Search Results for author:
Found 27 papers, 16 papers with code
BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving
Specifically, BEVerse first performs shared feature extraction and lifting to generate 4D BEV representations from multi-timestamp and multi-view images.
Gait Recognition in the Wild: A Benchmark
In this paper, we contribute a new benchmark for Gait REcognition in the Wild (GREW).
WebFace260M: A Benchmark for Million-Scale Deep Face Recognition
For a comprehensive evaluation of face matchers, three recognition tasks are performed under standard, masked and unbiased settings, respectively.
HFT: Lifting Perspective Representations via Hybrid Feature Transformation
In order to reap the benefits and avoid the drawbacks of CBFT and CFFT, we propose a novel framework with a Hybrid Feature Transformation module (HFT).
BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection
Single frame data contains finite information which limits the performance of the existing vision-based multi-camera 3D object detection paradigms.
Ranked #98 on
3D Object Detection
on nuScenes
PyTorch Geometric Signed Directed: A Survey and Software on Graph Neural Networks for Signed and Directed Graphs
In this paper, we present PyTorch Geometric Signed Directed, a survey and software on GNNs for signed and directed networks.
Learn over Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data
More concretely, we first design a search-based module to retrieve a user's relevant historical behaviors, which are then mixed up with her recent records to be fed into a time-aware sequential network for capturing her time-sensitive demands.
Reasoning over Hybrid Chain for Table-and-Text Open Domain QA
CARP utilizes hybrid chain to model the explicit intermediate reasoning process across table and text for question answering.
Ranked #2 on
Question Answering
on OTT-QA
BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View
As a fast version, BEVDet-Tiny scores 31. 2% mAP and 39. 2% NDS on the nuScenes val set.
Face-NMS: A Core-set Selection Approach for Efficient Face Recognition
The current research on this problem mainly focuses on designing an efficient Fully-connected layer (FC) to reduce GPU memory consumption caused by a large number of identities.
Single Node Injection Attack against Graph Neural Networks
In this paper, we focus on an extremely limited scenario of single node injection evasion attack, i. e., the attacker is only allowed to inject one single node during the test phase to hurt GNN's performance.
Signed Bipartite Graph Neural Networks
Signed bipartite networks are different from classical signed networks, which contain two different node sets and signed links between two node sets.
Masked Face Recognition Challenge: The WebFace260M Track Report
There are second phase of the challenge till October 1, 2021 and on-going leaderboard.
CoSQA: 20,000+ Web Queries for Code Search and Question Answering
Finding codes given natural language query isb eneficial to the productivity of software developers.
SIMPLE: SIngle-network with Mimicking and Point Learning for Bottom-up Human Pose Estimation
To make a better trade-off between accuracy and efficiency, we propose a novel multi-person pose estimation framework, SIngle-network with Mimicking and Point Learning for Bottom-up Human Pose Estimation (SIMPLE).
WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach
In this work, we conduct a thorough examination of pretrained model based unsupervised sentence embeddings.
WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition
In this paper, we contribute a new million-scale face benchmark containing noisy 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training data, as well as an elaborately designed time-constrained evaluation protocol.
Ranked #1 on
Face Verification
on IJB-C
(training dataset metric)
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
Benchmark datasets have a significant impact on accelerating research in programming language tasks.
Ranked #1 on
Cloze Test
on CodeXGLUE - CT-maxmin
SDGNN: Learning Node Representation for Signed Directed Networks
Guided by related sociological theories, we propose a novel Signed Directed Graph Neural Networks model named SDGNN to learn node embeddings for signed directed networks.
AID: Pushing the Performance Boundary of Human Pose Estimation with Information Dropping Augmentation
As AID successfully pushes the performance boundary of human pose estimation problem by considerable margin and sets a new state-of-the-art, we hope AID to be a regular configuration for training human pose estimators.
Ranked #1 on
Multi-Person Pose Estimation
on COCO minival
The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation
Specifically, by investigating the standard data processing in state-of-the-art approaches mainly including coordinate system transformation and keypoint format transformation (i. e., encoding and decoding), we find that the results obtained by common flipping strategy are unaligned with the original ones in inference.
Ranked #10 on
Pose Estimation
on COCO test-dev
Multi-Stage HRNet: Multiple Stage High-Resolution Network for Human Pose Estimation
Human pose estimation are of importance for visual understanding tasks such as action recognition and human-computer interaction.
Signed Graph Attention Networks
We evaluate the proposed SiGAT method by applying it to the signed link prediction task.
Ranked #1 on
Link Sign Prediction
on Slashdot
COS960: A Chinese Word Similarity Dataset of 960 Word Pairs
Word similarity computation is a widely recognized task in the field of lexical semantics.
An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes
Real-time motion detection in non-stationary scenes is a difficult task due to dynamic background, changing foreground appearance and limited computational resource.
Motion Detection
Motion Detection In Non-Stationary Scenes
+1
Optical Flow Based Online Moving Foreground Analysis
Obtained by moving object detection, the foreground mask result is unshaped and can not be directly used in most subsequent processes.
Optical Flow Based Real-time Moving Object Detection in Unconstrained Scenes
Real-time moving object detection in unconstrained scenes is a difficult task due to dynamic background, changing foreground appearance and limited computational resource.
