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- 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.
- Long-Term Cloth-Changing Person Re-identification248
This work introduces a shape embedding module as well as a cloth-elimination shape-distillation module aiming to eliminate the now unreliable clothing appearance features and focus on the body shape information.
- Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer243
This work proposes to simplify the meta-learning task by focusing solely on the simplest component – the classifier, whilst leaving the en-coder and decoder to pre-training, and introduces a Classifier Weight Transformer (CWT) designed to dynamically adapt the support-set trained classifier’s weights to each query image in an inductive way.
- Learning a Few-shot Embedding Model with Contrastive Learning207
This paper proposes a novel contrastive training scheme dubbed infoPatch, exploiting the patch-wise relationship to substantially improve the popular infoNCE and investigates the contrastive learning with Noise Contrastive Estimation in a supervised manner for training a few-shot embedding model.
- Instance Credibility Inference for Few-Shot Learning199
This paper presents a simple statistical approach, dubbed Instance Credibility Inference (ICI), to exploit the distribution support of unlabeled instances for few-shot learning to establish new state-of-the-arts on four widely used few- shot learning benchmark datasets.
- Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection170
A depth-conditioned dynamic message propagation (DDMP) network to effectively integrate the multi-scale depth information with the image context and dynamically predicting hybrid depth-dependent filter weights and affinity matrices for propagating information is proposed.
- Learning Dynamic Alignment via Meta-filter for Few-shot Learning134
This paper proposes to learn a dynamic alignment, which can effectively highlight both query regions and channels according to different local support information and facilitates dynamical knowledge adaptation for few-shot learning.
- SeaFormer++: Squeeze-Enhanced Axial Transformer for Mobile Visual Recognition132
A new method squeeze-enhanced Axial Transformer (SeaFormer) for mobile visual recognition, designed a generic attention block characterized by the formulation of squeeze Axial and detail enhancement, which can be further used to create a family of backbone architectures with superior cost-effectiveness.
- Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition124
A depth guided Adaptive Meta-Fusion Network for few-shot video recognition which is termed as AMeFu-Net, which is trained in the meta-learning way and demonstrates the effectiveness of the model on several action recognition benchmarks.
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- Effects of various LED light wavelengths and intensities on the performance of purifying synthetic domestic sewage by microalgae at different influent C/N ratios112
It is demonstrated that the microalgae Chlorella vulgaris can effectively remove nutrients from domestic sewage using the optimal influent C/N ratio, which is 5:1.
- ONCE-3DLanes: Building Monocular 3D Lane Detection98
This work presents ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space, and presents an extrinsic-free, anchorfree method, called SALAD, regressing the 3D coordinates of lanes in image view without converting the feature map into the bird's-eye view (BEV).
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- Generative Semantic Segmentation77
Generative Semantic Segmentation (GSS), a generative learning approach for semantic segmentation, is presented by replacing the conventional per-pixel discriminative learning with a latent prior learning process that model the variational posterior distribution of latent vari-ables given the segmentation mask.
- RoDyn-SLAM: Robust Dynamic Dense RGB-D SLAM With Neural Radiance Fields74
This work proposes a novel dynamic SLAM framework with neural radiance field that achieves state-of-the-art performance among recent neural RGB-D methods in both accuracy and robustness and introduces a motion mask generation method to filter out the invalid sampled rays.
- Delving into Data: Effectively Substitute Training for Black-box Attack73
A novel perspective substitute training that focuses on designing the distribution of data used in the knowledge stealing process and the combination of these two modules can further boost the consistency of the substitute model and target model, which greatly improves the effectiveness of adversarial attack.
- Boundary-sensitive Pre-training for Temporal Localization in Videos66
This paper proposes to synthesize temporal boundaries in existing video action classification datasets and introduces a novel boundary-sensitive pretext (BSP) task, which is superior and complementary to the existing action classification-based pre-training counterpart and achieves new state-of-the-art performance on several temporal localization tasks.
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- How to Trust Unlabeled Data? Instance Credibility Inference for Few-Shot Learning55
A statistical approach, dubbed Instance Credibility Inference (ICI), to exploit the support of unlabeled instances for few-shot visual recognition to collect all the correctly-predicted pseudo-labeled instances from the noisy pseudo- labeled set.
- Imaging port wine stains by fiber optical coherence tomography51
It is demonstrated that OCT may be a useful tool for the noninvasive imaging of PWS and may help determine the photosensitizer dose and laser parameters in photodynamic therapy for treating port wine stains.
- S-NeRF++: Autonomous Driving Simulation via Neural Reconstruction and Generation46
S-NeRF++ is an enhanced neural radiance field for synthesizing large-scale scenes and moving vehicles, with improved scene parameterization and camera pose learning, and an advanced foreground-background fusion pipeline that skillfully integrates illumination and shadow effects, further enhancing the realism of the simulations.
- SGM3D: Stereo Guided Monocular 3D Object Detection42
This paper proposes a stereo-guided monocular 3D object detection framework, dubbed SGM3D, which aligns the monocular feature to the stereo feature and exploits the network's ability of generating the cross-modal feature for accurate 3Dobject detection.
- A Unified Efficient Pyramid Transformer for Semantic Segmentation35
This work proposes a unified framework (UN-EPT) to segment objects by considering both context information and boundary artifacts, and demonstrates promising performance on three popular benchmarks for semantic segmentation with low memory footprint.
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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 2026-10-14. 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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