Search Results for author:
Found 46 papers, 36 papers with code
Temporal Coherence or Temporal Motion: Which is More Critical for Video-based Person Re-identification?
Specifically, we disentangle the video representation into the temporal coherence and motion parts and randomly change the scale of the temporal motion features as the adversarial noise.
X-3D: Explicit 3D Structure Modeling for Point Cloud Recognition
However, we contend that such implicit high-dimensional structure modeling approch inadequately represents the local geometric structure of point clouds due to the absence of explicit structural information.
Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models
In the realm of vision-language understanding, the proficiency of models in interpreting and reasoning over visual content has become a cornerstone for numerous applications.
Ranked #44 on
Visual Question Answering
on MM-Vet
Generative Multimodal Models are In-Context Learners
The human ability to easily solve multimodal tasks in context (i. e., with only a few demonstrations or simple instructions), is what current multimodal systems have largely struggled to imitate.
Ranked #21 on
Visual Question Answering
on MM-Vet
Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D Prior
Instead of retraining a costly viewpoint-aware model, we study how to fully exploit easily accessible coarse 3D knowledge to enhance the prompts and guide 2D lifting optimization for refinement.
TCOVIS: Temporally Consistent Online Video Instance Segmentation
The core of our method consists of a global instance assignment strategy and a spatio-temporal enhancement module, which improve the temporal consistency of the features from two aspects.
Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models
In this paper, we propose a novel 3D-to-2D generative pre-training method that is adaptable to any point cloud model.
Ranked #6 on
3D Part Segmentation
on ShapeNet-Part
Unleashing Text-to-Image Diffusion Models for Visual Perception
In this paper, we propose VPD (Visual Perception with a pre-trained Diffusion model), a new framework that exploits the semantic information of a pre-trained text-to-image diffusion model in visual perception tasks.
Ranked #7 on
Referring Expression Segmentation
on RefCoCo val
AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers
In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr, which adopts a Transformer encoder-decoder architecture for point cloud completion.
Ranked #2 on
Point Cloud Completion
on ShapeNet
DiffSwap: High-Fidelity and Controllable Face Swapping via 3D-Aware Masked Diffusion
Unlike previous work that relies on carefully designed network architectures and loss functions to fuse the information from the source and target faces, we reformulate the face swapping as a conditional inpainting task, performed by a powerful diffusion model guided by the desired face attributes (e. g., identity and landmarks).
FLAG3D: A 3D Fitness Activity Dataset with Language Instruction
With the continuously thriving popularity around the world, fitness activity analytic has become an emerging research topic in computer vision.
PLOT: Prompt Learning with Optimal Transport for Vision-Language Models
To solve this problem, we propose to apply optimal transport to match the vision and text modalities.
P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting
Nowadays, pre-training big models on large-scale datasets has become a crucial topic in deep learning.
Ranked #18 on
3D Point Cloud Classification
on ScanObjectNN
(using extra training data)
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions
In this paper, we show that the key ingredients behind the vision Transformers, namely input-adaptive, long-range and high-order spatial interactions, can also be efficiently implemented with a convolution-based framework.
Ranked #20 on
Semantic Segmentation
on ADE20K
Dynamic Spatial Sparsification for Efficient Vision Transformers and Convolutional Neural Networks
We extend our method to hierarchical models including CNNs and hierarchical vision Transformers as well as more complex dense prediction tasks that require structured feature maps by formulating a more generic dynamic spatial sparsification framework with progressive sparsification and asymmetric computation for different spatial locations.
SemAffiNet: Semantic-Affine Transformation for Point Cloud Segmentation
Conventional point cloud semantic segmentation methods usually employ an encoder-decoder architecture, where mid-level features are locally aggregated to extract geometric information.
FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment
Most existing action quality assessment methods rely on the deep features of an entire video to predict the score, which is less reliable due to the non-transparent inference process and poor interpretability.
SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation
In this paper, we propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras.
LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection
In this paper, we propose the LiDAR Distillation to bridge the domain gap induced by different LiDAR beams for 3D object detection.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion
Human behavior has the nature of indeterminacy, which requires the pedestrian trajectory prediction system to model the multi-modality of future motion states.
Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement
In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i. e. annotations of object centers).
Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results
Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration.
DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting
In this work, we present a new framework for dense prediction by implicitly and explicitly leveraging the pre-trained knowledge from CLIP.
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers.
Ranked #13 on
Few-Shot 3D Point Cloud Classification
on ModelNet40 5-way (10-shot)
(using extra training data)
3D Point Cloud Linear Classification
Few-Shot 3D Point Cloud Classification
+2
Structure-Preserving Image Super-Resolution
Firstly, we propose SPSR with gradient guidance (SPSR-G) by exploiting gradient maps of images to guide the recovery in two aspects.
NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo
In this work, we present a new multi-view depth estimation method that utilizes both conventional reconstruction and learning-based priors over the recently proposed neural radiance fields (NeRF).
PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers
In this paper, we present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr that adopts a transformer encoder-decoder architecture for point cloud completion.
Ranked #1 on
Point Cloud Completion
on ShapeNet
(Chamfer Distance L2 metric)
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification
Unlike most existing methods that learn visual attention based on conventional likelihood, we propose to learn the attention with counterfactual causality, which provides a tool to measure the attention quality and a powerful supervisory signal to guide the learning process.
Ranked #8 on
Vehicle Re-Identification
on VehicleID Medium
RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection
In particular, we propose to generate random layouts of a scene by making use of the objects in the synthetic CAD dataset and learn the 3D scene representation by applying object-level contrastive learning on two random scenes generated from the same set of synthetic objects.
Group-aware Contrastive Regression for Action Quality Assessment
Assessing action quality is challenging due to the subtle differences between videos and large variations in scores.
Ranked #2 on
Action Quality Assessment
on MTL-AQA
Towards Interpretable Deep Metric Learning with Structural Matching
Our method is model-agnostic, which can be applied to off-the-shelf backbone networks and metric learning methods.
Ranked #16 on
Metric Learning
on CUB-200-2011
Global Filter Networks for Image Classification
Recent advances in self-attention and pure multi-layer perceptrons (MLP) models for vision have shown great potential in achieving promising performance with fewer inductive biases.
Ranked #9 on
Image Classification
on Stanford Cars
(using extra training data)
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
Based on this observation, we propose a dynamic token sparsification framework to prune redundant tokens progressively and dynamically based on the input.
Ranked #3 on
Efficient ViTs
on ImageNet-1K (With LV-ViT-S)
PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds
In this paper, we propose a Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) method to estimate scene flow from point clouds.
MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation
Knowledge Distillation (KD) has been one of the most popu-lar methods to learn a compact model.
Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds
Based on this hypothesis, we propose to learn point cloud representation by bidirectional reasoning between the local structures at different abstraction hierarchies and the global shape without human supervision.
Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark Estimation
In this paper, we propose a deep face super-resolution (FSR) method with iterative collaboration between two recurrent networks which focus on facial image recovery and landmark estimation respectively.
Structure-Preserving Super Resolution with Gradient Guidance
In this paper, we propose a structure-preserving super resolution method to alleviate the above issue while maintaining the merits of GAN-based methods to generate perceptual-pleasant details.
Ranked #46 on
Image Super-Resolution
on Urban100 - 4x upscaling
P$^2$GNet: Pose-Guided Point Cloud Generating Networks for 6-DoF Object Pose Estimation
Humans are able to perform fast and accurate object pose estimation even under severe occlusion by exploiting learned object model priors from everyday life.
Spherical Fractal Convolutional Neural Networks for Point Cloud Recognition
We present a generic, flexible and 3D rotation invariant framework based on spherical symmetry for point cloud recognition.
Ranked #44 on
3D Part Segmentation
on ShapeNet-Part
COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis
There are substantial instructional videos on the Internet, which enables us to acquire knowledge for completing various tasks.
Learning Globally Optimized Object Detector via Policy Gradient
In this paper, we propose a simple yet effective method to learn globally optimized detector for object detection, which is a simple modification to the standard cross-entropy gradient inspired by the REINFORCE algorithm.
Runtime Neural Pruning
In this paper, we propose a Runtime Neural Pruning (RNP) framework which prunes the deep neural network dynamically at the runtime.
Learning Discriminative Aggregation Network for Video-Based Face Recognition
In this paper, we propose a discriminative aggregation network (DAN) for video face recognition, which aims to integrate information from video frames effectively and efficiently.
Attention-Aware Deep Reinforcement Learning for Video Face Recognition
In this paper, we propose an attention-aware deep reinforcement learning (ADRL) method for video face recognition, which aims to discard the misleading and confounding frames and find the focuses of attention in face videos for person recognition.
