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- Path Aggregation Network for Instance Segmentation9,321
Path Aggregation Network (PANet) is proposed aiming at boosting information flow in proposal-based instance segmentation framework by enhancing the entire feature hierarchy with accurate localization signals in lower layers by bottom-up path augmentation.
- MAT: Mask-Aware Transformer for Large Hole Image Inpainting539
A novel transformer-based model for large hole inpainting is presented, which unifies the merits of transformers and convolutions to efficiently process high-resolution images.
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- Faster R-CNN for marine organisms detection and recognition using data augmentation194
Three data augmentation methods dedicated to underwater-imaging are proposed, the inverse process of underwater image restoration is used to simulate different marine turbulence environments, and perspective transformation and Illumination synthesis are proposed to simulateDifferent marine uneven illuminating environments.
- MuCAN: Multi-correspondence Aggregation Network for Video Super-Resolution185
This work proposes a temporal multi-correspondence aggregation strategy to leverage similar patches across frames, and a cross-scale nonlocal-cor correspondence aggregation scheme to explore self-similarity of images across scales.
- BigDL154
This paper presents BigDL (adistributeddeeplearning framework for Apache Spark), which allows deep learning applications to run on the Apache Hadoop/Spark cluster so as to directly process the production data, and as a part of the end-to-end data analysis pipeline for deployment and management.
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- High Quality Entity Segmentation115
CropFormer is the first query-based Transformer architecture that can effectively fuse mask predictions from multiple image views, by learning queries that effectively associate the same entities across the full image and its crop.
- Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms106
A new network, SCANet (spatial-context-attention network), to identify pine nematode disease based on unmanned aerial vehicle (UAV) multi-spectral remote sensing images and a spatial information retention module is designed to reduce the loss of spatial information.
- Open World Entity Segmentation103
This work proposes a CondInst-like fully-convolutional architecture with two novel modules specifically designed to exploit the class-agnostic and non-overlapping requirements of ES, and investigates the feasibility of convolutional center-based representation to segment things and stuffs in a unified manner.
- Best-Buddy GANs for Highly Detailed Image Super-resolution95
This work considers the single image super-resolution (SISR) problem, and proposes best-buddy GANs (Beby-GAN) for rich-detail SISR, a region-aware adversarial learning strategy that directs the model to focus on generating details for textured areas adaptively.
- Image Synthesis via Semantic Composition75
A novel approach to synthesize realistic images based on their semantic layouts hypothesizes that for objects with similar appearance, they share similar representation, and proposes a dynamic weighted network constructed by spatially conditional computation.
- Multi-Scale Aligned Distillation for Low-Resolution Detection74
The idea of spatially aligning feature maps between models of varying input resolutions by shifting feature pyramid position is explored and crossed feature-level fusion is proposed to dynamically fuse teacher’s multi-resolution features to guide the student better.
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- Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervision65
A conceptually simple, strong, and efficient framework for fully- and weakly-supervised panoptic segmentation, called Panoptic FCN, which encodes each object instance or stuff category with the proposed kernel generator and produces the prediction by convolving the high-resolution feature directly.
- RGB-D Co-attention Network for Semantic Segmentation47
The extensive experiments validate the effectiveness of CANet in fusing RGB and D features, achieving the state-of-the-art performance on two challenging RGB-D semantic segmentation datasets, i.e., NYUDv2, SUN-RGBD.
- Reason3D: Searching and Reasoning 3D Segmentation via Large Language Model36
Reason3D is introduced, a novel LLM designed for comprehensive 3D understanding that processes point cloud data and text prompts to produce textual responses and segmentation masks, enabling advanced tasks such as 3D reasoning segmentation, hierarchical searching, express referring, and question answering with detailed mask outputs.
- Dual Associated Encoder for Face Restoration36
This work proposes a novel dual-branch framework named DAEFR, which introduces an auxiliary LQ branch that extracts crucial information from the LQ inputs and incorporates association training to promote effective synergy between the two branches, enhancing code prediction and output quality.
- PointINS: Point-Based Instance Segmentation34
This paper proposes an instance-aware convolution that decomposes this mask representation learning task into two tractable modules as instance- aware weights and instance-agnostic features, improving mask learning efficiency by avoiding employing several independent convolutions.
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- UniGS: Unified Representation for Image Generation and Segmentation33
A novel unified representation of diffusion models for image generation and segmentation using a colormap to represent entity-level masks is introduced, addressing the challenge of varying entity numbers while aligning the representation closely with the image RGB domain.
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- ICM-3D: Instantiated Category Modeling for 3D Instance Segmentation29
This paper proposes ICM-3D, a single-step method to segment 3D instances via instantiated categorization and shows that it obtains inspiring performance across multiple frameworks, backbones and benchmarks.
- Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation28
A unified framework, Reference Twice (RefT), is introduced to exploit the relationship between support and query features for FSIS and related tasks, demonstrating that support object queries encode key factors after base training, allowing query features to be enhanced twice at both feature and query levels.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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