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Found 21 papers, 15 papers with code
Fine-Grained Entity Segmentation
In dense image segmentation tasks (e. g., semantic, panoptic), existing methods can hardly generalize well to unseen image domains, predefined classes, and image resolution & quality variations.
PalGAN: Image Colorization with Palette Generative Adversarial Networks
Multimodal ambiguity and color bleeding remain challenging in colorization.
Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning
In the first stage, we obtain a robust feature extractor, which could serve for all images with base and novel categories.
MAT: Mask-Aware Transformer for Large Hole Image Inpainting
Recent studies have shown the importance of modeling long-range interactions in the inpainting problem.
Ranked #1 on
Image Inpainting
on CelebA-HQ
CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation
To improve instance-level detection/segmentation performance, existing self-supervised and semi-supervised methods extract either task-unrelated or task-specific training signals from unlabeled data.
High Quality Segmentation for Ultra High-resolution Images
To segment 4K or 6K ultra high-resolution images needs extra computation consideration in image segmentation.
Image Synthesis via Semantic Composition
In this paper, we present a novel approach to synthesize realistic images based on their semantic layouts.
Multi-Scale Aligned Distillation for Low-Resolution Detection
However, this option traditionally hurts the detection performance much.
ICM-3D: Instantiated Category Modeling for 3D Instance Segmentation
Separating 3D point clouds into individual instances is an important task for 3D vision.
Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervision
In particular, Panoptic FCN encodes each object instance or stuff category with the proposed kernel generator and produces the prediction by convolving the high-resolution feature directly.
Panoptic Segmentation
Weakly-supervised panoptic segmentation
Open-World Entity Segmentation
By removing the need of class label prediction, the models trained for such task can focus more on improving segmentation quality.
LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-Resolution and Beyond
Single image super-resolution (SISR) deals with a fundamental problem of upsampling a low-resolution (LR) image to its high-resolution (HR) version.
Scale-aware Automatic Augmentation for Object Detection
We propose Scale-aware AutoAug to learn data augmentation policies for object detection.
Best-Buddy GANs for Highly Detailed Image Super-Resolution
We consider the single image super-resolution (SISR) problem, where a high-resolution (HR) image is generated based on a low-resolution (LR) input.
MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution
Motivated by these findings, we propose a temporal multi-correspondence aggregation strategy to leverage similar patches across frames, and a cross-scale nonlocal-correspondence aggregation scheme to explore self-similarity of images across scales.
Dynamic Scale Training for Object Detection
We propose a Dynamic Scale Training paradigm (abbreviated as DST) to mitigate scale variation challenge in object detection.
PointINS: Point-based Instance Segmentation
In this paper, we explore the mask representation in instance segmentation with Point-of-Interest (PoI) features.
Amodal Instance Segmentation With KINS Dataset
We propose the network structure to reason invisible parts via a new multi-task framework with Multi-View Coding (MVC), which combines information in various recognition levels.
Sequential Context Encoding for Duplicate Removal
Duplicate removal is a critical step to accomplish a reasonable amount of predictions in prevalent proposal-based object detection frameworks.
Path Aggregation Network for Instance Segmentation
The way that information propagates in neural networks is of great importance.
Ranked #3 on
Object Detection
on iSAID
A Novel Biologically Mechanism-Based Visual Cognition Model--Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity
In general, the robustness and precision of recognition is one of the key problems for object recognition models.
