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Works280 from public data
- Densely Connected Convolutional Networks45,608
The Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion, and has several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
- Learning Efficient Convolutional Networks through Network Slimming2,909
The approach is called network slimming, which takes wide and large networks as input models, but during training insignificant channels are automatically identified and pruned afterwards, yielding thin and compact models with comparable accuracy.
- Deep Networks with Stochastic Depth2,786
Stochastic depth is proposed, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time and reduces training time substantially and improves the test error significantly on almost all data sets that were used for evaluation.
- Trends in extreme learning machines: A review1,732
This review gives an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability, and generalization ability, and focuses on the various improvements made to ELM which further improve its stability, sparsity and accuracy under general or specific conditions.
- Dynamic Neural Networks: A Survey1,012
This survey comprehensively review this rapidly developing area of dynamic networks by dividing dynamic networks into three main categories: sample-wise dynamic models that process each sample with data-dependent architectures or parameters; spatial-wiseynamic networks that conduct adaptive computation with respect to different spatial locations of image data; and temporal-wise Dynamic networks that perform adaptive inference along the temporal dimension for sequential data.
- Vision Transformer with Deformable Attention972
A novel deformable selfatt attention module is proposed, where the positions of key and value pairs in selfattention are selected in a data-dependent way, which enables the self-attention module to focus on relevant re-gions and capture more informative features.
- CondenseNet: An Efficient DenseNet Using Learned Group Convolutions881
CondenseNet is developed, a novel network architecture with unprecedented efficiency that combines dense connectivity with a novel module called learned group convolution, allowing for efficient computation in practice.
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- Convolutional Networks with Dense Connectivity590
The Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion, is introduced and achieves significant improvements over the state-of-the-art on most of them, whilst requiring less parameters and computation to achieve high performance.
- On the Integration of Self-Attention and Convolution551
A mixed model that enjoys the benefit of both self-Attention and Convolution (ACmix), while having minimum compu-tational overhead compared to the pure convolution or self-attention counterpart.
- Horizontal Pyramid Matching for Person Re-Identification512
A simple yet effective Horizontal Pyramid Matching (HPM) approach to fully exploit various partial information of a given person, so that correct person candidates can be still identified even even some key parts are missing.
- BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision490
A novel bird's-eye-view (BEV) detector with perspective supervision, which converges faster and bet-suits modern image backbones and achieves new SoTA results on the large-scale nuScenes dataset.
- 3D Object Detection with Pointformer472
This paper proposes Pointformer, a Transformer backbone designed for 3D point clouds to learn features effectively, and introduces an efficient coordinate refinement module to shift down-sampled points closer to object centroids, which improves object proposal generation.
- FLatten Transformer: Vision Transformer using Focused Linear Attention423
This paper proposes a novel Focused Linear Attention module, which introduces a simple yet effective mapping function and an efficient rank restoration module to enhance the expressiveness of self-attention while maintaining low computation complexity.
- Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study377
The ThyNet-assisted strategy can significantly improve the diagnostic performance of radiologists and help reduce unnecessary fine needle aspirations for thyroid nodules.
- Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory368
Ghost in the Minecraft (GITM), a novel framework that integrates Large Language Models (LLMs) with text-based knowledge and memory, aiming to create Generally Capable Agents (GCAs) in Minecraft, shows the potential of LLMs in developing capable agents for handling long-horizon, complex tasks and adapting to uncertainties in open-world environments.
- Frequency-Aware Feature Fusion for Dense Image Prediction325
Comprehensive visualization and quantitative analysis demonstrate that FreqFusion effectively improves feature consistency and sharpens object boundaries, and extensive experiments across various dense prediction tasks confirm its effectiveness.
- Domain Adaptation via Prompt Learning289
This article introduces a novel prompt learning paradigm for UDA, named domain adaptation via prompt learning (DAPrompt), which outperforms previous methods on several cross-domain benchmarks but also is very efficient to train and easy to implement.
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- Resource Aware Person Re-identification Across Multiple Resolutions266
This work presents a new person re-ID model that combines effective embeddings built on multiple convolutional network layers, trained with deep-supervision, and proposes two new formulations of the person reID problem under resource-constraints.
- Resolution Adaptive Networks for Efficient Inference256
This paper focuses on spatial redundancy of input samples and proposes a novel Resolution Adaptive Network (RANet), inspired by the intuition that low-resolution representations are sufficient for classifying “easy” inputs containing large objects with prototypical features, while only some “hard” samples need spatially detailed information.
- How Far is Video Generation from World Model: A Physical Law Perspective244
This study suggests that scaling alone is insufficient for video generation models to uncover fundamental physical laws, despite its role in Sora's broader success.
- SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation224
Semantic-Guided Pixel Contrast (SePiCo), a novel one-stage adaptation framework that highlights the semantic concepts of individual pixels to promote learning of class-discriminative and class-balanced pixel representations across domains, eventually boosting the performance of self-training methods.
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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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