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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.
- Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images1,529
An end-to-end deep learning architecture that produces a 3D shape in triangular mesh from a single color image by progressively deforming an ellipsoid, leveraging perceptual features extracted from the input image.
- Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks1,096
The proposed Soft Filter Pruning (SFP) method enables the pruned filters to be updated when training the model after pruning, which has two advantages over previous works: larger model capacity and less dependence on the pretrained model.
- Transductive Multi-View Zero-Shot Learning548
A novel heterogeneous multi-view hypergraph label propagation method is formulated for zero-shot learning in the transductive embedding space that rectifies the projection shift between the auxiliary and target domains, exploits the complementarity of multiple semantic representations, and significantly outperforms existing methods for both zero- shot and N-shot recognition.
- Pose-Normalized Image Generation for Person Re-identification483
This work proposes a novel deep person image generation model for synthesizing realistic person images conditional on the pose based on a generative adversarial network designed specifically for pose normalization in re-id, thus termed pose-normalization GAN (PN-GAN).
- Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking382
The two major novelties: chained structure and paired attentive regression, make CTracker simple, fast and effective, setting new MOTA records on MOT16 and MOT17 challenge datasets (67.6 and 66.6, respectively), without relying on any extra training data.
- Learning Salient Boundary Feature for Anchor-free Temporal Action Localization329
This paper proposes the first purely anchor-free temporal localization method, which is both efficient and effective, and beats all anchor-based and actionness-guided methods with a remarkable margin on THUMOS14, achieving state-of-the-art results, and comparable ones on ActivityNet v1.3.
- Multi-scale Deep Learning Architectures for Person Re-identification311
A novel multi-scale deep learning model is proposed that is able to learn deep discriminative feature representations at different scales and automatically determine the most suitable scales for matching.
- Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation291
This model learns to predict series of deformations to improve a coarse shape iteratively and exhibits generalization capability across different semantic categories, number of input images, and quality of mesh initialization.
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- Image Deformation Meta-Networks for One-Shot Learning255
This work combines a meta-learner with an image deformation sub-network that produces additional training examples, and optimize both models in an end-to-end manner to significantly outperform state-of-the-art approaches on widely used one-shot learning benchmarks.
- Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation251
This paper proposes a novel framework, transductive multi-view embedding, that rectifies the projection shift between the auxiliary and target domains, exploits the complementarity of multiple semantic representations, achieves state-of-the-art recognition results on image and video benchmark datasets, and enables novel cross-view annotation tasks.
- 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.
- Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding220
The proposed model restores holistic image structures with a powerful attention-based transformer model in a fixed low-resolution sketch space and can be integrated with other pretrained inpainting models efficiently with the zero-initialized residual addition.
- 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 Learning204
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.
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- Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks192
An asymptotic soft filter pruning (ASFP) method to accelerate the inference procedure of the deep neural networks and shows the effectiveness of the ASFP on image classification benchmarks.
- Recent Advances in Zero-Shot Recognition: Toward Data-Efficient Understanding of Visual Content186
This article provides a comprehensive review of existing zero-shot recognition techniques covering various aspects ranging from representations of models, data sets, and evaluation settings and highlights the limitations of existing approaches.
- Learning to Score Figure Skating Sport Videos181
A deep architecture that includes two complementary components, i.e., Self-Attentive L STM and Multi-scale Convolutional Skip LSTM can efficiently learn the local and global sequential information in each video.
- 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.
- When Person Re-identification Meets Changing Clothes167
It is found that changing clothes makes ReID a much harder problem in the sense of bringing difficulties to learning effective representations and also challenges the generalization ability of previous ReID models to identify persons with unseen (new) clothes.
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- DeepSFM: Structure from Motion via Deep Bundle Adjustment142
This work designs a physical driven architecture, namely DeepSFM, inspired by traditional Bundle Adjustment, which consists of two cost volume based architectures for depth and pose estimation respectively, iteratively running to improve both.
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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 2026-10-12. 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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