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Claim this profileWorks259 from public data
- 2,465
- Ensembling neural networks: Many could be better than all2,019
The bias-variance decomposition of the error is provided in this paper, which shows that the success of GASEN may lie in that it can significantly reduce the bias as well as the variance.
- ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression1,895
ThiNet is proposed, an efficient and unified framework to simultaneously accelerate and compress CNN models in both training and inference stages, and it is revealed that it needs to prune filters based on statistics information computed from its next layer, not the current layer, which differentiates ThiNet from existing methods.
- 784
- ActionFormer: Localizing Moments of Actions with Transformers577
This work presents ActionFormer -- a simple yet powerful model to identify actions in time and recognize their categories in a single shot, without using action proposals or relying on pre-defined anchor windows.
- Deep Label Distribution Learning With Label Ambiguity511
The proposed deep label distribution learning (DLDL) method effectively utilizes the label ambiguity in both feature learning and classifier learning, which help prevent the network from overfitting even when the training set is small.
- Probabilistic End-To-End Noise Correction for Learning With Noisy Labels498
An end-to-end framework called PENCIL, which can update both network parameters and label estimations as label distributions and is more general and robust than existing methods and is easy to apply.
- Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval464
The selective convolutional descriptor aggregation (SCDA) method is proposed, which is unsupervised, using no image label or bounding box annotation, and achieves comparable retrieval results with the state-of-the-art general image retrieval approaches.
- 398
- Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization392
It is proved that selecting useful deep descriptors contributes well to fine-grained image recognition, and a novel Mask-CNN model without the fully connected layers is proposed, which has a small feature dimensionality and efficient inference speed by comparing with other fine- grained approaches.
- Minimal gated unit for recurrent neural networks375
This work proposes a gated unit for RNN, named as minimal gated units (MGU), since it only contains one gate, which is a minimal design among all gated hidden units.
- 277
- Face Detection274
This chapter begins with a review of recent work in modeling face-specific information, including appearance-based methods used by sliding window classifiers, concepts from learning and local interest-point descriptors, and then focuses on representing the contextual information shared by faces with the surrounding scene.
- 264
- AutoPruner: An end-to-end trainable filter pruning method for efficient deep model inference258
This paper proposes an efficient channel selection layer, namely AutoPruner, to find less important filters automatically in a joint training manner and empirically demonstrates that the gradient information of this channel selectionlayer is also helpful for the whole model training.
- Face recognition with one training image per person257
This paper generalize and further enhance (PC) 2 A along two directions, which combines the original image with its second-order projections as well as its first-order projection in order to acquire more information from the original face, and applies principal component analysis (PCA) to such a set of the combined images.
- 230
- Residual Attention: A Simple but Effective Method for Multi-Label Recognition206
This work proposes an embarrassingly simple module, named class-specific residual attention (CSRA), which generates class- specific features for every category by proposing a simple spatial attention score, and then combines it with the class-agnostic average pooling feature.
- Age Estimation Using Expectation of Label Distribution Learning195
A lightweight network architecture is designed and a unified framework which can jointly learn age distribution and regress age is proposed which can achieve comparable results as the state-of-the-art even though model parameters are further reduced to 0.9M~(3.8MB disk storage).
- 192
- Exploit Bounding Box Annotations for Multi-Label Object Recognition185
This paper first extracts object proposals from each image, then proposes to make use of ground-truth bounding box annotations (strong labels) to add another level of local information by using nearest-neighbor relationships of local regions to form a multi-view pipeline.
- PBN184
This work combines the sensing power of on-body wireless sensors with the additional sensing power, computational resources, and user-friendly interface of an Android smartphone, and provides an accurate and efficient classification approach through the use of ensemble learning.
- On the Design of Cascades of Boosted Ensembles for Face Detection180
A novel method is proposed for making decisions about how many hypotheses to include in an ensemble and the appropriate balance of detection and false positive rates in the individual stages, which exploits the shape of the stage ROC curves in ways that have been previously ignored.
- Person Re-Identification with Correspondence Structure Learning175
A boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair is introduced, which integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images.
- Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural Networks168
This paper collects existing tricks in long-tailed visual recognition and performs extensive and systematic experiments in order to give a detailed experimental guideline and obtain an effective combination of these tricks, and proposes a novel data augmentation approach based on class activation maps for long-tail recognition.
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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