Search Results for author:
Found 24 papers, 10 papers with code
Employing Multi-Estimations for Weakly-Supervised Semantic Segmentation
Instead of struggling to refine a single seed, we propose a novel approach to alleviate the inaccurate seed problem by leveraging the segmentation model's robustness to learn from multiple seeds.
Generalizing Person Re-Identification by Camera-Aware Invariance Learning and Cross-Domain Mixup
Despite the impressive performance under the single-domain setup, current fully-supervised models for person re-identification (re-ID) degrade significantly when deployed to an unseen domain.
Boosting Decision-based Black-box Adversarial Attacks with Random Sign Flip
Decision-based black-box adversarial attacks (decision-based attack) pose a severe threat to current deep neural networks, as they only need the predicted label of the target model to craft adversarial examples.
Bottom-Up Human Pose Estimation by Ranking Heatmap-Guided Adaptive Keypoint Estimates
The typical bottom-up human pose estimation framework includes two stages, keypoint detection and grouping.
Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels
Training with more data has always been the most stable and effective way of improving performance in deep learning era.
Cascading Convolutional Color Constancy
Regressing the illumination of a scene from the representations of object appearances is popularly adopted in computational color constancy.
Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection
Instead of searching and constructing an entire network, NATS explores the architecture space on the base of existing network and reusing its weights.
Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization
To predict the existence of a particular attribute, it is demanded to localize the regions related to the attribute.
Ranked #1 on
Pedestrian Attribute Recognition
on RAP
POD: Practical Object Detection with Scale-Sensitive Network
Scale-sensitive object detection remains a challenging task, where most of the existing methods could not learn it explicitly and are not robust to scale variance.
Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection
With the combination of these two designs, an architecture transformation scheme could be discovered to adapt a network designed for image classification to task of object detection.
Revisiting Feature Alignment for One-stage Object Detection
Recently, one-stage object detectors gain much attention due to their simplicity in practice.
Sequence Level Semantics Aggregation for Video Object Detection
In this work, we argue that aggregating features in the full-sequence level will lead to more discriminative and robust features for video object detection.
Ranked #17 on
Video Object Detection
on ImageNet VID
SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition
A Simple and Versatile Framework for Object Detection and Instance Recognition
Scale-Aware Trident Networks for Object Detection
In this work, we first present a controlled experiment to investigate the effect of receptive fields for scale variation in object detection.
Ranked #102 on
Object Detection
on COCO test-dev
Spectral Feature Transformation for Person Re-identification
With the surge of deep learning techniques, the field of person re-identification has witnessed rapid progress in recent years.
CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation with only image-level labels saves large human effort to annotate pixel-level labels.
Accelerating Deep Neural Networks with Spatial Bottleneck Modules
This paper presents an efficient module named spatial bottleneck for accelerating the convolutional layers in deep neural networks.
Hard-Aware Point-to-Set Deep Metric for Person Re-identification
Person re-identification (re-ID) is a highly challenging task due to large variations of pose, viewpoint, illumination, and occlusion.
Relational Network for Skeleton-Based Action Recognition
We introduce the Recurrent Relational Network to learn the spatial features in a single skeleton, followed by a multi-layer LSTM to learn the temporal features in the skeleton sequences.
Ranked #99 on
Skeleton Based Action Recognition
on NTU RGB+D
Multi-pseudo Regularized Label for Generated Data in Person Re-Identification
Unlike the traditional label which usually is a single integral number, the virtual label proposed in this work is a set of weight-based values each individual of which is a number in (0, 1] called multi-pseudo label and reflects the degree of relation between each generated data to every pre-defined class of real data.
Integrating both Visual and Audio Cues for Enhanced Video Caption
Furthermore, we first propose a dynamic multimodal feature fusion framework to deal with the part modalities missing case.
CMCGAN: A Uniform Framework for Cross-Modal Visual-Audio Mutual Generation
By recovering the missing modality from the existing one based on the common information shared between them and the prior information of the specific modality, great bonus will be gained for various vision tasks.
Rethink ReLU to Training Better CNNs
Most of convolutional neural networks share the same characteristic: each convolutional layer is followed by a nonlinear activation layer where Rectified Linear Unit (ReLU) is the most widely used.
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer
have shown that the dark knowledge within a powerful teacher model can significantly help the training of a smaller and faster student network.
