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Found 37 papers, 25 papers with code
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
Explore In-Context Segmentation via Latent Diffusion Models
In-context segmentation has drawn more attention with the introduction of vision foundation models.
Training Class-Imbalanced Diffusion Model Via Overlap Optimization
To address the observed appearance overlap between synthesized images of rare classes and tail classes, we propose a method based on contrastive learning to minimize the overlap between distributions of synthetic images for different classes.
Generalizable Entity Grounding via Assistance of Large Language Model
In this work, we propose a novel approach to densely ground visual entities from a long caption.
RAP-SAM: Towards Real-Time All-Purpose Segment Anything
Segment Anything Model (SAM) is one remarkable model that can achieve generalized segmentation.
TMT-VIS: Taxonomy-aware Multi-dataset Joint Training for Video Instance Segmentation
What we possess are numerous isolated filed-specific datasets, thus, it is appealing to jointly train models across the aggregation of datasets to enhance data volume and diversity.
CSL: Class-Agnostic Structure-Constrained Learning for Segmentation Including the Unseen
Addressing Out-Of-Distribution (OOD) Segmentation and Zero-Shot Semantic Segmentation (ZS3) is challenging, necessitating segmenting unseen classes.
Effective Adapter for Face Recognition in the Wild
Traditional heuristic approaches-either training models directly on these degraded images or their enhanced counterparts using face restoration techniques-have proven ineffective, primarily due to the degradation of facial features and the discrepancy in image domains.
Multi-task Image Restoration Guided By Robust DINO Features
Multi-task image restoration has gained significant interest due to its inherent versatility and efficiency compared to its single-task counterpart.
UniGS: Unified Representation for Image Generation and Segmentation
On the other hand, the progressive dichotomy module can efficiently decode the synthesized colormap to high-quality entity-level masks in a depth-first binary search without knowing the cluster numbers.
ScribbleGen: Generative Data Augmentation Improves Scribble-supervised Semantic Segmentation
We propose ScribbleGen, a generative data augmentation method that leverages a ControlNet diffusion model conditioned on semantic scribbles to produce high-quality training data.
Pyramid Diffusion for Fine 3D Large Scene Generation
Directly transferring the 2D techniques to 3D scene generation is challenging due to significant resolution reduction and the scarcity of comprehensive real-world 3D scene datasets.
Rethinking Evaluation Metrics of Open-Vocabulary Segmentaion
We benchmarked the proposed evaluation metrics on 12 open-vocabulary methods of three segmentation tasks.
Dual Associated Encoder for Face Restoration
Restoring facial details from low-quality (LQ) images has remained a challenging problem due to its ill-posedness induced by various degradations in the wild.
Ranked #2 on
Blind Face Restoration
on WIDER
AIMS: All-Inclusive Multi-Level Segmentation
Despite the progress of image segmentation for accurate visual entity segmentation, completing the diverse requirements of image editing applications for different-level region-of-interest selections remains unsolved.
High Quality Entity Segmentation
Given the high-quality and -resolution nature of the dataset, we propose CropFormer which is designed to tackle the intractability of instance-level segmentation on high-resolution images.
High-Quality Entity Segmentation
It improves mask prediction by fusing high-res image crops that provide more fine-grained image details and the full image.
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.
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.
