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Found 23 papers, 7 papers with code
LRR: Language-Driven Resamplable Continuous Representation against Adversarial Tracking Attacks
To achieve high accuracy on both clean and adversarial data, we propose building a spatial-temporal continuous representation using the semantic text guidance of the object of interest.
Natural-language-driven Simulation Benchmark and Copilot for Efficient Production of Object Interactions in Virtual Road Scenes
To facilitate the research of NLD simulation, we collect the Language-to-Interaction(L2I) benchmark dataset with 120, 000 natural-language descriptions of object interactions in 6 common types of road topologies.
BEVControl: Accurately Controlling Street-view Elements with Multi-perspective Consistency via BEV Sketch Layout
To this end, we propose a two-stage generative method, dubbed BEVControl, that can generate accurate foreground and background contents.
CVSformer: Cross-View Synthesis Transformer for Semantic Scene Completion
In this paper, we propose Cross-View Synthesis Transformer (CVSformer), which consists of Multi-View Feature Synthesis and Cross-View Transformer for learning cross-view object relationships.
Surface Geometry Processing: An Efficient Normal-based Detail Representation
With the rapid development of high-resolution 3D vision applications, the traditional way of manipulating surface detail requires considerable memory and computing time.
On the Robustness of Segment Anything
Second, we extend representative adversarial attacks against SAM and study the influence of different prompts on robustness.
Leveraging Inpainting for Single-Image Shadow Removal
In this work, we find that pretraining shadow removal networks on the image inpainting dataset can reduce the shadow remnants significantly: a naive encoder-decoder network gets competitive restoration quality w. r. t.
Non-Iterative Scribble-Supervised Learning with Pacing Pseudo-Masks for Medical Image Segmentation
To address these issues, we propose a non-iterative method where a stream of varying (pacing) pseudo-masks teach a network via consistency training, named PacingPseudo.
MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting
As a result, the final method takes the advantage of effective semantic & image-level filling for high-fidelity inpainting.
Uncertainty-Aware Cascaded Dilation Filtering for High-Efficiency Deraining
First, we propose the uncertainty-aware cascaded predictive filtering (UC-PFilt) that can identify the difficulties of reconstructing clean pixels via predicted kernels and remove the residual rain traces effectively.
Recurrent Feature Propagation and Edge Skip-Connections for Automatic Abdominal Organ Segmentation
Automatic segmentation of abdominal organs in computed tomography (CT) images can support radiation therapy and image-guided surgery workflows.
CarveNet: Carving Point-Block for Complex 3D Shape Completion
3D point cloud completion is very challenging because it heavily relies on the accurate understanding of the complex 3D shapes (e. g., high-curvature, concave/convex, and hollowed-out 3D shapes) and the unknown & diverse patterns of the partially available point clouds.
RANet: Region Attention Network for Semantic Segmentation
In contrast to the previous methods, RANet configures the information pathways between the pixels in different regions, enabling the region interaction to exchange the regional context for enhancing all of the pixels in the image.
PRSNet: Part Relation and Selection Network for Bone Age Assessment
These kinds of information can be effectively captured by the relation of different anatomical parts of hand bone.
ZigZagNet: Fusing Top-Down and Bottom-Up Context for Object Segmentation
In this work, we introduce ZigZagNet, which aggregates a richer multi-context feature map by using not only dense top-down and bottom-up propagation, but also by introducing pathways crossing between different levels of the top-down and the bottom-up hierarchies, in a zig-zag fashion.
Multi-Scale Context Intertwining for Semantic Segmentation
Accurate semantic image segmentation requires the joint consideration of local appearance, semantic information, and global scene context.
Structure-aware Generative Network for 3D-Shape Modeling
The key idea is that during the analysis, the two branches exchange information between them, thereby learning the dependencies between structure and geometry and encoding two augmented features, which are then fused into a single latent code.
Graphics
Cascaded Feature Network for Semantic Segmentation of RGB-D Images
Our approach is to use the available depth to split the image into layers with common visual characteristic of objects/scenes, or common "scene-resolution".
Ranked #72 on
Semantic Segmentation
on NYU Depth v2
Learning to Aggregate Ordinal Labels by Maximizing Separating Width
While crowdsourcing has been a cost and time efficient method to label massive samples, one critical issue is quality control, for which the key challenge is to infer the ground truth from noisy or even adversarial data by various users.
ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation
Large-scale data is of crucial importance for learning semantic segmentation models, but annotating per-pixel masks is a tedious and inefficient procedure.
Deep LAC: Deep Localization, Alignment and Classification for Fine-Grained Recognition
Our major contribution is to propose a valve linkage function(VLF) for back-propagation chaining and form our deep localization, alignment and classification (LAC) system.
Two-Class Weather Classification
Given a single outdoor image, this paper proposes a collaborative learning approach for labeling it as either sunny or cloudy.
Learning Important Spatial Pooling Regions for Scene Classification
We address the false response influence problem when learning and applying discriminative parts to construct the mid-level representation in scene classification.
