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Claim this profileWorks172 from public data
- Deep Domain Generalization via Conditional Invariant Adversarial Networks848
This work proposes an end-to-end conditional invariant deep domain generalization approach by leveraging deep neural networks for domain-invariant representation learning and proves the effectiveness of the proposed method.
- The Sixth Visual Object Tracking VOT2018 Challenge Results804
The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
- A Twofold Siamese Network for Real-Time Object Tracking672
The proposed SA-Siam outperforms all other real-time trackers by a large margin on OTB-2013/50/100 benchmarks and proposes a channel attention mechanism for the semantic branch.
- A Multi-Organ Nucleus Segmentation Challenge558
Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics as well as heavy data augmentation in the MoNuSeg 2018 challenge.
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- Quantization Networks432
This paper provides a simple and uniform way for weights and activations quantization by formulating it as a differentiable non-linear function that will shed new lights on the interpretation of neural network quantization.
- Gaussian Temporal Awareness Networks for Action Localization369
Gaussian Temporal Awareness Networks (GTAN) is presented --- a new architecture that novelly integrates the exploitation of temporal structure into an one-stage action localization framework and achieves superior results when comparing to state-of-the-art approaches.
- Prompt Distribution Learning367
This work presents prompt distribution learning for effectively adapting a pre-trained vision-language model to address downstream recognition tasks and employs a Gaussian distribution to model them effectively and derive a surrogate loss for efficient training.
- Exploring Object Relation in Mean Teacher for Cross-Domain Detection355
This work presents Mean Teacher with Object Relations (MTOR) that novelly remolds Mean Teacher under the backbone of Faster R-CNN by integrating the object relations into the measure of consistency cost between teacher and student modules.
- Domain Generalization via Conditional Invariant Representations312
This paper proposes to learn a feature representation which has domain-invariant class conditional distributions P(h(X)|Y), and proposes a conditional invariant representation which can be guaranteed if the class prior P(Y) does not change across training and test domains.
- Graph Edge Convolutional Neural Networks for Skeleton-Based Action Recognition205
The dynamics of human limbs for skeleton-based action recognition is investigated, and an edge in a graph of a human skeleton is represented by integrating its spatial neighboring edges and its temporal neighboring edges for achieving the consistency of movements in an action.
- Learning Spatio-Temporal Representation With Local and Global Diffusion198
A novel framework to boost the spatio-temporal representation learning by Local and Global Diffusion (LGD) is presented, which construct a novel neural network architecture that learns the local and global representations in parallel.
- DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training186
This work proposes a novel personalized federated learning framework in a decentralized (peer-to-peer) communication protocol named Dis-PFL, which employs personalized sparse masks to customize sparse local models on the edge to save the communication and computation cost.
- Camera Lens Super-Resolution170
This paper investigates SR from the perspective of camera lenses, named as CameraSR, which aims to alleviate the intrinsic tradeoff between resolution (R) and field-of-view (V) in realistic imaging systems and quantitatively analyzes the performance of commonly-used synthetic degradation models.
- Bayesian video search reranking161
Two new methods are proposed in this paper to measure the ranking distance based on the disagreement in terms of pair-wise orders, which represents the disagreement between the objective ranking list and the initial text-based.
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- Sparse transfer learning for interactive video search reranking119
This article proposes a novel dimension reduction tool, termed sparse transfer learning (STL), to effectively and efficiently encode user's labeling information and conducts extensive experiments on the TRECVID 2005, 2006 and 2007 benchmark datasets and compared STL with popular dimension reduction algorithms.
- DigestPath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system117
Two well-annotated benchmark datasets and organized challenges for the digestive-system pathological cell detection and tissue segmentation are released, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI).
- Deep Boosting for Image Denoising110
A novel deep boosting framework (DBF) for denoising, which integrates several convolutional networks in a feed-forward fashion, and introduces the concept of dense connection that overcomes the vanishing of gradients during training.
- QAOA-in-QAOA: Solving Large-Scale MaxCut Problems on Small Quantum Machines103
This work revisits the MaxCut problem via the divide-and-conquer heuristic and proposes QAOA-in-QAOA, a method to solve arbitrary large-scale MaxCut problems using small quantum machines that can be seamlessly embedded into other advanced strategies to enhance the capability of QAOAs in large- scale combinatorial optimization problems.
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- On Efficient Training of Large-Scale Deep Learning Models102
This survey presents a comprehensive review of the general techniques for training acceleration and provides a detailed analysis and discussion of future works on the development of general acceleration techniques, which could inspire us to re-think and design novel efficient paradigms.
- Mocycle-GAN101
A new Motion-guided Cycle GAN, dubbed as Mocycle-GAN, that novelly integrates motion estimation into unpaired video translator that capitalizes on three types of constrains: adversarial constraint discriminating between synthetic and real frame, cycle consistency encouraging an inverse translation on both frame and motion, and motion translation validating the transfer of motion between consecutive frames.
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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