Search Results for author:
Found 47 papers, 16 papers with code
Single Domain Dynamic Generalization for Iris Presentation Attack Detection
To tackle the above issues, we propose a Single Domain Dynamic Generalization (SDDG) framework, which simultaneously exploits domain-invariant and domain-specific features on a per-sample basis and learns to generalize to various unseen domains with numerous natural images.
Rethinking the Approximation Error in 3D Surface Fitting for Point Cloud Normal Estimation
Most existing approaches for point cloud normal estimation aim to locally fit a geometric surface and calculate the normal from the fitted surface.
1st Place Solution for ECCV 2022 OOD-CV Challenge Image Classification Track
In the test-time training stage, we use the pre-trained model to assign noisy label for the unlabeled target data, and propose a Label-Periodically-Updated DivideMix method for noisy label learning.
1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection Track
The domain adaptation part is implemented as a Source-Free Domain Adaptation paradigm, which only uses the pre-trained model and the unlabeled target data to further optimize in a self-supervised training manner.
NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation
Gaze estimation is the fundamental basis for many visual tasks.
Attention Diversification for Domain Generalization
Under this guidance, a novel Attention Diversification framework is proposed, in which Intra-Model and Inter-Model Attention Diversification Regularization are collaborated to reassign appropriate attention to diverse task-related features.
Multi-Scale Wavelet Transformer for Face Forgery Detection
To overcome these limitations, we propose a multi-scale wavelet transformer framework for face forgery detection.
Point Cloud Upsampling via Cascaded Refinement Network
In this manner, the proposed cascaded refinement network can be easily optimized without extra learning strategies.
FBNet: Feedback Network for Point Cloud Completion
The rapid development of point cloud learning has driven point cloud completion into a new era.
Semi-supervised Ranking for Object Image Blur Assessment
Based on this dataset, we propose a method to obtain the blur scores only with the pairwise rank labels as supervision.
Slimmable Domain Adaptation
In this paper, we introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank, from which models of different capacities can be sampled to accommodate different accuracy-efficiency trade-offs.
Label Matching Semi-Supervised Object Detection
To remedy this issue, we present a novel label assignment mechanism for self-training framework, namely proposal self-assignment, which injects the proposals from student into teacher and generates accurate pseudo labels to match each proposal in the student model accordingly.
2nd Place Solution for ICCV 2021 VIPriors Image Classification Challenge: An Attract-and-Repulse Learning Approach
Therefore, it is crucial to study how to learn more discriminative representations while avoiding over-fitting.
Transductive CLIP with Class-Conditional Contrastive Learning
This framework can reduce the impact of noisy labels from CLIP model effectively by combining both techniques.
Learning Domain Adaptive Object Detection with Probabilistic Teacher
In addition, we conduct anchor adaptation in parallel with localization adaptation, since anchor can be regarded as a learnable parameter.
KRNet: Towards Efficient Knowledge Replay
However, the number of stored latent codes in autoencoder increases linearly with the scale of data and the trained encoder is redundant for the replaying stage.
Self-distilled Knowledge Delegator for Exemplar-free Class Incremental Learning
The knowledge extracted by the delegator is then utilized to maintain the performance of the model on old tasks in incremental learning.
Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks
In this work, we propose a new paradigm for FSCIL based on meta-learning by LearnIng Multi-phase Incremental Tasks (LIMIT), which synthesizes fake FSCIL tasks from the base dataset.
Ranked #4 on
Few-Shot Class-Incremental Learning
on CIFAR-100
class-incremental learning
Few-Shot Class-Incremental Learning
+2
Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action Recognition
In particular, we develop a novel cross-channel feature augmentation module, which is a combo of map-attend-group-map operations.
Hindsight Reward Tweaking via Conditional Deep Reinforcement Learning
Designing optimal reward functions has been desired but extremely difficult in reinforcement learning (RL).
TransForensics: Image Forgery Localization with Dense Self-Attention
Nowadays advanced image editing tools and technical skills produce tampered images more realistically, which can easily evade image forensic systems and make authenticity verification of images more difficult.
Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detection
Surprisingly, by varying the granularity of division on feature maps, we are able to modulate the reconstruction capability of the model for both normal and abnormal samples.
Modulating Localization and Classification for Harmonized Object Detection
Object detection involves two sub-tasks, i. e. localizing objects in an image and classifying them into various categories.
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation
Usually, the given source domain pre-trained model is expected to optimize with only unlabeled target data, which is termed as source-free unsupervised domain adaptation.
Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection
Therefore, it yields our attention to suppress false positive in each target domain in an unsupervised way.
A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data
However, it is difficult to evaluate the quality of pseudo labels since no labels are available in target domain.
MGD-GAN: Text-to-Pedestrian generation through Multi-Grained Discrimination
In this paper, we investigate the problem of text-to-pedestrian synthesis, which has many potential applications in art, design, and video surveillance.
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition
The key lies in the design of the graph structure, which encodes skeleton topology information.
Unsupervised Image Classification for Deep Representation Learning
Extensive experiments on ImageNet dataset have been conducted to prove the effectiveness of our method.
IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report
This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top $8$ finalists (out of over~$150$ teams).
Neural Inheritance Relation Guided One-Shot Layer Assignment Search
In this paper, for the first time, we systematically investigate the impact of different layer assignments to the network performance by building an architecture dataset of layer assignment on CIFAR-100.
An End-to-End Audio Classification System based on Raw Waveforms and Mix-Training Strategy
This paper introduces an end-to-end audio classification system based on raw waveforms and mix-training strategy.
Fast Task Adaptation for Few-Shot Learning
The key lies in generalization of prior knowledge learned from large-scale base classes and fast adaptation of the classifier to novel classes.
All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification
In this family of architectures, the basic block is only composed by 1x1 convolutional layers with only a few shift operations applied to the intermediate feature maps.
Collaborative Spatio-temporal Feature Learning for Video Action Recognition
By sharing the convolution kernels of different views, spatial and temporal features are collaboratively learned and thus benefit from each other.
Action Recognition In Videos
Temporal Action Localization
+1
A Layer Decomposition-Recomposition Framework for Neuron Pruning towards Accurate Lightweight Networks
A better alternative is to propagate the entire useful information to reconstruct the pruned layer instead of directly discarding the less important neurons.
Learning Incremental Triplet Margin for Person Re-identification
In particular, we propose a novel multi-stage training strategy which learns incremental triplet margin and improves triplet loss effectively.
Small-scale Pedestrian Detection Based on Topological Line Localization and Temporal Feature Aggregation
A critical issue in pedestrian detection is to detect small-scale objects that will introduce feeble contrast and motion blur in images and videos, which in our opinion should partially resort to deep-rooted annotation bias.
Attentive Sequence to Sequence Translation for Localizing Clips of Interest by Natural Language Descriptions
We validate the effectiveness of our ASST on two large-scale datasets.
Extreme Network Compression via Filter Group Approximation
In this paper we propose a novel decomposition method based on filter group approximation, which can significantly reduce the redundancy of deep convolutional neural networks (CNNs) while maintaining the majority of feature representation.
Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation
A critical issue in pedestrian detection is to detect small-scale objects that will introduce feeble contrast and motion blur in images and videos, which in our opinion should partially resort to deep-rooted annotation bias.
Ranked #17 on
Pedestrian Detection
on CityPersons
A practical convolutional neural network as loop filter for intra frame
It aims to design a single CNN model with low redundancy to adapt to decoded frames with different qualities and ensure consistency.
Multimedia
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation
Skeleton-based human action recognition has recently drawn increasing attentions with the availability of large-scale skeleton datasets.
Ranked #2 on
Skeleton Based Action Recognition
on PKU-MMD
Cascade Region Proposal and Global Context for Deep Object Detection
Deep region-based object detector consists of a region proposal step and a deep object recognition step.
Skeleton-based Action Recognition with Convolutional Neural Networks
Current state-of-the-art approaches to skeleton-based action recognition are mostly based on recurrent neural networks (RNN).
Ranked #3 on
Skeleton Based Action Recognition
on PKU-MMD
All You Need is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation
Moreover, we can successfully train plain CNNs to match the performance of the residual counterparts.
Mixed context networks for semantic segmentation
Semantic segmentation is challenging as it requires both object-level information and pixel-level accuracy.
