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Works159 from public data
- Pyramid Scene Parsing Network15,291
This paper exploits the capability of global context information by different-region-based context aggregation through the pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet) to produce good quality results on the scene parsing task.
- Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers3,852
This paper deploys a pure transformer to encode an image as a sequence of patches, termed SEgmentation TRansformer (SETR), and shows that SETR achieves new state of the art on ADE20K, Pascal Context, and competitive results on Cityscapes.
- Point Transformer2,467
The Point Transformer design improves upon prior work across domains and tasks and crosses the 70% mIoU threshold for the first time on the challenging S3DIS dataset.
- Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data2,221
This work designs a data engine to collect and automatically annotate large-scale unlabeled data and develops an auxiliary supervision to enforce the model to inherit rich semantic priors from pre-trained encoders, which results in a better depth-conditioned ControlNet.
- ICNet for Real-Time Semantic Segmentation on High-Resolution Images1,627
An image cascade network (ICNet) that incorporates multi-resolution branches under proper label guidance to address the challenging task of real-time semantic segmentation is proposed and in-depth analysis of the framework is provided.
- PSANet: Point-wise Spatial Attention Network for Scene Parsing1,191
The point-wise spatial attention network (PSANet) is proposed to relax the local neighborhood constraint and achieves top performance on various competitive scene parsing datasets, including ADE20K, PASCAL VOC 2012 and Cityscapes, demonstrating its effectiveness and generality.
- Point Transformer V3: Simpler, Faster, Stronger1,171
This paper presents Point Transformer V3 (PTv3), which prioritizes simplicity and efficiency over the accuracy of certain mechanisms that are minor to the over-all performance after scaling, such as replacing the precise neighbor search by KNN with an efficient serialized neighbor mapping of point clouds organized with specific patterns.
- Exploring Self-Attention for Image Recognition946
This work considers two forms of self-attention, pairwise and patchwise, which generalizes standard dot-product attention and is fundamentally a set operator and strictly more powerful than convolution.
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- DriveGPT4: Interpretable End-to-End Autonomous Driving Via Large Language Model795
DriveGPT4 is introduced, a novel interpretable end-to-end autonomous driving system based on LLMs capable of processing multi-frame video inputs and textual queries that facilitates the interpretation of vehicle actions, offers pertinent reasoning, and effectively addresses a diverse range of questions posed by users.
- Prior Guided Feature Enrichment Network for Few-Shot Segmentation764
The PFENet consists of novel designs of a training-free prior mask generation method that not only retains generalization power but also improves model performance and Feature Enrichment Module (FEM) that overcomes spatial inconsistency by adaptively enriching query features with support features and prior masks.
- Distilling Knowledge via Knowledge Review717
For the first time in knowledge distillation, cross-stage connection paths are proposed and a new review mechanism is proposed that requires negligible computation overhead, and outperforms other methods on a variety of tasks.
- PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation610
This paper designs a two-branch network to extract point features and predict semantic labels and offsets, for shifting each point towards its respective instance centroid, and presents PointGroup, a new end-to-end bottom-up architecture specifically focused on better grouping the points by exploring the void space between objects.
- PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds591
Different from the existing point convolution operators whose network architectures are often heavily engineered, the PAConv integrates into classical MLP-based point cloud pipelines without changing network configurations and significantly improves baseline performance on both classification and segmentation tasks, yet with decent efficiency.
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- LAVT: Language-Aware Vision Transformer for Referring Image Segmentation569
This work shows that significantly better cross-modal alignments can be achieved through the early fusion of linguistic and visual features in intermediate layers of a vision Transformer encoder network.
- Stratified Transformer for 3D Point Cloud Segmentation564
Stratified Transformer is proposed that is able to capture long-range contexts and demonstrates strong generalization ability and high performance and first-layer point embedding to aggregate local information, which facilitates convergence and boosts performance.
- AnyDoor: Zero-shot Object-level Image Customization539
This work presents AnyDoor, a diffusion-based image generator with the power to teleport target objects to new scenes at user-specified locations with desired shapes, and proposes to borrow knowledge from video datasets, which can observe various forms of a single object, leading to stronger model generalizability and robustness.
- UPSNet: A Unified Panoptic Segmentation Network469
A parameter-free panoptic head is introduced which solves thepanoptic segmentation via pixel-wise classification and first leverages the logits from the previous two heads and then innovatively expands the representation for enabling prediction of an extra unknown class which helps better resolving the conflicts between semantic and instance segmentation.
- SegStereo: Exploiting Semantic Information for Disparity Estimation383
This paper suggests that appropriate incorporation of semantic cues can greatly rectify prediction in commonly-used disparity estimation frameworks and proposes a unified model SegStereo, which employs semantic features from segmentation and introduces semantic softmax loss, which helps improve the prediction accuracy of disparity maps.
- PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer343
This paper proposes the PhysFormer, an end-to-end video transformer based architecture, to adaptively aggregate both local and global spatio-temporal features for rPPG representation enhancement, and proposes the label distribution learning and a curriculum learning inspired dynamic constraint in frequency domain.
- Semi-supervised Semantic Segmentation with Directional Context-aware Consistency261
This paper proposes to maintain the context-aware consistency between features of the same identity but with different contexts, making the representations robust to the varying environments, and presents the Directional Contrastive Loss (DC Loss) to accomplish the consistency in a pixel-to-pixel manner.
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- Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation219
This work establishes a hierarchical graph framework, where the graph is initialized from a coarse layer and gradually enriched along the point decoding process, and predicts a label to indicate the semantic consistency of the two connected points to enhance point prediction.
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 2026-10-13. 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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