Search Results for author:
Found 54 papers, 29 papers with code
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 #2 on
Face Identification
on MegaFace
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?)
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
Hand Pose Estimation
on K2HPD
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.
Ranked #12 on
Few-Shot Image Classification
on CIFAR-FS 5-way (1-shot)
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.
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 #14 on
Few-Shot Object Detection
on MS-COCO (30-shot)
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.
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.
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.
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.
A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation from a Single RGB Image
3D interacting hand pose estimation from a single RGB image is a challenging task, due to serious self-occlusion and inter-occlusion towards hands, confusing similar appearance patterns between 2 hands, ill-posed joint position mapping from 2D to 3D, etc.. To address these, we propose to extend A2J-the state-of-the-art depth-based 3D single hand pose estimation method-to RGB domain under interacting hand condition.
Ranked #7 on
Hand Pose Estimation
on NYU Hands
PIZZA: A Powerful Image-only Zero-Shot Zero-CAD Approach to 6 DoF Tracking
Our results on challenging datasets are on par with previous works that require much more information (training images of the target objects, 3D models, and/or depth data).
3DV: 3D Dynamic Voxel for Action Recognition in Depth Video
Each available 3DV voxel intrinsically involves 3D spatial and motion feature jointly.
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.
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.
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.
Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video
Experiments on MPEblink verify the essential challenges of real-time multi-person eyeblink detection in the wild for untrimmed video.
End-to-end Video Gaze Estimation via Capturing Head-face-eye Spatial-temporal Interaction Context
In this letter, we propose a new method, Multi-Clue Gaze (MCGaze), to facilitate video gaze estimation via capturing spatial-temporal interaction context among head, face, and eye in an end-to-end learning way, which has not been well concerned yet.
Ranked #1 on
Gaze Estimation
on Gaze360
A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation
We propose a simple, yet powerful approach for unsupervised object segmentation in videos.
Ranked #1 on
Unsupervised Video Object Segmentation
on SegTrack v2
(Jaccard (Mean) metric)
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.
How Far Are We from Believable AI Agents? A Framework for Evaluating the Believability of Human Behavior Simulation
Human behavior simulation of AI agents necessitates the agents to possess a quality of believability, which is crucial as it facilitates users in establishing trust toward the agents and streamlines the fulfillment of the agents' goal.
Continual Learning For On-Device Environmental Sound Classification
Experimental results on the DCASE 2019 Task 1 and ESC-50 dataset show that our proposed method outperforms baseline continual learning methods on classification accuracy and computational efficiency, indicating our method can efficiently and incrementally learn new classes without the catastrophic forgetting problem for on-device environmental sound classification.
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?
Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword Spotting
Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment.
Action Recognition for Depth Video using Multi-view Dynamic Images
To better exploit three-dimensional (3D) characteristics, multi-view dynamic images are proposed.
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Level
As ordinary injection attacks disrupt the typical timing properties of the CAN data stream, rule-based intrusion detection systems (IDS) can easily detect them.
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.
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.
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.
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.
Monocular Relative Depth Perception With Web Stereo Data Supervision
In this paper we study the problem of monocular relative depth perception in the wild.
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 Cognitive Routing based on Deep Reinforcement Learning
Routing is one of the key functions for stable operation of network infrastructure.
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.
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.
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.
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.
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.
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.
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.
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.
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling
Specifically, 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 alleviating the effect from noisy samples incorrectly grouped into the clean set.
Multi-Ship Tracking by Robust Similarity metric
The low frame rates and severe image shake caused by wave turbulence in ship datasets often result in minimal, or even zero, Intersection of Union (IoU) between the predicted and detected bounding boxes.
Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction
Unlike existing approaches treating models as black boxes, Scale-MIA recognizes the importance of the intricate architecture and inner workings of machine learning models.
GBD-TS: Goal-based Pedestrian Trajectory Prediction with Diffusion using Tree Sampling Algorithm
GBD combines goal prediction with the diffusion network.
SAI3D: Segment Any Instance in 3D Scenes
Advancements in 3D instance segmentation have traditionally been tethered to the availability of annotated datasets, limiting their application to a narrow spectrum of object categories.
Dual Knowledge Distillation for Efficient Sound Event Detection
To address this issue, we introduce a novel framework referred to as dual knowledge distillation for developing efficient SED systems in this work.
Ranked #2 on
Sound Event Detection
on DESED
(using extra training data)
A Survey of Lottery Ticket Hypothesis
The Lottery Ticket Hypothesis (LTH) states that a dense neural network model contains a highly sparse subnetwork (i. e., winning tickets) that can achieve even better performance than the original model when trained in isolation.
CrossGLG: LLM Guides One-shot Skeleton-based 3D Action Recognition in a Cross-level Manner
Most existing one-shot skeleton-based action recognition focuses on raw low-level information (e. g., joint location), and may suffer from local information loss and low generalization ability.
A Survey on Long Video Generation: Challenges, Methods, and Prospects
Video generation is a rapidly advancing research area, garnering significant attention due to its broad range of applications.
