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- Deep High-Resolution Representation Learning for Visual Recognition5,145
The superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, is shown, suggesting that the HRNet is a stronger backbone for computer vision problems.
- Efficient Test-Time Model Adaptation without Forgetting740
An active sample selection criterion is proposed to identify reliable and non-redundant samples, on which the model is updated to minimize the entropy loss for test-time adaptation, and a Fisher regularizer is introduced to constrain important model parameters from drastic changes.
- Discrimination-aware channel pruning for deep neural networks642
This work investigates a simple-yet-effective method, called discrimination-aware channel pruning, to choose those channels that really contribute to discriminative power and proposes a greedy algorithm to conduct channel selection and parameter optimization in an iterative way.
- Towards Stable Test-Time Adaptation in Dynamic Wild World617
This paper proposes a sharpness-aware and reliable entropy minimization method, called SAR, for further stabilizing TTA from two aspects: 1) remove partial noisy samples with large gradients, 2) encourage model weights to go to a flat minimum so that the model is robust to the remaining noisy samples.
- Graph Convolutional Networks for Temporal Action Localization572
This paper builds an action proposal graph, where each proposal is represented as a node and their relations between two proposals as an edge and applies the GCNs over the graph to model the relations among different proposals and learn powerful representations for the action classification and localization.
- Domain-Symmetric Networks for Adversarial Domain Adaptation356
This paper proposes a new domain adaptation method called Domain-Symmetric Networks (SymNets), which is based on a symmetric design of source and target task classifiers, based on which an additional classifier is constructed that shares with them its layer neurons.
- Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution348
A dual regression scheme is proposed by introducing an additional constraint on LR data to reduce the space of the possible functions and it is shown that this scheme can easily adapt SR models to real-world data, e.g., raw video frames from YouTube.
- Dense Regression Network for Video Grounding346
A novel dense regression network (DRN) is designed to regress the distances between the frame within the ground truth and the starting (ending) frame of the video segment described by the query to improve the video grounding accuracy.
- Towards Effective Low-Bitwidth Convolutional Neural Networks266
This paper tackles the problem of training a deep convolutional neural network with both low-precision weights and low-bitwidth activations by proposing a two-stage optimization strategy to progressively find good local minima and adopting a novel learning scheme to jointly train a full- Precision model alongside the low-Precision one.
- Attention Guided Network for Retinal Image Segmentation226
An Attention Guided Network (AG-Net) is proposed to preserve the structural information and guide the expanding operation and an attention block is introduced to exclude the noise and reduce the negative influence of background further.
- 215
- Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation215
A new robust contrastive prototype adaptation strategy to align each pseudo-labeled target data to the corresponding source prototypes via contrastive learning is developed.
- 215
- Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis195
This work innovatively proposes a Collaborative Unsupervised Domain Adaptation algorithm, which conducts transferability-aware adaptation and conquers label noise in a collaborative way and theoretically analyze the generalization performance of the proposed method, and also empirically evaluate it on both medical and general images.
- Location-Aware Graph Convolutional Networks for Video Question Answering192
This work proposes to represent the contents in the video as a location-aware graph by incorporating the location information of an object into the graph construction, and proposes to use graph convolution to infer both the category and temporal locations of an action.
- Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets186
Comprehensive experimental results show that the proposed method can obtain better or competitive performance compared with existing SVM-based feature selection methods in term of sparsity and generalization performance, and can effectively handle large-scale and extremely high dimensional problems.
- Discrimination-aware Network Pruning for Deep Model Compression178
This paper proposes a simple-yet-effective method called discrimination-aware channel pruning (DCP) to choose the channels that actually contribute to the discriminative power of the network, and proposes a greedy algorithm to solve the resultant problem.
- 178
- Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks177
An end-to-end solution of Semantic Superpoint Tree Network (SSTNet) for proposing object instances from scene points, and design in SSTNet a refinement module, termed CliqueNet, to prune superpoints that may be wrongly grouped into instance proposals.
- Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation166
This paper proposes a ``network decomposition'' strategy, named Group-Net, in which each full-precision group can be effectively reconstructed by aggregating a set of homogeneous binary branches, and shows strong generalization to other tasks.
- Generative Low-Bitwidth Data Free Quantization161
This paper proposes a knowledge matching generator to produce meaningful fake data by exploiting classification boundary knowledge and distribution information in the pre-trained model with much higher accuracy on 4-bit quantization than the existing data free quantization method.
- Heterogeneous Domain Adaptation for Multiple Classes161
An efficient multi-class heterogeneous domain adaptation method, where data from source and target domains are represented by heterogeneous features of different dimensions, to reconstruct a sparse feature transformation matrix to map the weight vector of classifiers learned from the source domain to the target domain.
- Towards ultrahigh dimensional feature selection for big data161
A new adaptive feature scaling scheme for ultrahigh-dimensional feature selection on Big Data is presented, and then reformulate it as a convex semi-infinite programming (SIP) problem to address the SIP, and an efficient feature generating paradigm is proposed.
- T-C3D: Temporal Convolutional 3D Network for Real-Time Action Recognition145
A new real-time action recognition architecture, called Temporal Convolutional 3D Network (T-C3D), which learns video action representations in a hierarchical multi-granularity manner, demonstrating comparable recognition performance to the state-of-the-art methods.
- Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation144
A multi-granularity map, which contains both object fine-grained details and semantic classes, to represent objects more comprehensively, and a weakly-supervised auxiliary task, which requires the agent to localize instruction-relevant objects on the map.
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 2026-10-12. 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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