hampers model optimization for semantic segmentation.
Factorization Vision Transformer: Modeling Long Range Dependency with Local Window Cost
Accordingly, we propose a novel factorization self-attention mechanism (FaSA) that enjoys both the advantages of local window cost and long-range dependency modeling capability.
BACTrack: Building Appearance Collection for Aerial Tracking
At the same time, the appearance discriminator employs an online adaptive template-update strategy to ensure that the collected multiple templates remain reliable and diverse, allowing them to closely follow rapid changes in the target's appearance and suppress background interference during tracking.
Spectrum-driven Mixed-frequency Network for Hyperspectral Salient Object Detection
The Spectral Saliency approximates the region of salient objects, while the Spectral Edge captures edge information of salient objects.
Spectral-wise Implicit Neural Representation for Hyperspectral Image Reconstruction
The Fourier coordinate encoder enhances the SINR's ability to emphasize high-frequency components, while the spectral scale factor module guides the SINR to adapt to the variable number of spectral channels.
Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions
Robust 3D perception under corruption has become an essential task for the realm of 3D vision.
RDFNet: Regional Dynamic FISTA-Net for Spectral Snapshot Compressive Imaging
The regional dynamic block guides the network to adjust the transformation domain for different regions.
Rethinking Few-Shot Medical Segmentation: A Vector Quantization View
The existing few-shot medical segmentation networks share the same practice that the more prototypes, the better performance.
Automated Optical Inspection of FAST's Reflector Surface using Drones and Computer Vision
Hence, the timely and accurate detection of surface defects is crucial for FAST's stable operation.
Dynamic Loss For Robust Learning
Label noise and class imbalance commonly coexist in real-world data.
Ranked #6 on
Learning with noisy labels
on ANIMAL
FusionRCNN: LiDAR-Camera Fusion for Two-stage 3D Object Detection
Existing 3D detectors significantly improve the accuracy by adopting a two-stage paradigm which merely relies on LiDAR point clouds for 3D proposal refinement.
RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-lesion Segmentation
Automatic diabetic retinopathy (DR) lesions segmentation makes great sense of assisting ophthalmologists in diagnosis.
Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Data
In practice, however, biased samples with corrupted labels and of tailed classes commonly co-exist in training data.
PAPooling: Graph-based Position Adaptive Aggregation of Local Geometry in Point Clouds
Fine-grained geometry, captured by aggregation of point features in local regions, is crucial for object recognition and scene understanding in point clouds.
Pyramid Correlation based Deep Hough Voting for Visual Object Tracking
Through experiments we find that, without regression, the performance could be equally promising as long as we delicately design the network to suit the training objective.
Updatable Siamese Tracker with Two-stage One-shot Learning
Offline Siamese networks have achieved very promising tracking performance, especially in accuracy and efficiency.
Attribute-conditioned Layout GAN for Automatic Graphic Design
In this paper, we introduce Attribute-conditioned Layout GAN to incorporate the attributes of design elements for graphic layout generation by forcing both the generator and the discriminator to meet attribute conditions.
Human-Aware Motion Deblurring
This paper proposes a human-aware deblurring model that disentangles the motion blur between foreground (FG) humans and background (BG).
Exploiting Semantics for Face Image Deblurring
Specifically, we first use a coarse deblurring network to reduce the motion blur on the input face image.
LayoutGAN: Generating Graphic Layouts with Wireframe Discriminator
Layouts are important for graphic design and scene generation.
LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators
Layout is important for graphic design and scene generation.
Stochastic Channel Decorrelation Network and Its Application to Visual Tracking
Deep convolutional neural networks (CNNs) have dominated many computer vision domains because of their great power to extract good features automatically.
Deep Semantic Face Deblurring
In this paper, we present an effective and efficient face deblurring algorithm by exploiting semantic cues via deep convolutional neural networks (CNNs).
Perceptual Generative Adversarial Networks for Small Object Detection
In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to "super-resolved" ones, achieving similar characteristics as large objects and thus more discriminative for detection.
Multi-stage Object Detection with Group Recursive Learning
Most of existing detection pipelines treat object proposals independently and predict bounding box locations and classification scores over them separately.
Attentive Contexts for Object Detection
We provide preliminary answers to these questions through developing a novel Attention to Context Convolution Neural Network (AC-CNN) based object detection model.
Scale-aware Fast R-CNN for Pedestrian Detection
Taking pedestrian detection as an example, we illustrate how we can leverage this philosophy to develop a Scale-Aware Fast R-CNN (SAF R-CNN) framework.
Ranked #23 on
Pedestrian Detection
on Caltech
