Search Results for author:
Found 36 papers, 17 papers with code
Fast-BEV: A Fast and Strong Bird's-Eye View Perception Baseline
Our Fast-BEV consists of five parts, We novelly propose (1) a lightweight deployment-friendly view transformation which fast transfers 2D image feature to 3D voxel space, (2) an multi-scale image encoder which leverages multi-scale information for better performance, (3) an efficient BEV encoder which is particularly designed to speed up on-vehicle inference.
Towards Explainable 3D Grounded Visual Question Answering: A New Benchmark and Strong Baseline
We also propose a new 3D VQA framework to effectively predict the completely visually grounded and explainable answer.
Improving RGB-D Point Cloud Registration by Learning Multi-scale Local Linear Transformation
In addition to previous methods that seek correspondences by hand-crafted or learnt geometric features, recent point cloud registration methods have tried to apply RGB-D data to achieve more accurate correspondence.
SketchSampler: Sketch-based 3D Reconstruction via View-dependent Depth Sampling
Reconstructing a 3D shape based on a single sketch image is challenging due to the large domain gap between a sparse, irregular sketch and a regular, dense 3D shape.
X-Learner: Learning Cross Sources and Tasks for Universal Visual Representation
2) Squeeze Stage: X-Learner condenses the model to a reasonable size and learns the universal and generalizable representation for various tasks transferring.
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy
This work thus proposes a novel active learning framework for realistic dataset annotation.
Ranked #1 on
Image Classification
on Food-101
(using extra training data)
3DJCG: A Unified Framework for Joint Dense Captioning and Visual Grounding on 3D Point Clouds
Observing that the 3D captioning task and the 3D grounding task contain both shared and complementary information in nature, in this work, we propose a unified framework to jointly solve these two distinct but closely related tasks in a synergistic fashion, which consists of both shared task-agnostic modules and lightweight task-specific modules.
ForgeryNet -- Face Forgery Analysis Challenge 2021: Methods and Results
The rapid progress of photorealistic synthesis techniques has reached a critical point where the boundary between real and manipulated images starts to blur.
VoteHMR: Occlusion-Aware Voting Network for Robust 3D Human Mesh Recovery from Partial Point Clouds
3D human mesh recovery from point clouds is essential for various tasks, including AR/VR and human behavior understanding.
Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds
Inspired by the back-tracing strategy in the conventional Hough voting methods, in this work, we introduce a new 3D object detection method, named as Back-tracing Representative Points Network (BRNet), which generatively back-traces the representative points from the vote centers and also revisits complementary seed points around these generated points, so as to better capture the fine local structural features surrounding the potential objects from the raw point clouds.
Ranked #9 on
3D Object Detection
on SUN-RGBD val
DanceFormer: Music Conditioned 3D Dance Generation with Parametric Motion Transformer
In this paper, we reformulate it by a two-stage process, ie, a key pose generation and then an in-between parametric motion curve prediction, where the key poses are easier to be synchronized with the music beats and the parametric curves can be efficiently regressed to render fluent rhythm-aligned movements.
ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis
To counter this emerging threat, we construct the ForgeryNet dataset, an extremely large face forgery dataset with unified annotations in image- and video-level data across four tasks: 1) Image Forgery Classification, including two-way (real / fake), three-way (real / fake with identity-replaced forgery approaches / fake with identity-remained forgery approaches), and n-way (real and 15 respective forgery approaches) classification.
3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds
Visual grounding on 3D point clouds is an emerging vision and language task that benefits various applications in understanding the 3D visual world.
StyleFormer: Real-Time Arbitrary Style Transfer via Parametric Style Composition
In this work, we propose a new feed-forward arbitrary style transfer method, referred to as StyleFormer, which can simultaneously fulfill fine-grained style diversity and semantic content coherency.
PV-NAS: Practical Neural Architecture Search for Video Recognition
Recently, deep learning has been utilized to solve video recognition problem due to its prominent representation ability.
Adaptive Gradient Method with Resilience and Momentum
Several variants of stochastic gradient descent (SGD) have been proposed to improve the learning effectiveness and efficiency when training deep neural networks, among which some recent influential attempts would like to adaptively control the parameter-wise learning rate (e. g., Adam and RMSProp).
Thinking in Frequency: Face Forgery Detection by Mining Frequency-aware Clues
As realistic facial manipulation technologies have achieved remarkable progress, social concerns about potential malicious abuse of these technologies bring out an emerging research topic of face forgery detection.
Unsupervised Domain Expansion from Multiple Sources
Given an existing system learned from previous source domains, it is desirable to adapt the system to new domains without accessing and forgetting all the previous domains in some applications.
Powering One-shot Topological NAS with Stabilized Share-parameter Proxy
Specifically, the difficulties for architecture searching in such a complex space has been eliminated by the proposed stabilized share-parameter proxy, which employs Stochastic Gradient Langevin Dynamics to enable fast shared parameter sampling, so as to achieve stabilized measurement of architecture performance even in search space with complex topological structures.
Morphing and Sampling Network for Dense Point Cloud Completion
3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community.
Ranked #6 on
Point Cloud Completion
on ShapeNet
Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization
To predict the existence of a particular attribute, it is demanded to localize the regions related to the attribute.
Ranked #1 on
Pedestrian Attribute Recognition
on RAP
Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry Towards Monocular Deep SLAM
In this paper we tackle the joint learning problem of keyframe detection and visual odometry towards monocular visual SLAM systems.
CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval
Text-image cross-modal retrieval is a challenging task in the field of language and vision.
Ranked #8 on
Image Retrieval
on Flickr30K 1K test
Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking
In this paper, we propose a generative framework that unifies depth-based 3D facial pose tracking and face model adaptation on-the-fly, in the unconstrained scenarios with heavy occlusions and arbitrary facial expression variations.
Semantics Disentangling for Text-to-Image Generation
Synthesizing photo-realistic images from text descriptions is a challenging problem.
Context and Attribute Grounded Dense Captioning
Dense captioning aims at simultaneously localizing semantic regions and describing these regions-of-interest (ROIs) with short phrases or sentences in natural language.
Ranked #2 on
Dense Captioning
on Visual Genome
GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving
We present an efficient 3D object detection framework based on a single RGB image in the scenario of autonomous driving.
Ranked #18 on
Vehicle Pose Estimation
on KITTI Cars Hard
Video Generation from Single Semantic Label Map
This paper proposes the novel task of video generation conditioned on a SINGLE semantic label map, which provides a good balance between flexibility and quality in the generation process.
Unsupervised Bi-directional Flow-based Video Generation from one Snapshot
Imagining multiple consecutive frames given one single snapshot is challenging, since it is difficult to simultaneously predict diverse motions from a single image and faithfully generate novel frames without visual distortions.
Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection
Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs.
Ranked #9 on
Multi-Label Classification
on NUS-WIDE
Zoom-Net: Mining Deep Feature Interactions for Visual Relationship Recognition
We show that by encouraging deep message propagation and interactions between local object features and global predicate features, one can achieve compelling performance in recognizing complex relationships without using any linguistic priors.
Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration
Zero-shot artistic style transfer is an important image synthesis problem aiming at transferring arbitrary style into content images.
Exploring Disentangled Feature Representation Beyond Face Identification
This paper proposes learning disentangled but complementary face features with minimal supervision by face identification.
Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
In this study, we introduce a novel compact motion representation for video action recognition, named Optical Flow guided Feature (OFF), which enables the network to distill temporal information through a fast and robust approach.
Ranked #32 on
Action Recognition
on UCF101
HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis
Pedestrian analysis plays a vital role in intelligent video surveillance and is a key component for security-centric computer vision systems.
Ranked #2 on
Pedestrian Attribute Recognition
on RAP
A Generative Model for Depth-Based Robust 3D Facial Pose Tracking
We consider the problem of depth-based robust 3D facial pose tracking under unconstrained scenarios with heavy occlusions and arbitrary facial expression variations.
