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Works57 from public data
- Recent advances in convolutional neural networks6,499
This paper details the improvements of CNN on different aspects, including layer design, activation function, loss function, regularization, optimization and fast computation, and introduces various applications of convolutional neural networks in computer vision, speech and natural language processing.
- Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification490
A novel end-to-end trainable framework, called Dual ATtention Matching network (DuATM), to learn context-aware feature sequences and perform attentive sequence comparison simultaneously, in which both intrasequence and inter-sequence attention strategies are used for feature refinement and feature-pair alignment.
- Recurrent Attentional Networks for Saliency Detection243
A recurrent attentional convolutional-deconvolution network (RACDNN) that is able to iteratively attend to selected image sub-regions to perform saliency refinement progressively and outperforms state-of-the-art saliency detection methods.
- Multimodal Contrastive Training for Visual Representation Learning229
This work develops an approach to learning visual representations that embraces multimodal data, driven by a combination of intra- and inter-modal similarity preservation objectives, and exploits intrinsic data properties within each modality and semantic information from cross- modal correlation simultaneously, hence improving the quality of learned visual representations.
- SelfDoc: Self-Supervised Document Representation Learning187
This work proposes SelfDoc, a task-agnostic pre-training framework for document image understanding that benefits from self-supervised pre- training on documents without requiring annotations by a feature masking training strategy, and proposes a novel modality-adaptive attention mechanism for multimodal feature fusion by adaptively emphasizing language and vision signals.
- Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling119
This work proposes a cross-modal pseudo-labeling framework, which generates training pseudo masks by aligning word semantics in captions with visual features of object masks in images and designs a robust student model that selectively distills mask knowledge by estimating the mask noise levels, hence mitigating the adverse impact of noisy pseudo masks.
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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.
- 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.
- 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.
- High Quality Segmentation for Ultra High-resolution Images63
The Continuous Refinement Model (CRM) continuously aligns the feature map with the refinement target and aggregates features to reconstruct these image details and shows its significant generalization ability to fill the resolution gap between low-resolution training images and ultra high-resolution testing ones.
- SceneComposer: Any-Level Semantic Image Synthesis61
Experimental results show that the proposed method can generate high-quality images following the layout at given precision, and compares favorably against existing methods.
- Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle44
This paper proposes to learn complex-valued invariant representations from tracked sequential image patches, via strong temporal slowness constraint and stacked convolutional autoencoders and integrated into a particle filter framework to perform visual tracking.
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- Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks29
This work proposes a novel approach for cost-adjustable inference in CNNs - Stochastic Downsampling Point (SDPoint), which is validated through extensive experiments on image classification.
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- Scaling Object Detection by Transferring Classification Weights19
This paper first introduces input and feature normalization schemes to curb the under-fitting during training of a vanilla WTN, and proposes autoencoder-WTN (AE- WTN) which uses reconstruction loss to preserve classification network's information over all classes in the target latent space to ensure generalization to novel classes.
- Self-supervised relationship probing18
A self-supervised method that implicitly learns the visual relationships without relying on any ground-truth visual relationship annotations is introduced, and the effectiveness of the proposed method on various vision-language tasks that benefit from improved visual relationship understanding is verified.
- DelugeNets: Deep Networks with Efficient and Flexible Cross-Layer Information Inflows17
Deluge Networks are deep neural networks which efficiently facilitate massive cross-layer information inflows from preceding layers to succeeding layers, and can propagate information across many layers with greater flexibility and utilize network parameters more effectively compared to ResNets, whilst being more efficient than DenseNets.
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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 2026-10-13. 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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