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Works151 from public data
- Deformable Convolutional Networks7,148
This work introduces two new modules to enhance the transformation modeling capability of CNNs, namely, deformable convolution and deformable RoI pooling, based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from the target tasks, without additional supervision.
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- Face Liveness Detection from a Single Image with Sparse Low Rank Bilinear Discriminative Model586
This paper develops two new extensions to the sparse logistic regression model which allow quick and accurate spoof detection and proposes two strategies to extract the essential information about different surface properties of a live human face or a photograph, in terms of latent samples.
- RESOUND: Towards Action Recognition Without Representation Bias429
Experimental evaluation confirms the effectiveness of RESOUND to reduce the static biases of current datasets.
- Instance-Sensitive Fully Convolutional Networks413
This paper develops FCNs that are capable of proposing instance-level segment candidates that do not have any high-dimensional layer related to the mask resolution, but instead exploits image local coherence for estimating instances.
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- Simultaneous estimation of image quality and distortion via multi-task convolutional neural networks172
- Beyond Physical Connections: Tree Models in Human Pose Estimation157
This paper surprisingly finds that no latent variables are introduced in the Leeds Sport Dataset (LSP) during learning latent trees for deformable model, which aims at approximating the joint distributions of body part locations using minimal tree structure.
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- GRAPE: Generalizing Robot Policy via Preference Alignment128
GRAPE enhances the performance of state-of-the-art VLA models, increasing success rates on in-domain and unseen manipulation tasks by 51.79% and 58.20%, respectively, and can be aligned with various objectives, such as safety and efficiency.
- Free-hand sketch recognition by multi-kernel feature learning103
This work proposes a Multiple Kernel Learning (MKL) framework for sketch recognition, fusing several features common to sketches, and investigates the use of attributes as a high-level feature for sketches and shows how this complements low-level features for improving recognition performance under the MKL framework.
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- A Survey of Deep Facial Attribute Analysis72
A general pipeline that deep facial attribute analysis follows, which comprises two stages: data preprocessing and model construction is summarized and the underlying theories of this two-stage pipeline for both FAE and FAM are introduced.
- A New Algorithm for Detecting Text Line in Handwritten Documents70
This work model text line detection as an image segmentation problem by enhancing text line structure using a Gaussian window, and adopting the level set method to evolve text line boundaries.
- Arbitrary Talking Face Generation via Attentional Audio-Visual Coherence Learning65
A novel arbitrary talking face generation framework is proposed by discovering the audio-visual coherence via the proposed Asymmetric Mutual Information Estimator (AMIE) and a Dynamic Attention (DA) block by selectively focusing the lip area of the input image during the training stage, to further enhance lip synchronization.
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- VALHALLA: Visual Hallucination for Machine Translation59
A visual hallucination framework, called VALHALLA, which requires only source sentences at inference time and instead uses hallucinated visual representations for multi-modal machine translation, and demonstrates the effectiveness over both text-only baselines and state-of-the-art methods.
- Heterogeneous Attention Network for Effective and Efficient Cross-modal Retrieval57
This work proposes a heterogeneous attention network (HAN) for effective and efficient cross-modal retrieval, and proposes a new soft-max triplet loss, which adaptively gives more attention to harder negatives and thus trains the proposed HAN in a more effective manner compared with the original doublet loss.
- Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification52
This paper proposes a learning from generation approach for makeup-invariant face verification by introducing a bi-level adversarial network (BLAN), and achieves state-of-the-art verification accuracy across makeup status and can produce photo-realistic non-makeup face images.
- A convolutional neural networks denoising approach for salt and pepper noise51
Experimental results show that NLSF-CNN outperforms the state-of-the-art denoising algorithms with a few training images.
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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 2026-10-15. 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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