Search Results for author:
Found 71 papers, 22 papers with code
An Explicit Discrete-Time Dynamic Vehicle Model with Assured Numerical Stability
Subsequent to the validation process, we conduct comprehensive simulations comparing our proposed model with both kinematic models and existing dynamic models discretized through the forward Euler method.
Open-Vocabulary Octree-Graph for 3D Scene Understanding
Open-vocabulary 3D scene understanding is indispensable for embodied agents.
Night-to-Day Translation via Illumination Degradation Disentanglement
Specifically, our method comprises a degradation disentanglement module and a degradation-aware contrastive learning module.
FreeGaussian: Guidance-free Controllable 3D Gaussian Splats with Flow Derivatives
Reconstructing controllable Gaussian splats from monocular video is a challenging task due to its inherently insufficient constraints.
Efficient Diffusion as Low Light Enhancer
In this paper, we identify two primary factors contributing to performance degradation: fitting errors and the inference gap.
Aerial Vision-and-Language Navigation via Semantic-Topo-Metric Representation Guided LLM Reasoning
Aerial Vision-and-Language Navigation (VLN) is a novel task enabling Unmanned Aerial Vehicles (UAVs) to navigate in outdoor environments through natural language instructions and visual cues.
Semi-LLIE: Semi-supervised Contrastive Learning with Mamba-based Low-light Image Enhancement
Despite the impressive advancements made in recent low-light image enhancement techniques, the scarcity of paired data has emerged as a significant obstacle to further advancements.
COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models
Specifically, a Proposal-Execution-Feedback-Adjustment (PEFA) mechanism is designed to decompose and assign actions for individual robots, where a centralized task assigner makes a task planning proposal to decompose the complex task into subtasks, and then assigns subtasks to robot executors.
AlignBot: Aligning VLM-powered Customized Task Planning with User Reminders Through Fine-Tuning for Household Robots
To validate the effectiveness of AlignBot, experiments are conducted in real-world household environments, which are constructed within the laboratory to replicate typical household settings.
Learning 2D Invariant Affordance Knowledge for 3D Affordance Grounding
3D Object Affordance Grounding aims to predict the functional regions on a 3D object and has laid the foundation for a wide range of applications in robotics.
Physical prior guided cooperative learning framework for joint turbulence degradation estimation and infrared video restoration
Infrared imaging and turbulence strength measurements are in widespread demand in many fields.
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance
Within the intervals between semantic key states, optical flow is employed to capture motion key states to understand the mechanisms of "how to do".
Diffusion-driven lensless fiber endomicroscopic quantitative phase imaging towards digital pathology
Lensless fiber endomicroscope is an emerging tool for in-vivo microscopic imaging, where quantitative phase imaging (QPI) can be utilized as a label-free method to enhance image contrast.
A Multi-Stage Framework for 3D Individual Tooth Segmentation in Dental CBCT
Cone beam computed tomography (CBCT) is a common way of diagnosing dental related diseases.
LiveScene: Language Embedding Interactive Radiance Fields for Physical Scene Rendering and Control
This paper scales object-level reconstruction to complex scenes, advancing interactive scene reconstruction.
Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models
We cast the dynamics learning as an auto-regressive sequence modeling problem over discrete tokens by leveraging the expressive Transformer architecture, in order to model complex local dynamics across different agents and provide accurate and consistent long-term imaginations.
Learning Manipulation by Predicting Interaction
To this end, we propose a general pre-training pipeline that learns Manipulation by Predicting the Interaction (MPI) and enhances the visual representation. Given a pair of keyframes representing the initial and final states, along with language instructions, our algorithm predicts the transition frame and detects the interaction object, respectively.
SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation
In this paper, we propose SAM-E, a novel architecture for robot manipulation by leveraging a vision-foundation model for generalizable scene understanding and sequence imitation for long-term action reasoning.
CrossMatch: Enhance Semi-Supervised Medical Image Segmentation with Perturbation Strategies and Knowledge Distillation
Semi-supervised learning for medical image segmentation presents a unique challenge of efficiently using limited labeled data while leveraging abundant unlabeled data.
Pessimistic Value Iteration for Multi-Task Data Sharing in Offline Reinforcement Learning
We further provide theoretical analysis, which shows that the optimality gap of our method is only related to the expected data coverage of the shared dataset, thus resolving the distribution shift issue in data sharing.
S3-SLAM: Sparse Tri-plane Encoding for Neural Implicit SLAM
With the emergence of Neural Radiance Fields (NeRF), neural implicit representations have gained widespread applications across various domains, including simultaneous localization and mapping.
Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding
The adapter incorporates prior spatial knowledge from the source modality to guide the local feature aggregation of 3D tokens, compelling the semantic adaption of any-modality transformers.
HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation
In this paper, we propose a novel hybrid pseudo-labeling framework for unsupervised event-based semantic segmentation, HPL-ESS, to alleviate the influence of noisy pseudo labels.
Learning an Actionable Discrete Diffusion Policy via Large-Scale Actionless Video Pre-Training
In the pre-training stage, we employ a discrete diffusion model with a mask-and-replace diffusion strategy to predict future video tokens in the latent space.
Motion-Aware Video Frame Interpolation
Subsequently, a cross-scale motion structure is presented to estimate and refine intermediate flow maps by the extracted features.
Cyclic Learning for Binaural Audio Generation and Localization
Visual sounding object localization establishes the correspondence between specific visual objects and sound modalities which provides object-aware guidance to improve binaural generation performance.
X4D-SceneFormer: Enhanced Scene Understanding on 4D Point Cloud Videos through Cross-modal Knowledge Transfer
The field of 4D point cloud understanding is rapidly developing with the goal of analyzing dynamic 3D point cloud sequences.
Calibration-free quantitative phase imaging in multi-core fiber endoscopes using end-to-end deep learning
Quantitative phase imaging (QPI) through multi-core fibers (MCFs) has been an emerging in vivo label-free endoscopic imaging modality with minimal invasiveness.
GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting
This strategy is essential to extend 3D Gaussian representation to reconstruct the whole scene rather than synthesize a static object in existing methods.
Implicit Event-RGBD Neural SLAM
To address these challenges, we propose EN-SLAM, the first event-RGBD implicit neural SLAM framework, which effectively leverages the high rate and high dynamic range advantages of event data for tracking and mapping.
Cross-modal Generative Model for Visual-Guided Binaural Stereo Generation
To this end, a metric to measure the spatial perception of audio is proposed for the first time.
Kinematic-aware Prompting for Generalizable Articulated Object Manipulation with LLMs
Generalizable articulated object manipulation is essential for home-assistant robots.
Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained Models
To this end, we introduce Point-PEFT, a novel framework for adapting point cloud pre-trained models with minimal learnable parameters.
Disentangled Contrastive Image Translation for Nighttime Surveillance
Targeting the surveillance scenes, we develop a disentangled representation, which is an auxiliary pretext task that separates surveillance scenes into the foreground and background with contrastive learning.
Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning
Specifically, we propose Multi-Task Diffusion Model (\textsc{MTDiff}), a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis in multi-task offline settings.
On the Value of Myopic Behavior in Policy Reuse
Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence.
Behavior Contrastive Learning for Unsupervised Skill Discovery
Under mild assumptions, our objective maximizes the MI between different behaviors based on the same skill, which serves as an upper bound of the previous MI objective.
One-Shot High-Fidelity Talking-Head Synthesis with Deformable Neural Radiance Field
Talking head generation aims to generate faces that maintain the identity information of the source image and imitate the motion of the driving image.
Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement
The popularity of Contrastive Language-Image Pre-training (CLIP) has propelled its application to diverse downstream vision tasks.
Towards Nonlinear-Motion-Aware and Occlusion-Robust Rolling Shutter Correction
This paper addresses the problem of rolling shutter correction in complex nonlinear and dynamic scenes with extreme occlusion.
ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance
In this paper, we propose ViewRefer, a multi-view framework for 3D visual grounding exploring how to grasp the view knowledge from both text and 3D modalities.
Propagate And Calibrate: Real-time Passive Non-line-of-sight Tracking
In contrast, we propose a purely passive method to track a person walking in an invisible room by only observing a relay wall, which is more in line with real application scenarios, e. g., security.
Fully Self-Supervised Depth Estimation from Defocus Clue
We show that our framework circumvents the needs for the depth and AIF image ground-truth, and receives superior predictions, thus closing the gap between the theoretical success of DFD works and their applications in the real world.
ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding
In this paper, we propose ViewRefer, a multi-view framework for 3D visual grounding exploring how to grasp the view knowledge from both text and 3D modalities.
Low-Light Hyperspectral Image Enhancement
The illumination enhancement branch is adopted to enlighten the low-frequency component with reduced resolution.
RCLane: Relay Chain Prediction for Lane Detection
This is because most of the existing lane detection methods either treat the lane detection as a dense prediction or a detection task, few of them consider the unique topologies (Y-shape, Fork-shape, nearly horizontal lane) of the lane markers, which leads to sub-optimal solution.
Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
By fine-tuning on downstream tasks, Point-M2AE achieves 86. 43% accuracy on ScanObjectNN, +3. 36% to the second-best, and largely benefits the few-shot classification, part segmentation and 3D object detection with the hierarchical pre-training scheme.
Ranked #5 on
3D Point Cloud Linear Classification
on ModelNet40
(using extra training data)
Community detection in censored hypergraph
In practice, a network may has censored (or missing) values and it is shown that censored values have non-negligible effect on the structural properties of a network.
Hierarchical Multimodal Transformer to Summarize Videos
To integrate the two kinds of information, they are encoded in a two-stream scheme, and a multimodal fusion mechanism is developed based on the hierarchical transformer.
Ranked #17 on
Supervised Video Summarization
on TvSum
Audio-Visual Collaborative Representation Learning for Dynamic Saliency Prediction
Motivated by this, an audio-visual collaborative representation learning method is proposed for the DSP task, which explores the audio modality to better predict the dynamic saliency map by assisting vision modality.
Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark
Video crowd localization is a crucial yet challenging task, which aims to estimate exact locations of human heads in the given crowded videos.
PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery
Unlike convolutions which are inherently parallel, the de-facto standard for NMS, namely GreedyNMS, cannot be easily parallelized and thus could be the performance bottleneck in convolutional object detection pipelines.
AudioVisual Video Summarization
Motivated by this, we propose to jointly exploit the audio and visual information for the video summarization task, and develop an AudioVisual Recurrent Network (AVRN) to achieve this.
EA-Net: Edge-Aware Network for Flow-based Video Frame Interpolation
Specifically, in the flow estimation stage, three edge-aware mechanisms are developed to emphasize the frame edges in estimating flow maps, so that the edge-maps are taken as the auxiliary information to provide more guidance to boost the flow accuracy.
Reconstructive Sequence-Graph Network for Video Summarization
Then, the videos are summarized by exploiting both the local and global dependencies among shots.
Weather GAN: Multi-Domain Weather Translation Using Generative Adversarial Networks
To this end, the generator of Weather GAN is composed of an initial translation module, an attention module and a weather-cue segmentation module.
Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos
This effectively limits the performance and generalization capabilities of existing video segmentation methods.
Automated Segmentation of Brain Gray Matter Nuclei on Quantitative Susceptibility Mapping Using Deep Convolutional Neural Network
Abnormal iron accumulation in the brain subcortical nuclei has been reported to be correlated to various neurodegenerative diseases, which can be measured through the magnetic susceptibility from the quantitative susceptibility mapping (QSM).
Automatic acute ischemic stroke lesion segmentation using semi-supervised learning
By using a large number of weakly labeled subjects and a small number of fully labeled subjects, our proposed method is able to accurately detect and segment the AIS lesions.
Travel Time Estimation without Road Networks: An Urban Morphological Layout Representation Approach
Travel time estimation is a crucial task for not only personal travel scheduling but also city planning.
C^3 Framework: An Open-source PyTorch Code for Crowd Counting
This technical report attempts to provide efficient and solid kits addressed on the field of crowd counting, which is denoted as Crowd Counting Code Framework (C$^3$F).
Hierarchical Recurrent Neural Network for Video Summarization
Compared to traditional RNNs, H-RNN is more suitable to video summarization, since it can exploit long temporal dependency among frames, meanwhile, the computation operations are significantly lessened.
A General Framework for Edited Video and Raw Video Summarization
Besides, the property-weights are learned for edited videos and raw videos, respectively.
A CNN-RNN Architecture for Multi-Label Weather Recognition
To address this problem, we make the first attempt to view weather recognition as a multi-label classification task, i. e., assigning an image more than one labels according to the displayed weather conditions.
Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks
This paper addresses a challenging problem - how to reduce energy consumption without incurring performance drop when deploying deep neural networks (DNNs) at the inference stage.
HSA-RNN: Hierarchical Structure-Adaptive RNN for Video Summarization
Although video summarization has achieved great success in recent years, few approaches have realized the influence of video structure on the summarization results.
Hierarchical Feature Hashing for Fast Dimensionality Reduction
Curse of dimensionality is a practical and challenging problem in image categorization, especially in cases with a large number of classes.
Quasi Real-Time Summarization for Consumer Videos
With the widespread availability of video cameras, we are facing an ever-growing enormous collection of unedited and unstructured video data.
Sparse Output Coding for Large-Scale Visual Recognition
Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands.
Large-Scale Category Structure Aware Image Categorization
With the emergence of structured large-scale dataset such as the ImageNet, rich information about the conceptual relationships between images, such as a tree hierarchy among various image categories, become available.
