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- Selective-Supervised Contrastive Learning with Noisy Labels258
Experiments on multiple noisy datasets demonstrate the robustness of the learned representations by the proposed selective-supervised contrastive learning (Sel-CL), following the state-of-the-art performance.
- Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation210
This paper proposes a learning approach to recognize low-resolution faces via selective knowledge distillation by selectively distil the most informative facial features from the teacher stream by solving a sparse graph optimization problem, which is then used to regularize the fine-tuning process of the student stream.
- Bootstrapping Multi-View Representations for Fake News Detection149
A novel scheme of Bootstrapping Multi-view Representations (BMR) for fake news detection and improves Multi-gate Mixture-of-Expert networks (iMMoE) are proposed for feature refinement and fusion.
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- The Ninth Visual Object Tracking VOT2021 Challenge Results127
The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative; results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years.
- Student Network Learning via Evolutionary Knowledge Distillation114
Instead of a fixed pre-trained teacher, an evolutionary teacher is learned online and consistently transfers intermediate knowledge to supervise student network learning on-the-fly to improve the transfer effectiveness of teacher knowledge.
- Detecting Deepfake Videos with Temporal Dropout 3DCNN111
A Temporal Dropout 3-Dimensional Convolutional Neural Network (TD-3DCNN) is proposed to detect deepfake videos and can enhance the representation and generalization ability, avoiding model overfitting and improving detecting accuracy.
- Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning95
This work studies the identifiability problem of the class-dependent transition matrix in noisy multi-label learning, and inspired by the identifiability results, a new estimator is proposed by exploiting label correlations without neither anchor points nor accurate class posterior, leading to better classification performance.
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- M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy80
A novel DM-based method named M3D for dataset condensation is presented by Minimizing the Maximum Mean Discrepancy between feature representations of the synthetic and real images by embedding their distributions in a reproducing kernel Hilbert space, resulting in a more generalized condensed set.
- Accurate Temporal Action Proposal Generation with Relation-Aware Pyramid Network79
In RapNet, a novel relation-aware module is introduced to exploit bi-directional long-range relations between local features for context distilling and generates superior accurate proposals over the existing state-of-the-art methods.
- Efficient Low-Resolution Face Recognition via Bridge Distillation78
By learning low- resolution face representations and mimicking the adapted high-resolution knowledge, a light-weight student model can be constructed with high efficiency and promising accuracy in recognizing low-resolution faces.
- Look Through Masks: Towards Masked Face Recognition with De-Occlusion Distillation78
This work proposes to migrate the mechanism of amodal completion for the task of masked face recognition with an end-to-end de-occlusion distillation framework, which consists of two modules that applies a generative adversarial network to perform face completion.
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- Predicting Aesthetic Score Distribution Through Cumulative Jensen-Shannon Divergence74
A novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning methodbased on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human rating data (without normalization).
- WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking67
A fine-grained UAV tracking-under-scenario constraint (UTUSC) evaluation protocol and seven challenging scenario subtest sets are constructed to enable the community to develop, adapt and evaluate various types of advanced trackers.
- Deepfake Video Detection via Predictive Representation Learning64
A predictive representative learning approach termed Latent Pattern Sensing to capture semantic change characteristics for deepfake video detection, which can simultaneously describe the latent patterns of videos across frames spatially and temporally in a unified way, leading to an effective and robust deep fake video detector.
- ILGNet: inception modules with connected local and global features for efficient image aesthetic quality classification using domain adaptation62
A novel deep convolutional neural network named ILGNet is proposed, which combines both the inception modules and a connected layer of both local and global features in order to label an input image as high- or low-aesthetic quality.
- Deepfake Video Detection with Spatiotemporal Dropout Transformer60
This paper proposes a simple yet effective patch-level approach to facilitate deepfake video detection via spatiotemporal dropout transformer that reorganizes each input video into bag of patches that is then fed into a vision transformer to achieve robust representation.
- Aesthetic Attributes Assessment of Images55
The evaluation criteria used in image captions is used to prove that the specially designed AMAN model outperforms traditional CNN-LSTM model and modern SCA-CNN model of imageCaptions.
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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 2026-10-16. 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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