Search Results for author:
Found 109 papers, 44 papers with code
Beyond 3DMM Space: Towards Fine-grained 3D Face Reconstruction
However, most of their training data is constructed by 3D Morphable Model, whose space spanned is only a small part of the shape space.
GigaGS: Scaling up Planar-Based 3D Gaussians for Large Scene Surface Reconstruction
To this end, we propose GigaGS, the first work for high-quality surface reconstruction for large-scale scenes using 3DGS.
Training-Free Point Cloud Recognition Based on Geometric and Semantic Information Fusion
For the geometric branch, we adopt a non-parametric strategy to extract geometric features.
GenAgent: Build Collaborative AI Systems with Automated Workflow Generation -- Case Studies on ComfyUI
Much previous AI research has focused on developing monolithic models to maximize their intelligence and capability, with the primary goal of enhancing performance on specific tasks.
ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor Reconstruction
Unlike previous methods that uniformly apply geometric priors on all samples, introducing significant bias in accuracy, our proposed normal deflection field dynamically learns and adapts the utilization of samples based on their specific characteristics, thereby improving both the accuracy and effectiveness of the model.
NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction
Signed Distance Function (SDF)-based volume rendering has demonstrated significant capabilities in surface reconstruction.
Freehand Sketch Generation from Mechanical Components
To address this issue, we design a two-stage generative framework mimicking the human sketching behavior pattern, called MSFormer, which is the first time to produce humanoid freehand sketches tailored for mechanical components.
PS-TTL: Prototype-based Soft-labels and Test-Time Learning for Few-shot Object Detection
Specifically, we design a Test-Time Learning (TTL) module that employs a mean-teacher network for self-training to discover novel instances from test data, allowing detectors to learn better representations and classifiers for novel classes.
Multi-modal Relation Distillation for Unified 3D Representation Learning
Recent advancements in multi-modal pre-training for 3D point clouds have demonstrated promising results by aligning heterogeneous features across 3D shapes and their corresponding 2D images and language descriptions.
AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm Quantizer
To address these issues, we propose a novel non-uniform quantizer, dubbed the Adaptive Logarithm AdaLog (AdaLog) quantizer.
Crowd-SAM: SAM as a Smart Annotator for Object Detection in Crowded Scenes
In computer vision, object detection is an important task that finds its application in many scenarios.
CodeV: Empowering LLMs for Verilog Generation through Multi-Level Summarization
Instruction-tuned large language models (LLMs) have demonstrated remarkable performance in automatically generating code for general-purpose programming languages like Python.
PredBench: Benchmarking Spatio-Temporal Prediction across Diverse Disciplines
In this paper, we introduce PredBench, a benchmark tailored for the holistic evaluation of spatio-temporal prediction networks.
InverseCoder: Unleashing the Power of Instruction-Tuned Code LLMs with Inverse-Instruct
Recent advancements in open-source code large language models (LLMs) have demonstrated remarkable coding abilities by fine-tuning on the data generated from powerful closed-source LLMs such as GPT-3. 5 and GPT-4 for instruction tuning.
STAL3D: Unsupervised Domain Adaptation for 3D Object Detection via Collaborating Self-Training and Adversarial Learning
Existing 3D object detection suffers from expensive annotation costs and poor transferability to unknown data due to the domain gap, Unsupervised Domain Adaptation (UDA) aims to generalize detection models trained in labeled source domains to perform robustly on unexplored target domains, providing a promising solution for cross-domain 3D object detection.
Holistic-Motion2D: Scalable Whole-body Human Motion Generation in 2D Space
In this paper, we introduce a novel path to $\textit{general}$ human motion generation by focusing on 2D space.
MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers
Recently, 3D assets created via reconstruction and generation have matched the quality of manually crafted assets, highlighting their potential for replacement.
I4VGen: Image as Stepping Stone for Text-to-Video Generation
Text-to-video generation has lagged behind text-to-image synthesis in quality and diversity due to the complexity of spatio-temporal modeling and limited video-text datasets.
Luban: Building Open-Ended Creative Agents via Autonomous Embodied Verification
In this work, we introduce autonomous embodied verification techniques for agents to fill the gap, laying the groundwork for creative tasks.
Vision-based 3D occupancy prediction in autonomous driving: a review and outlook
In recent years, autonomous driving has garnered escalating attention for its potential to relieve drivers' burdens and improve driving safety.
3D Face Modeling via Weakly-supervised Disentanglement Network joint Identity-consistency Prior
Generative 3D face models featuring disentangled controlling factors hold immense potential for diverse applications in computer vision and computer graphics.
DreamScape: 3D Scene Creation via Gaussian Splatting joint Correlation Modeling
Recent progress in text-to-3D creation has been propelled by integrating the potent prior of Diffusion Models from text-to-image generation into the 3D domain.
iVPT: Improving Task-relevant Information Sharing in Visual Prompt Tuning by Cross-layer Dynamic Connection
It innovatively incorporates a cross-layer dynamic connection (CDC) for input prompt tokens from adjacent layers, enabling effective sharing of task-relevant information.
InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization
Recent strides in the development of diffusion models, exemplified by advancements such as Stable Diffusion, have underscored their remarkable prowess in generating visually compelling images.
Generalizing 6-DoF Grasp Detection via Domain Prior Knowledge
We focus on the generalization ability of the 6-DoF grasp detection method in this paper.
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.
GVGEN: Text-to-3D Generation with Volumetric Representation
To better optimize the representation of these details, we propose a unique pruning and densifying method named the Candidate Pool Strategy, enhancing detail fidelity through selective optimization.
Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation
This paper focuses on the sim-to-real issue of RGB-D grasp detection and formulates it as a domain adaptation problem.
Agent3D-Zero: An Agent for Zero-shot 3D Understanding
The ability to understand and reason the 3D real world is a crucial milestone towards artificial general intelligence.
DrFER: Learning Disentangled Representations for 3D Facial Expression Recognition
Facial Expression Recognition (FER) has consistently been a focal point in the field of facial analysis.
Multi-Grained Cross-modal Alignment for Learning Open-vocabulary Semantic Segmentation from Text Supervision
Recently, learning open-vocabulary semantic segmentation from text supervision has achieved promising downstream performance.
Deep Common Feature Mining for Efficient Video Semantic Segmentation
Recent advancements in video semantic segmentation have made substantial progress by exploiting temporal correlations.
NeRF-Det++: Incorporating Semantic Cues and Perspective-aware Depth Supervision for Indoor Multi-View 3D Detection
We project the freely available 3D segmentation annotations onto the 2D plane and leverage the corresponding 2D semantic maps as the supervision signal, significantly enhancing the semantic awareness of multi-view detectors.
Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning
These outcomes suggest that the 3D point cloud is a valuable observation modality for intricate robotic tasks.
Assessing and Understanding Creativity in Large Language Models
This paper aims to establish an efficient framework for assessing the level of creativity in LLMs.
ImFace++: A Sophisticated Nonlinear 3D Morphable Face Model with Implicit Neural Representations
Accurate representations of 3D faces are of paramount importance in various computer vision and graphics applications.
Towards Generalizable Referring Image Segmentation via Target Prompt and Visual Coherence
Referring image segmentation (RIS) aims to segment objects in an image conditioning on free-from text descriptions.
BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping
We propose a metadata-aware self-supervised learning~(SSL)~framework useful for fine-grained classification and ecological mapping of bird species around the world.
UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
In the context of autonomous driving, the significance of effective feature learning is widely acknowledged.
PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
In this paper, we introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation, thereby establishing a pathway to 3D foundational models.
Ranked #2 on
Semantic Segmentation
on S3DIS
(using extra training data)
Self-driven Grounding: Large Language Model Agents with Automatical Language-aligned Skill Learning
Large language models (LLMs) show their powerful automatic reasoning and planning capability with a wealth of semantic knowledge about the human world.
DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic Calibration
Furthermore, to alleviate the interference by semantic drift, we develop the semantic calibration (SC) module to align the global shape and class centers of the pretrained and downstream feature distributions.
Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection
Anomaly detection (AD), aiming to find samples that deviate from the training distribution, is essential in safety-critical applications.
MotionGPT: Finetuned LLMs Are General-Purpose Motion Generators
Generating realistic human motion from given action descriptions has experienced significant advancements because of the emerging requirement of digital humans.
Flew Over Learning Trap: Learn Unlearnable Samples by Progressive Staged Training
Unlearning techniques are proposed to prevent third parties from exploiting unauthorized data, which generate unlearnable samples by adding imperceptible perturbations to data for public publishing.
ANPL: Towards Natural Programming with Interactive Decomposition
We deploy ANPL on the Abstraction and Reasoning Corpus (ARC), a set of unique tasks that are challenging for state-of-the-art AI systems, showing it outperforms baseline programming systems that (a) without the ability to decompose tasks interactively and (b) without the guarantee that the modules can be correctly composed together.
Ranked #12 on
Code Generation
on HumanEval
FR-Net:A Light-weight FFT Residual Net For Gaze Estimation
Furthermore, our method outperforms existing lightweight methods in terms of accuracy and efficiency for the gaze estimation task.
Align-DETR: Improving DETR with Simple IoU-aware BCE loss
We propose a metric, recall of best-regressed samples, to quantitively evaluate the misalignment problem.
Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images
Object detection on drone images with low-latency is an important but challenging task on the resource-constrained unmanned aerial vehicle (UAV) platform.
NeuFace: Realistic 3D Neural Face Rendering from Multi-view Images
Realistic face rendering from multi-view images is beneficial to various computer vision and graphics applications.
OcTr: Octree-based Transformer for 3D Object Detection
A key challenge for LiDAR-based 3D object detection is to capture sufficient features from large scale 3D scenes especially for distant or/and occluded objects.
Denoising Diffusion Autoencoders are Unified Self-supervised Learners
Inspired by recent advances in diffusion models, which are reminiscent of denoising autoencoders, we investigate whether they can acquire discriminative representations for classification via generative pre-training.
RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attention
Planar grasp detection is one of the most fundamental tasks to robotic manipulation, and the recent progress of consumer-grade RGB-D sensors enables delivering more comprehensive features from both the texture and shape modalities.
Online Symbolic Regression with Informative Query
Symbolic regression, the task of extracting mathematical expressions from the observed data $\{ \vx_i, y_i \}$, plays a crucial role in scientific discovery.
OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models
We propose a new method for object pose estimation without CAD models.
Ponder: Point Cloud Pre-training via Neural Rendering
We propose a novel approach to self-supervised learning of point cloud representations by differentiable neural rendering.
Towards Scale Balanced 6-DoF Grasp Detection in Cluttered Scenes
Moreover, a Scale Balanced Learning (SBL) loss and an Object Balanced Sampling (OBS) strategy are designed, where SBL enlarges the gradients of the samples whose scales are in low frequency by apriori weights while OBS captures more points on small-scale objects with the help of an auxiliary segmentation network.
Ranked #2 on
Robotic Grasping
on GraspNet-1Billion
Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing
Along with the widespread use of face recognition systems, their vulnerability has become highlighted.
Reconstructing Hand-Held Objects from Monocular Video
The key idea is that the hand motion naturally provides multiple views of the object and the motion can be reliably estimated by a hand pose tracker.
Racial Disparities in Pulse Oximetry Cannot Be Fixed With Race-Based Correction
Studies have shown pulse oximeter measurements of blood oxygenation have statistical bias that is a function of race, which results in higher rates of occult hypoxemia, i. e., missed detection of dangerously low oxygenation, in patients of color.
STS: Surround-view Temporal Stereo for Multi-view 3D Detection
Instead of using a sole monocular depth method, in this work, we propose a novel Surround-view Temporal Stereo (STS) technique that leverages the geometry correspondence between frames across time to facilitate accurate depth learning.
Motion Sensitive Contrastive Learning for Self-supervised Video Representation
Contrastive learning has shown great potential in video representation learning.
Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles
Video Anomaly Detection (VAD) is an important topic in computer vision.
Ranked #6 on
Anomaly Detection
on ShanghaiTech
Neural Program Synthesis with Query
In this work, we propose a query-based framework that trains a query neural network to generate informative input-output examples automatically and interactively from a large query space.
Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement
To address the two issues above, this paper proposes a novel framework, namely Attentive Cross-modal Interaction Network with Motion Enhancement (MEACI-Net).
Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection
Specifically, TRKP adopts the teacher-student framework, where the multi-head teacher network is built to extract knowledge from labeled source domains and guide the student network to learn detectors in unlabeled target domain.
Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint
Active learning is a promising alternative to alleviate the issue of high annotation cost in the computer vision tasks by consciously selecting more informative samples to label.
Beyond 3DMM: Learning to Capture High-fidelity 3D Face Shape
3D Morphable Model (3DMM) fitting has widely benefited face analysis due to its strong 3D priori.
CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection
In autonomous driving, LiDAR point-clouds and RGB images are two major data modalities with complementary cues for 3D object detection.
ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations
Precise representations of 3D faces are beneficial to various computer vision and graphics applications.
Ranked #2 on
Face Alignment
on FaceScape
ABPN: Adaptive Blend Pyramid Network for Real-Time Local Retouching of Ultra High-Resolution Photo
The network is mainly composed of two components: a context-aware local retouching layer (LRL) and an adaptive blend pyramid layer (BPL).
ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization
Weakly-supervised temporal action localization (WTAL) in untrimmed videos has emerged as a practical but challenging task since only video-level labels are available.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
UFPMP-Det: Toward Accurate and Efficient Object Detection on Drone Imagery
This paper proposes a novel approach to object detection on drone imagery, namely Multi-Proxy Detection Network with Unified Foreground Packing (UFPMP-Det).
Segmentation-Reconstruction-Guided Facial Image De-occlusion
The proposed model consists of a 3D face reconstruction module, a face segmentation module, and an image generation module.
Boundary Guided Context Aggregation for Semantic Segmentation
The recent studies on semantic segmentation are starting to notice the significance of the boundary information, where most approaches see boundaries as the supplement of semantic details.
Identity-aware Graph Memory Network for Action Detection
Action detection plays an important role in high-level video understanding and media interpretation.
iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization
Differentiable ARchiTecture Search (DARTS) uses a continuous relaxation of network representation and dramatically accelerates Neural Architecture Search (NAS) by almost thousands of times in GPU-day.
Ranked #9 on
Neural Architecture Search
on CIFAR-10
PR-GCN: A Deep Graph Convolutional Network with Point Refinement for 6D Pose Estimation
This paper proposes a novel deep learning approach, namely Graph Convolutional Network with Point Refinement (PR-GCN), to simultaneously address the issues above in a unified way.
Image Inpainting via Conditional Texture and Structure Dual Generation
Deep generative approaches have recently made considerable progress in image inpainting by introducing structure priors.
Recurrent Graph Neural Networks for Rumor Detection in Online Forums
The widespread adoption of online social networks in daily life has created a pressing need for effectively classifying user-generated content.
Double-Dot Network for Antipodal Grasp Detection
This paper proposes a new deep learning approach to antipodal grasp detection, named Double-Dot Network (DD-Net).
Pixel Sampling for Style Preserving Face Pose Editing
The existing auto-encoder based face pose editing methods primarily focus on modeling the identity preserving ability during pose synthesis, but are less able to preserve the image style properly, which refers to the color, brightness, saturation, etc.
Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction
Missing textures in the incomplete UV map are further full-filled by the UV generator.
Magnifying Subtle Facial Motions for Effective 4D Expression Recognition
In this paper, an effective pipeline to automatic 4D Facial Expression Recognition (4D FER) is proposed.
Student-Teacher Feature Pyramid Matching for Anomaly Detection
Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies.
Ranked #28 on
Anomaly Detection
on VisA
MRDet: A Multi-Head Network for Accurate Oriented Object Detection in Aerial Images
Objects in aerial images usually have arbitrary orientations and are densely located over the ground, making them extremely challenge to be detected.
PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection
LiDAR-based 3D object detection is an important task for autonomous driving and current approaches suffer from sparse and partial point clouds of distant and occluded objects.
Ranked #4 on
3D Object Detection
on KITTI Cars Hard val
Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances, and is useful when manual annotation is time-consuming or data acquisition is limited.
Ranked #18 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Improving Object Detection with Selective Self-supervised Self-training
They fail to improve object detectors in their vanilla forms due to the domain gap between the Web images and curated datasets.
Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation
Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred.
DWM: A Decomposable Winograd Method for Convolution Acceleration
In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd's minimal filtering algorithm to a wide and general convolutions.
A Feasible Framework for Arbitrary-Shaped Scene Text Recognition
Deep learning based methods have achieved surprising progress in Scene Text Recognition (STR), one of classic problems in computer vision.
Distraction-Aware Feature Learning for Human Attribute Recognition via Coarse-to-Fine Attention Mechanism
Recently, Human Attribute Recognition (HAR) has become a hot topic due to its scientific challenges and application potentials, where localizing attributes is a crucial stage but not well handled.
Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels
Due to the lack of suitable datasets, previous research has only examined deep learning on controlled synthetic label noise, and real-world label noise has never been studied in a controlled setting.
Ranked #12 on
Image Classification
on WebVision-1000
Learning Spatial Fusion for Single-Shot Object Detection
Pyramidal feature representation is the common practice to address the challenge of scale variation in object detection.
Ranked #147 on
Object Detection
on COCO test-dev
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers.
Synthetic vs Real: Deep Learning on Controlled Noise
Performing controlled experiments on noisy data is essential in thoroughly understanding deep learning across a spectrum of noise levels.
ArduCode: Predictive Framework for Automation Engineering
We show that machine learning can be leveraged to assist the automation engineer in classifying automation, finding similar code snippets, and reasoning about the hardware selection of sensors and actuators.
Graph Representation Ensemble Learning
In this work, we introduce the problem of graph representation ensemble learning and provide a first of its kind framework to aggregate multiple graph embedding methods efficiently.
Benchmarks for Graph Embedding Evaluation
We use the comparisons on our 100 benchmark graphs to define GFS-score, that can be applied to any embedding method to quantify its performance.
CompactNet: Platform-Aware Automatic Optimization for Convolutional Neural Networks
Convolutional Neural Network (CNN) based Deep Learning (DL) has achieved great progress in many real-life applications.
Adaptive NMS: Refining Pedestrian Detection in a Crowd
Pedestrian detection in a crowd is a very challenging issue.
Ranked #18 on
Object Detection
on CrowdHuman (full body)
Learning Continuous Face Age Progression: A Pyramid of GANs
The two underlying requirements of face age progression, i. e. aging accuracy and identity permanence, are not well studied in the literature.
Learning Face Age Progression: A Pyramid Architecture of GANs
The two underlying requirements of face age progression, i. e. aging accuracy and identity permanence, are not well studied in the literature.
Feature Map Pooling for Cross-View Gait Recognition Based on Silhouette Sequence Images
In this paper, we develop a novel convolutional neural network based approach to extract and aggregate useful information from gait silhouette sequence images instead of simply representing the gait process by averaging silhouette images.
Receptive Field Block Net for Accurate and Fast Object Detection
Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs.
Defect detection for patterned fabric images based on GHOG and low-rank decomposition
In order to accurately detect defects in patterned fabric images, a novel detection algorithm based on Gabor-HOG (GHOG) and low-rank decomposition is proposed in this paper.
Face Aging Effect Simulation using Hidden Factor Analysis Joint Sparse Representation
Face aging simulation has received rising investigations nowadays, whereas it still remains a challenge to generate convincing and natural age-progressed face images.
