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Works440 from public data
- BERT-ATTACK: Adversarial Attack Against BERT Using BERT936
This paper proposes a high-quality and effective method to generate adversarial samples using pre-trained masked language models exemplified by BERT against its fine-tuned models and other deep neural models for downstream tasks and successfully misleads the target models to predict incorrectly.
- DSOD: Learning Deeply Supervised Object Detectors from Scratch646
Deeply Supervised Object Detector (DSOD), a framework that can learn object detectors from scratch following the single-shot detection (SSD) framework, and one of the key findings is that deep supervision, enabled by dense layer-wise connections, plays a critical role in learning a good detector.
- Towards 3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach618
A weakly-supervised transfer learning method that uses mixed 2D and 3D labels in a unified deep neutral network that presents two-stage cascaded structure to regularize the 3D pose prediction, which is effective in the absence of ground truth depth labels.
- ExFuse: Enhancing Feature Fusion for Semantic Segmentation572
It is found that introducing semantic information into low-level features and high-resolution details into high-level features is more effective for the later fusion, and a new framework, named ExFuse, is proposed to bridge the gap between low-level and high-level features.
- Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification479
This work proposes a hybrid deep learning framework for video classification, which is able to model static spatial information, short-term motion, as well as long-term temporal clues in the videos, and achieves very competitive performance on two popular and challenging benchmarks.
- Pose-Normalized Image Generation for Person Re-identification476
This work proposes a novel deep person image generation model for synthesizing realistic person images conditional on the pose based on a generative adversarial network designed specifically for pose normalization in re-id, thus termed pose-normalization GAN (PN-GAN).
- Metaverse: Perspectives from graphics, interactions and visualization396
This paper proposes a framework that summarizes how graphics, interaction, and visualization techniques support the visual construction of the metaverse and user-centric exploration and proposes a taxonomy of interaction technologies based on interaction tasks, user actions, feedback and various sensory channels.
- Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks385
Through arming the DNN with better capability of harnessing both the feature and the class relationships, the proposed regularized DNN (rDNN) is more suitable for modeling video semantics.
- Transfer learning for collaborative filtering via a rating-matrix generative model385
The proposed rating-matrix generative model (RMGM) can share the knowledge by pooling the rating data from multiple tasks even when the users and items of these tasks do not overlap, and can outperform the individual models trained separately.
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- Multi-scale Deep Learning Architectures for Person Re-identification319
A novel multi-scale deep learning model is proposed that is able to learn deep discriminative feature representations at different scales and automatically determine the most suitable scales for matching.
- Star-Transformer296
Star-Transformer replaces the fully-connected structure with a star-shaped topology, in which every two non-adjacent nodes are connected through a shared relay node, and complexity is reduced from quadratic to linear, while preserving the capacity to capture both local composition and long-range dependency.
- Long-Term Cloth-Changing Person Re-identification248
This work introduces a shape embedding module as well as a cloth-elimination shape-distillation module aiming to eliminate the now unreliable clothing appearance features and focus on the body shape information.
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- Trajectory-Based Modeling of Human Actions with Motion Reference Points224
This paper proposes a simple representation specifically aimed at the modeling of human action recognition in videos that operates on top of visual codewords derived from local patch trajectories, and therefore does not require accurate foreground-background separation, which is typically a necessary step to model object relationships.
- Multi-Stream Multi-Class Fusion of Deep Networks for Video Classification188
A multi-stream framework that is able to exploit multimodal features that are more comprehensive than those previously attempted and produces significantly better results than the state of the arts on two popular benchmarks, 92.2% on UCF-101 (without using audio) and 84.9% on Columbia Consumer Videos.
- Addressing cold start in recommender systems: a semi-supervised co-training algorithm188
This paper uses a factorization model to capture fine-grained user-item context and proposes a semi-supervised ensemble learning algorithm that constructs different (weak) prediction models using examples with different contexts and employs the co-training strategy to allow each ( weak) prediction model to learn from the other prediction models.
- Recent Advances in Zero-Shot Recognition: Toward Data-Efficient Understanding of Visual Content186
This article provides a comprehensive review of existing zero-shot recognition techniques covering various aspects ranging from representations of models, data sets, and evaluation settings and highlights the limitations of existing approaches.
- Learning to Score Figure Skating Sport Videos181
A deep architecture that includes two complementary components, i.e., Self-Attentive L STM and Multi-scale Convolutional Skip LSTM can efficiently learn the local and global sequential information in each video.
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- Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection170
A depth-conditioned dynamic message propagation (DDMP) network to effectively integrate the multi-scale depth information with the image context and dynamically predicting hybrid depth-dependent filter weights and affinity matrices for propagating information is proposed.
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- MEAL: Multi-Model Ensemble via Adversarial Learning157
This paper proposes an adversarial-based learning strategy where a block-wise training loss is defined to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously.
- Exploring Inter-feature and Inter-class Relationships with Deep Neural Networks for Video Classification154
A novel unified framework that jointly learns feature relationships and exploits the class relationships for improved video classification performance is proposed and demonstrates that the proposed framework exhibits superior performance over several state-of-the-art approaches.
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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