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Found 38 papers, 20 papers with code
Pixel-Pair Occlusion Relationship Map (P2ORM): Formulation, Inference & Application
Inference & Application","We formalize concepts around geometric occlusion in 2D images (i. e., ignoring semantics), and propose a novel unified formulation of both occlusion boundaries and occlusion orientations via a pixel-pair occlusion relation.
Are All the Datasets in Benchmark Necessary? A Pilot Study of Dataset Evaluation for Text Classification
In this paper, we ask the research question of whether all the datasets in the benchmark are necessary.
CANShield: Signal-based Intrusion Detection for Controller Area Networks
Modern vehicles rely on a fleet of electronic control units (ECUs) connected through controller area network (CAN) buses for critical vehicular control.
Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions
It relies on a small set of training objects to learn local object representations, which allow us to locally match the input image to a set of "templates", rendered images of the CAD models for the new objects.
Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword Spotting
Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment.
DataLab: A Platform for Data Analysis and Intervention
Despite data's crucial role in machine learning, most existing tools and research tend to focus on systems on top of existing data rather than how to interpret and manipulate data.
Re-ranking for image retrieval and transductive few-shot classification
In the problems of image retrieval and few-shot classification, the mainstream approaches focus on learning a better feature representation.
UVO Challenge on Video-based Open-World Segmentation 2021: 1st Place Solution
In this report, we introduce our (pretty straightforard) two-step "detect-then-match" video instance segmentation method.
1st Place Solution for the UVO Challenge on Image-based Open-World Segmentation 2021
We describe our two-stage instance segmentation framework we use to compete in the challenge.
On the Robustness of Reading Comprehension Models to Entity Renaming
We study the robustness of machine reading comprehension (MRC) models to entity renaming -- do models make more wrong predictions when the same questions are asked about an entity whose name has been changed?
Robust Learning with Adaptive Sample Credibility Modeling
For the clean set, we deliberately design a memory-based modulation scheme to dynamically adjust the contribution of each sample in terms of its historical credibility sequence during training, thus to alleviate the effect from potential hard noisy samples in clean set.
PoseContrast: Class-Agnostic Object Viewpoint Estimation in the Wild with Pose-Aware Contrastive Learning
We experimented on Pascal3D+, ObjectNet3D and Pix3D in a cross-dataset fashion, with both seen and unseen classes.
Learning to Better Segment Objects from Unseen Classes with Unlabeled Videos
Through extensive experiments, we show that our method can generate a high-quality training set which significantly boosts the performance of segmenting objects of unseen classes.
ExplainaBoard: An Explainable Leaderboard for NLP
In this paper, we present a new conceptualization and implementation of NLP evaluation: the ExplainaBoard, which in addition to inheriting the functionality of the standard leaderboard, also allows researchers to (i) diagnose strengths and weaknesses of a single system (e. g.~what is the best-performing system bad at?)
Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources
C-CycleGAN transfers source samples at instance-level to an intermediate domain that is closer to the target domain with sentiment semantics preserved and without losing discriminative features.
Partial FC: Training 10 Million Identities on a Single Machine
The experiment demonstrates no loss of accuracy when training with only 10\% randomly sampled classes for the softmax-based loss functions, compared with training with full classes using state-of-the-art models on mainstream benchmarks.
Ranked #1 on
Face Identification
on MegaFace
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019
All methods were based on deep learning and categorized into two groups: multi-model method and single model method.
Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild
In this paper, we tackle the problems of few-shot object detection and few-shot viewpoint estimation.
Ranked #6 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Pixel-Pair Occlusion Relationship Map(P2ORM): Formulation, Inference & Application
The former provides a way to generate large-scale accurate occlusion datasets while, based on the latter, we propose a novel method for task-independent pixel-level occlusion relationship estimation from single images.
ECML: An Ensemble Cascade Metric Learning Mechanism towards Face Verification
Embedding RMML into the proposed ECML mechanism, our metric learning paradigm (EC-RMML) can run in the one-pass learning manner.
P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds
Specifically, we first sample seeds from the point clouds in template and search area respectively.
3DV: 3D Dynamic Voxel for Action Recognition in Depth Video
Each available 3DV voxel intrinsically involves 3D spatial and motion feature jointly.
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
The evidence lower bound of the marginal log-likelihood of empirical Bayes decomposes as a sum of local KL divergences between the variational posterior and the true posterior on the query set of each task.
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction
To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set.
Towards Cognitive Routing based on Deep Reinforcement Learning
Routing is one of the key functions for stable operation of network infrastructure.
A2J: Anchor-to-Joint Regression Network for 3D Articulated Pose Estimation from a Single Depth Image
For 3D hand and body pose estimation task in depth image, a novel anchor-based approach termed Anchor-to-Joint regression network (A2J) with the end-to-end learning ability is proposed.
Ranked #1 on
Depth Estimation
on NYU-Depth V2
(mAP metric)
Pose from Shape: Deep Pose Estimation for Arbitrary 3D Objects
Most deep pose estimation methods need to be trained for specific object instances or categories.
Comparative evaluation of 2D feature correspondence selection algorithms
Correspondence selection aiming at seeking correct feature correspondences from raw feature matches is pivotal for a number of feature-matching-based tasks.
Towards Real-time Eyeblink Detection in The Wild:Dataset,Theory and Practices
Effective and real-time eyeblink detection is of wide-range applications, such as deception detection, drive fatigue detection, face anti-spoofing, etc.
Towards Good Practices on Building Effective CNN Baseline Model for Person Re-identification
Person re-identification is indeed a challenging visual recognition task due to the critical issues of human pose variation, human body occlusion, camera view variation, etc.
DCFEE: A Document-level Chinese Financial Event Extraction System based on Automatically Labeled Training Data
We present an event extraction framework to detect event mentions and extract events from the document-level financial news.
Action Recognition for Depth Video using Multi-view Dynamic Images
To better exploit three-dimensional (3D) characteristics, multi-view dynamic images are proposed.
Monocular Relative Depth Perception With Web Stereo Data Supervision
In this paper we study the problem of monocular relative depth perception in the wild.
Performance Evaluation of 3D Correspondence Grouping Algorithms
This paper presents a thorough evaluation of several widely-used 3D correspondence grouping algorithms, motived by their significance in vision tasks relying on correct feature correspondences.
TasselNet: Counting maize tassels in the wild via local counts regression network
To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment.
Predicting Restaurant Consumption Level through Social Media Footprints
Accurate prediction of user attributes from social media is valuable for both social science analysis and consumer targeting.
