Search Results for author:
Found 36 papers, 22 papers with code
Hybrid Relation Guided Set Matching for Few-shot Action Recognition
To overcome the two limitations, we propose a novel Hybrid Relation guided Set Matching (HyRSM) approach that incorporates two key components: hybrid relation module and set matching metric.
Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency
In this work, we aim to learn representations by leveraging more abundant information in untrimmed videos.
Style Transformer for Image Inversion and Editing
Existing GAN inversion methods fail to provide latent codes for reliable reconstruction and flexible editing simultaneously.
Multi-Centroid Representation Network for Domain Adaptive Person Re-ID
First, we present a Domain-Specific Contrastive Learning (DSCL) mechanism to fully explore intradomain information by comparing samples only from the same domain.
Contrastive Learning
Domain Adaptive Person Re-Identification
+1
Modality-Aware Triplet Hard Mining for Zero-shot Sketch-Based Image Retrieval
By combining two fundamental learning approaches in DML, e. g., classification training and pairwise training, we set up a strong baseline for ZS-SBIR.
Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior
We introduce a Noise Disentanglement Module (NDM) to disentangle the noise and content in the reflectance maps with the reliable aid of unpaired clean images.
Ranked #1 on
Low-Light Image Enhancement
on DICM
(NIQE metric)
Attribute-specific Control Units in StyleGAN for Fine-grained Image Manipulation
Image manipulation with StyleGAN has been an increasing concern in recent years. Recent works have achieved tremendous success in analyzing several semantic latent spaces to edit the attributes of the generated images. However, due to the limited semantic and spatial manipulation precision in these latent spaces, the existing endeavors are defeated in fine-grained StyleGAN image manipulation, i. e., local attribute translation. To address this issue, we discover attribute-specific control units, which consist of multiple channels of feature maps and modulation styles.
CondNet: Conditional Classifier for Scene Segmentation
The last layer of FCN is typically a global classifier (1x1 convolution) to recognize each pixel to a semantic label.
Ranked #12 on
Semantic Segmentation
on PASCAL Context
Weakly Supervised Person Search with Region Siamese Networks
Large-scale labeled training data is often difficult to collect, especially for person identities.
ParamCrop: Parametric Cubic Cropping for Video Contrastive Learning
The visualizations show that ParamCrop adaptively controls the center distance and the IoU between two augmented views, and the learned change in the disparity along the training process is beneficial to learning a strong representation.
Exploring Stronger Feature for Temporal Action Localization
Temporal action localization aims to localize starting and ending time with action category.
OadTR: Online Action Detection with Transformers
Most recent approaches for online action detection tend to apply Recurrent Neural Network (RNN) to capture long-range temporal structure.
Proposal Relation Network for Temporal Action Detection
We calculate the detection results by assigning the proposals with corresponding classification results.
Ranked #1 on
Temporal Action Localization
on ActivityNet-1.3
A Stronger Baseline for Ego-Centric Action Detection
This technical report analyzes an egocentric video action detection method we used in the 2021 EPIC-KITCHENS-100 competition hosted in CVPR2021 Workshop.
Lite-HRNet: A Lightweight High-Resolution Network
We introduce a lightweight unit, conditional channel weighting, to replace costly pointwise (1x1) convolutions in shuffle blocks.
Ranked #22 on
Pose Estimation
on COCO test-dev
Self-Supervised Learning for Semi-Supervised Temporal Action Proposal
In this paper, we focus on applying the power of self-supervised methods to improve semi-supervised action proposal generation.
Ranked #11 on
Temporal Action Localization
on ActivityNet-1.3
Temporal Context Aggregation Network for Temporal Action Proposal Refinement
In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through "local and global" temporal context aggregation and complementary as well as progressive boundary refinement.
Ranked #3 on
Temporal Action Localization
on ActivityNet-1.3
Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search
Specifically, to reconcile the conflicts of multiple objectives, we simplify the standard tightly coupled pipelines and establish a deeply decoupled multi-task learning framework.
Weakly Supervised Text-Based Person Re-Identification
Specifically, to alleviate the intra-class variations, a clustering method is utilized to generate pseudo labels for both visual and textual instances.
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.
Representative Graph Neural Network
In this paper, we present a Representative Graph (RepGraph) layer to dynamically sample a few representative features, which dramatically reduces redundancy.
Multi-Level Temporal Pyramid Network for Action Detection
By this means, the proposed MLTPN can learn rich and discriminative features for different action instances with different durations.
Adversarial Semantic Data Augmentation for Human Pose Estimation
Human pose estimation is the task of localizing body keypoints from still images.
CBR-Net: Cascade Boundary Refinement Network for Action Detection: Submission to ActivityNet Challenge 2020 (Task 1)
In this report, we present our solution for the task of temporal action localization (detection) (task 1) in ActivityNet Challenge 2020.
Temporal Fusion Network for Temporal Action Localization:Submission to ActivityNet Challenge 2020 (Task E)
This technical report analyzes a temporal action localization method we used in the HACS competition which is hosted in Activitynet Challenge 2020. The goal of our task is to locate the start time and end time of the action in the untrimmed video, and predict action category. Firstly, we utilize the video-level feature information to train multiple video-level action classification models.
Relevant Region Prediction for Crowd Counting
The module builds a fully connected directed graph between the regions of different density where each node (region) is represented by weighted global pooled feature, and GCN is learned to map this region graph to a set of relation-aware regions representations.
Domain Adaptation for Image Dehazing
By training image translation and dehazing network in an end-to-end manner, we can obtain better effects of both image translation and dehazing.
BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation
We propose to treat these spatial details and categorical semantics separately to achieve high accuracy and high efficiency for realtime semantic segmentation.
Ranked #1 on
Real-Time Semantic Segmentation
on COCO-Stuff
Context Prior for Scene Segmentation
Given an input image and corresponding ground truth, Affinity Loss constructs an ideal affinity map to supervise the learning of Context Prior.
Ranked #1 on
Scene Understanding
on ADE20K val
FGN: Fully Guided Network for Few-Shot Instance Segmentation
Few-shot instance segmentation (FSIS) conjoins the few-shot learning paradigm with general instance segmentation, which provides a possible way of tackling instance segmentation in the lack of abundant labeled data for training.
GTNet: Generative Transfer Network for Zero-Shot Object Detection
FFU and BFU add the IoU variance to the results of CFU, yielding class-specific foreground and background features, respectively.
Re-ID Driven Localization Refinement for Person Search
The state-of-the-art methods train the detector individually, and the detected bounding boxes may be sub-optimal for the following re-ID task.
BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
Semantic segmentation requires both rich spatial information and sizeable receptive field.
Ranked #4 on
Semantic Segmentation
on SkyScapes-Dense
Learning a Discriminative Feature Network for Semantic Segmentation
Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra-class inconsistency and inter-class indistinction.
Ranked #9 on
Semantic Segmentation
on PASCAL VOC 2012 val
Learning a Discriminative Prior for Blind Image Deblurring
We present an effective blind image deblurring method based on a data-driven discriminative prior. Our work is motivated by the fact that a good image prior should favor clear images over blurred images. In this work, we formulate the image prior as a binary classifier which can be achieved by a deep convolutional neural network (CNN). The learned prior is able to distinguish whether an input image is clear or not. Embedded into the maximum a posterior (MAP) framework, it helps blind deblurring in various scenarios, including natural, face, text, and low-illumination images. However, it is difficult to optimize the deblurring method with the learned image prior as it involves a non-linear CNN. Therefore, we develop an efficient numerical approach based on the half-quadratic splitting method and gradient decent algorithm to solve the proposed model. Furthermore, the proposed model can be easily extended to non-uniform deblurring. Both qualitative and quantitative experimental results show that our method performs favorably against state-of-the-art algorithms as well as domain-specific image deblurring approaches.
Exemplar-based Linear Discriminant Analysis for Robust Object Tracking
However, the task in tracking is to search for a specific object, rather than an object category as in detection.
