Search Results for author:
Found 24 papers, 17 papers with code
Automatic Script Identification in the Wild
With the rapid increase of transnational communication and cooperation, people frequently encounter multilingual scenarios in various situations.
Adversarial Attribute-Image Person Re-identification
While attributes have been widely used for person re-identification (Re-ID) which aims at matching the same person images across disjoint camera views, they are used either as extra features or for performing multi-task learning to assist the image-image matching task.
Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training
Most existing Re-IDentification (Re-ID) methods are highly dependent on precise bounding boxes that enable images to be aligned with each other.
Ranked #2 on
Person Re-Identification
on CUHK03-C
Unsupervised Person Re-identification by Soft Multilabel Learning
To overcome this problem, we propose a deep model for the soft multilabel learning for unsupervised RE-ID.
Ranked #80 on
Person Re-Identification
on DukeMTMC-reID
Semi-Supervised Adversarial Monocular Depth Estimation
In this paper, we address the problem of monocular depth estimation when only a limited number of training image-depth pairs are available.
Rethinking Temporal Fusion for Video-based Person Re-identification on Semantic and Time Aspect
Recently, the research interest of person re-identification (ReID) has gradually turned to video-based methods, which acquire a person representation by aggregating frame features of an entire video.
Asymmetric Co-Teaching for Unsupervised Cross Domain Person Re-Identification
This procedure encourages that the selected training samples can be both clean and miscellaneous, and that the two models can promote each other iteratively.
Ranked #9 on
Unsupervised Domain Adaptation
on Market to Duke
Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification
Instead of one subspace for each viewpoint, our method projects the feature from different viewpoints into a unified hypersphere and effectively models the feature distribution on both the identity-level and the viewpoint-level.
Ranked #5 on
Person Re-Identification
on Market-1501
(using extra training data)
Dynamic Refinement Network for Oriented and Densely Packed Object Detection
However, the detection of oriented and densely packed objects remains challenging because of following inherent reasons: (1) receptive fields of neurons are all axis-aligned and of the same shape, whereas objects are usually of diverse shapes and align along various directions; (2) detection models are typically trained with generic knowledge and may not generalize well to handle specific objects at test time; (3) the limited dataset hinders the development on this task.
Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle
This paper proposes an adaptive energy management strategy for hybrid electric vehicles by combining deep reinforcement learning (DRL) and transfer learning (TL).
NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination
Greedy-NMS inherently raises a dilemma, where a lower NMS threshold will potentially lead to a lower recall rate and a higher threshold introduces more false positives.
Ranked #12 on
Object Detection
on CrowdHuman (full body)
Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians
In the conventional person Re-ID setting, it is widely assumed that cropped person images are for each individual.
Devil's in the Details: Aligning Visual Clues for Conditional Embedding in Person Re-Identification
Secondly, the Conditional Feature Embedding requires the overall feature of a query image to be dynamically adjusted based on the gallery image it matches, while most of the existing methods ignore the reference images.
Ranked #1 on
Person Re-Identification
on CUHK03-C
Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion
Specifically, we construct a positive clip and a negative clip for each video.
Pruning Filter in Filter
Through extensive experiments, we demonstrate that SWP is more effective compared to the previous FP-based methods and achieves the state-of-art pruning ratio on CIFAR-10 and ImageNet datasets without obvious accuracy drop.
One for More: Selecting Generalizable Samples for Generalizable ReID Model
Current training objectives of existing person Re-IDentification (ReID) models only ensure that the loss of the model decreases on selected training batch, with no regards to the performance on samples outside the batch.
Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search
Secondly, a BERT with locality-constrained attention is proposed to obtain representations of descriptions at different scales.
Ranked #15 on
Text based Person Retrieval
on CUHK-PEDES
Ask&Confirm: Active Detail Enriching for Cross-Modal Retrieval with Partial Query
However, the performance of existing methods suffers in real life since the user is likely to provide an incomplete description of an image, which often leads to results filled with false positives that fit the incomplete description.
Discriminator-Free Generative Adversarial Attack
The Deep Neural Networks are vulnerable toadversarial exam-ples(Figure 1), making the DNNs-based systems collapsed byadding the inconspicuous perturbations to the images.
Distributed Attention for Grounded Image Captioning
We study the problem of weakly supervised grounded image captioning.
Transformer-based Dual Relation Graph for Multi-label Image Recognition
Different from these researches, in this paper, we propose a novel Transformer-based Dual Relation learning framework, constructing complementary relationships by exploring two aspects of correlation, i. e., structural relation graph and semantic relation graph.
Ranked #8 on
Multi-Label Classification
on PASCAL VOC 2007
Video Moment Retrieval from Text Queries via Single Frame Annotation
Weakly supervised methods only rely on the paired video and query, but the performance is relatively poor.
Locate before Answering: Answer Guided Question Localization for Video Question Answering
Considering the fact that the question often remains concentrated in a short temporal range, we propose to first locate the question to a segment in the video and then infer the answer using the located segment only.
Revolutionizing Agrifood Systems with Artificial Intelligence: A Survey
With the world population rapidly increasing, transforming our agrifood systems to be more productive, efficient, safe, and sustainable is crucial to mitigate potential food shortages.
