Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks68 from public data
- Deep feature learning with relative distance comparison for person re-identification687
A scalable distance driven feature learning framework based on the deep neural network for person re-identification that achieves very promising results and outperforms other state-of-the-art approaches.
- EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning206
A pruning method called EagleEye is presented, in which a simple yet efficient evaluation component based on adaptive batch normalization is applied to unveil a strong correlation between different pruned DNN structures and their final settled accuracy.
- Block-Wisely Supervised Neural Architecture Search With Knowledge Distillation201
This work proposes to modularize the large search space of NAS into blocks to ensure that the potential candidate architectures are fully trained, and distill the neural architecture (DNA) knowledge from a teacher model to supervise the block-wise architecture search, which significantly improves the effectiveness of NAS.
- Dynamic Slimmable Network195
A dynamic network slimming regime, named Dynamic Slimmable Network (DS-Net), which aims to achieve good hardware-efficiency via dynamically adjusting filter numbers of networks at test time with respect to different inputs, while keeping filters stored statically and contiguously in hardware to prevent the extra burden.
- Distance metric optimization driven convolutional neural network for age invariant face recognition192
A novel distance metric optimization driven learning approach that integrates these traditional steps via a deep convolutional neural network, which learns feature representations and the decision function in an end-to-end way, and can be optimized simultaneously by backward propagation.
- 147
- BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search128
This work presents Block-wisely Self-supervised Neural Architecture Search (BossNAS), an unsupervised NAS method that addresses the problem of in-accurate architecture rating caused by large weight-sharing space and biased supervision in previous methods
- Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking96
This work examines the insecurity of current best-performing ReID models by proposing a learning-to-mis-rank formulation to perturb the ranking of the system output and performs a back-box attack by developing a novel multi-stage network architecture that pyramids the features of different levels to extract general and transferable features for the adversarial perturbations.
- 96
- LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields76
The extensive experiments on the scene-level KITTI-360 dataset, and on the object-level NeRF-MVL show that the LiDAR-NeRF surpasses the model-based algorithms significantly, and a structural regularization method to preserve local structural details is introduced.
- 70
- 69
- Adaptively Connected Neural Networks69
This paper presents a novel adaptively connected neural network (ACNet) to improve the traditional convolutional neural networks (CNNs) {in} two aspects: first, ACNet employs a flexible way to switch global and local inference in processing the internal feature representations by adaptively determining the connection status among the feature nodes.
- Solving Inefficiency of Self-supervised Representation Learning68
This paper proposes a novel self-supervised learning framework using a truncated triplet loss that demonstrates the model’s superiority over the latest state-of-the-art methods by a clear margin.
- ViewCo: Discovering Text-Supervised Segmentation Masks via Multi-View Semantic Consistency62
The text-to-views consistency modeling benefits the dense assignment of the visual features by encouraging different crops to align with the same text, while the cross-view segmentation consistency modeling provides additional self-supervision, overcoming the limitation of ambiguous text supervision for segmentation masks.
- Automated Progressive Learning for Efficient Training of Vision Transformers56
This paper proposes automated progressive learning (AutoProg), an efficient training scheme that aims to achieve lossless acceleration by automatically increasing the training overload on-the-fly; this is achieved by adaptively deciding whether, where and how much should the model grow during progressive learning.
- Beyond Fixation: Dynamic Window Visual Transformer51
The dynamic window strategy proposed by DW- ViT goes beyond the model that employs a fixed single window setting and is the first to use dynamic multi-scale windows to explore the upper limit of the effect of window settings on model performance.
- 50
- Reward-Adaptive Reinforcement Learning: Dynamic Policy Gradient Optimization for Bipedal Locomotion48
This work proposes a novel reward-adaptive reinforcement learning method for biped locomotion, allowing the control policy to be simultaneously optimized by multiple criteria using a dynamic mechanism, leading to hybrid policy gradients.
- 45
- 45
- DARI: Distance Metric and Representation Integration for Person Verification37
This work proposes an end-to-end learning framework called DARI, i.e. Distance metric And Representation Integration, and validates the effectiveness of DARI in the challenging task of person verification.
- Tem-adapter: Adapting Image-Text Pretraining for Video Question Answer34
The proposed Tem-adapter enables the learning of temporal dynamics and complex semantics by a visual Temporal Aligner and a textual Semantic Aligner that first designs a template to fuse question and answer pairs as event descriptions and then learns a Transformer decoder with the whole video sequence as guidance for refinement.
- Deep Structured Scene Parsing by Learning with Image Descriptions33
This paper proposes a deep architecture consisting of a convolutional neural network extracting the image representation for pixelwise object labeling and a recursive neural network discovering the hierarchical object structure and the inter-object relations and decomposes each sentence into a semantic tree.
- 29
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.