Search Results for author:
Found 49 papers, 16 papers with code
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.
Large-Scale Actionless Video Pre-Training via Discrete Diffusion for Efficient Policy Learning
In the fine-tuning stage, we harness the imagined future videos to guide low-level action learning trained on a limited set of robot data.
Motion-Aware Video Frame Interpolation
Subsequently, a cross-scale motion structure is presented to estimate and refine intermediate flow maps by the extracted features.
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 #4 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.
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.
