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Works487 from public data
- Coordinate Attention for Efficient Mobile Network Design6,437
This paper proposes a novel attention mechanism for mobile networks by embedding positional information into channel attention, which it is called "coordinate attention", which factorizes channel attention into two 1D feature encoding processes that aggregate features along the two spatial directions.
- Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet2,580
A new Tokens-To-Token Vision Transformer (T2T-VTT), which incorporates an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study and reduces the parameter count and MACs of vanilla ViT by half.
- 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.
- Return of Frustratingly Easy Domain Adaptation2,159
This work proposes a simple, effective, and efficient method for unsupervised domain adaptation called CORrelation ALignment (CORAL), which minimizes domain shift by aligning the second-order statistics of source and target distributions, without requiring any target labels.
- PANet: Few-Shot Image Semantic Segmentation With Prototype Alignment1,540
This paper tackles the challenging few-shot segmentation problem from a metric learning perspective and presents PANet, a novel prototype alignment network to better utilize the information of the support set to better generalize to unseen object categories.
- MetaFormer is Actually What You Need for Vision1,462
It is argued that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks, and calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules.
- Deep Joint Rain Detection and Removal from a Single Image1,331
A recurrent rain detection and removal network that removes rain streaks and clears up the rain accumulation iteratively and progressively is proposed and a new contextualized dilated network is developed to exploit regional contextual information and to produce better representations for rain detection.
- Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm1,142
This paper considers the Tensor Robust Principal Component Analysis (TRPCA) problem, which aims to exactly recover the low-rank and sparse components from their sum, and proposes a model based on the recently proposed tensor-tensor product, which includes matrix RPCA as a special case.
- A Simple Pooling-Based Design for Real-Time Salient Object Detection1,100
This work solves the problem of salient object detection by investigating how to expand the role of pooling in convolutional neural networks by building a global guidance module (GGM) and designing a feature aggregation module (FAM) to make the coarse-level semantic information well fused with the fine-level features from the top-down path- way.
- Deep Long-Tailed Learning: A Survey1,013
This article groups existing deep long-tailed learning studies into three main categories (i.e., class re-balancing, information augmentation and module improvement), and examines several state-of-the-art methods following this taxonomy in detail.
- Perceptual Generative Adversarial Networks for Small Object Detection913
This work addresses the small object detection problem by developing a single architecture that internally lifts representations of small objects to super-resolved ones, achieving similar characteristics as large objects and thus more discriminative for detection.
- Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach881
This work investigates a principle way to progressively mine discriminative object regions using classification networks to address the weakly-supervised semantic segmentation problems and proposes a new adversarial erasing approach for localizing and expanding object regions progressively.
- Few-Shot Object Detection via Feature Reweighting879
This work develops a few-shot object detector that can learn to detect novel objects from only a few annotated examples, using a meta feature learner and a reweighting module within a one-stage detection architecture.
- Scale-aware Fast R-CNN for Pedestrian Detection843
This paper argues that the issue of large variance in instance scales, which results in undesirable large intracategory variance in features, may severely hurt the performance of modern object instance detection methods, can be substantially alleviated by the divide-and-conquer philosophy.
- Strip Pooling: Rethinking Spatial Pooling for Scene Parsing820
Beyond conventional spatial pooling that usually has a regular shape of NxN, a new pooling strategy is introduced, called strip pooling, which considers a long but narrow kernel, i.e., 1xN or Nx1.
- Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks With Octave Convolution735
This work proposes to factorize the mixed feature maps by their frequencies, and design a novel Octave Convolution (OctConv) operation to store and process feature maps that vary spatially “slower” at a lower spatial resolution reducing both memory and computation cost.
- 676
- Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation627
It is found that varying dilation rates can effectively enlarge the receptive fields of convolutional kernels and more importantly transfer the surrounding discriminative information to non-discriminative object regions, promoting the emergence of these regions in the object localization maps.
- Adversarial Complementary Learning for Weakly Supervised Object Localization626
This work mathematically proves that class localization maps can be obtained by directly selecting the class-specific feature maps of the last convolutional layer, which paves a simple way to identify object regions and presents a simple network architecture including two parallel-classifiers for object localization.
- STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation588
A simple to complex (STC) framework in which only image-level annotations are utilized to learn DCNNs for semantic segmentation, which demonstrates the superiority of the proposed STC framework compared with other state-of-the-arts frameworks.
- 586
- Natural Language Object Retrieval579
Experimental results demonstrate that the SCRC model effectively utilizes both local and global information, outperforming previous baseline methods significantly on different datasets and scenarios, and can exploit large scale vision and language datasets for knowledge transfer.
- Voxel Transformer for 3D Object Detection574
Voxel Transformer is presented, a novel and effective voxel-based Transformer backbone for 3D object detection from point clouds that shows consistent improvement over the convolutional baselines while maintaining computational efficiency on the KITTI dataset and the Waymo Open dataset.
- Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization541
This work proves that under certain suitable assumptions, it can recover both the low-rank and the sparse components exactly by simply solving a convex program whose objective is a weighted combination of the tensor nuclear norm and the l1-norm.
- Correlation Alignment for Unsupervised Domain Adaptation534
This chapter describes a solution that applies a linear transformation to source features to align them with target features before classifier training, and proposes to equivalently apply CORAL to the classifier weights, leading to added efficiency when the number of classifiers is small but the number and dimensionality of target examples are very high.
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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