Search Results for author:
Found 128 papers, 55 papers with code
Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks
The advancement of Offline Reinforcement Learning (RL) and Offline Multi-Agent Reinforcement Learning (MARL) critically depends on the availability of high-quality, pre-collected offline datasets that represent real-world complexities and practical applications.
CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications
In this paper, we introduce CAS-ViT: Convolutional Additive Self-attention Vision Transformers, to achieve a balance between efficiency and performance in mobile applications.
Ranked #510 on
Image Classification
on ImageNet
Robust Fast Adaptation from Adversarially Explicit Task Distribution Generation
This work has practical implications, particularly in dealing with task distribution shifts in meta-learning, and contributes to theoretical insights in the field.
LLM-Empowered State Representation for Reinforcement Learning
Conventional state representations in reinforcement learning often omit critical task-related details, presenting a significant challenge for value networks in establishing accurate mappings from states to task rewards.
Local Action-Guided Motion Diffusion Model for Text-to-Motion Generation
Specifically, we provide an automated method for reference local action sampling and leverage graph attention networks to assess the guiding weight of each local action in the overall motion synthesis.
Efficient Personalized Text-to-image Generation by Leveraging Textual Subspace
Personalized text-to-image generation has attracted unprecedented attention in the recent few years due to its unique capability of generating highly-personalized images via using the input concept dataset and novel textual prompt.
Technique Report of CVPR 2024 PBDL Challenges
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies.
Spatial Annealing Smoothing for Efficient Few-shot Neural Rendering
In this paper, we introduce an accurate and efficient few-shot neural rendering method named Spatial Annealing smoothing regularized NeRF (SANeRF), which is specifically designed for a pre-filtering-driven hybrid representation architecture.
CompetEvo: Towards Morphological Evolution from Competition
Training an agent to adapt to specific tasks through co-optimization of morphology and control has widely attracted attention.
The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks
In safety-critical applications such as medical imaging and autonomous driving, where decisions have profound implications for patient health and road safety, it is imperative to maintain both high adversarial robustness to protect against potential adversarial attacks and reliable uncertainty quantification in decision-making.
Mesh Denoising Transformer
Next, we propose a dual-stream structure consisting of a Geometric Encoder branch and a Spatial Encoder branch, which jointly encode local geometry details and spatial information to fully explore multimodal information for mesh denoising.
RAPIDFlow: Recurrent Adaptable Pyramids with Iterative Decoding for Efficient Optical Flow Estimation
Extracting motion information from videos with optical flow estimation is vital in multiple practical robot applications.
Ranked #6 on
Optical Flow Estimation
on KITTI 2015
RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications
In robotic vision, a de-facto paradigm is to learn in simulated environments and then transfer to real-world applications, which poses an essential challenge in bridging the sim-to-real domain gap.
SGCNeRF: Few-Shot Neural Rendering via Sparse Geometric Consistency Guidance
Neural Radiance Field (NeRF) technology has made significant strides in creating novel viewpoints.
ParCo: Part-Coordinating Text-to-Motion Synthesis
However, these methods encounter challenges such as the lack of coordination between different part motions and difficulties for networks to understand part concepts.
Ranked #9 on
Motion Synthesis
on HumanML3D
Solution for Point Tracking Task of ICCV 1st Perception Test Challenge 2023
To address this issue, we propose a simple yet effective approach called TAP with confident static points (TAPIR+), which focuses on rectifying the tracking of the static point in the videos shot by a static camera.
SynFog: A Photo-realistic Synthetic Fog Dataset based on End-to-end Imaging Simulation for Advancing Real-World Defogging in Autonomous Driving
To advance research in learning-based defogging algorithms, various synthetic fog datasets have been developed.
Stimulate the Potential of Robots via Competition
It is common for us to feel pressure in a competition environment, which arises from the desire to obtain success comparing with other individuals or opponents.
KP-RED: Exploiting Semantic Keypoints for Joint 3D Shape Retrieval and Deformation
In this paper, we present KP-RED, a unified KeyPoint-driven REtrieval and Deformation framework that takes object scans as input and jointly retrieves and deforms the most geometrically similar CAD models from a pre-processed database to tightly match the target.
FaceChain-SuDe: Building Derived Class to Inherit Category Attributes for One-shot Subject-Driven Generation
In this paper, motivated by object-oriented programming, we model the subject as a derived class whose base class is its semantic category.
Pursuit Winning Strategies for Reach-Avoid Games with Polygonal Obstacles
The pursuers cooperate to protect a convex region from the evaders who try to reach the region.
MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models
To tackle this challenge, we propose a MamMIL framework for WSI classification by cooperating the selective structured state space model (i. e., Mamba) with MIL for the first time, enabling the modeling of instance dependencies while maintaining linear complexity.
VideoElevator: Elevating Video Generation Quality with Versatile Text-to-Image Diffusion Models
Different from conventional T2V sampling (i. e., temporal and spatial modeling), VideoElevator explicitly decomposes each sampling step into temporal motion refining and spatial quality elevating.
Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarios
Monocular depth estimation from RGB images plays a pivotal role in 3D vision.
Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning
Using the measurement, we analyze existing techniques for inverting samples and get some insightful information that inspires a novel loss function to reduce the inconsistency.
On the Dynamics Under the Unhinged Loss and Beyond
In this paper, we introduce the unhinged loss, a concise loss function, that offers more mathematical opportunities to analyze the closed-form dynamics while requiring as few simplifications or assumptions as possible.
Supported Trust Region Optimization for Offline Reinforcement Learning
Offline reinforcement learning suffers from the out-of-distribution issue and extrapolation error.
Enhancing Few-shot CLIP with Semantic-Aware Fine-Tuning
Hence, we propose fine-tuning the parameters of the attention pooling layer during the training process to encourage the model to focus on task-specific semantics.
MOHO: Learning Single-view Hand-held Object Reconstruction with Multi-view Occlusion-Aware Supervision
Previous works concerning single-view hand-held object reconstruction typically rely on supervision from 3D ground-truth models, which are hard to collect in real world.
ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning
When personalized federated learning (FL) meets large foundation models, new challenges arise from various limitations in resources.
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning
Offline multi-agent reinforcement learning is challenging due to the coupling effect of both distribution shift issue common in offline setting and the high dimension issue common in multi-agent setting, making the action out-of-distribution (OOD) and value overestimation phenomenon excessively severe.
Towards Real-World Burst Image Super-Resolution: Benchmark and Method
Despite substantial advances, single-image super-resolution (SISR) is always in a dilemma to reconstruct high-quality images with limited information from one input image, especially in realistic scenarios.
CCD-3DR: Consistent Conditioning in Diffusion for Single-Image 3D Reconstruction
However, such strategies fail to consistently align the denoised point cloud with the given image, leading to unstable conditioning and inferior performance.
U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds
In this paper, we propose U-RED, an Unsupervised shape REtrieval and Deformation pipeline that takes an arbitrary object observation as input, typically captured by RGB images or scans, and jointly retrieves and deforms the geometrically similar CAD models from a pre-established database to tightly match the target.
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization
We propose DIVERSIFY, a general framework, for OOD detection and generalization on dynamic distributions of time series.
WiCo: Win-win Cooperation of Bottom-up and Top-down Referring Image Segmentation
Bottom-up methods are mainly perturbed by Inferior Positive (IP) errors due to the lack of prior object information.
Backdoor Attacks Against Incremental Learners: An Empirical Evaluation Study
Large amounts of incremental learning algorithms have been proposed to alleviate the catastrophic forgetting issue arises while dealing with sequential data on a time series.
Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning
Contrastive learning-based video-language representation learning approaches, e. g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined video-text pairs.
Ranked #8 on
Video Question Answering
on MSRVTT-QA
Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation
Interactive segmentation enables users to segment as needed by providing cues of objects, which introduces human-computer interaction for many fields, such as image editing and medical image analysis.
TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization
To better exploit the potential of pre-trained models in adversarial robustness, this paper focuses on the fine-tuning of an adversarially pre-trained model in various classification tasks.
DiffusionRet: Generative Text-Video Retrieval with Diffusion Model
Existing text-video retrieval solutions are, in essence, discriminant models focused on maximizing the conditional likelihood, i. e., p(candidates|query).
Ranked #15 on
Video Retrieval
on MSVD
Parallel Vertex Diffusion for Unified Visual Grounding
An intuitive materialization of our paradigm is Parallel Vertex Diffusion (PVD) to directly set vertex coordinates as the generation target and use a diffusion model to train and infer.
Guided Depth Map Super-resolution: A Survey
Guided depth map super-resolution (GDSR), which aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image, is a longstanding and fundamental problem, it has attracted considerable attention from computer vision and image processing communities.
UATVR: Uncertainty-Adaptive Text-Video Retrieval
In the refined embedding space, we represent text-video pairs as probabilistic distributions where prototypes are sampled for matching evaluation.
TopoSeg: Topology-Aware Nuclear Instance Segmentation
Experiments on three nuclear instance segmentation datasets justify the superiority of TopoSeg, which achieves state-of-the-art performance.
LaPE: Layer-adaptive Position Embedding for Vision Transformers with Independent Layer Normalization
A typical way to introduce position information is adding the absolute Position Embedding (PE) to patch embedding before entering VTs.
Disjoint Masking with Joint Distillation for Efficient Masked Image Modeling
Masked image modeling (MIM) has shown great promise for self-supervised learning (SSL) yet been criticized for learning inefficiency.
Human Health Indicator Prediction from Gait Video
To address this issue, we analyse the similarity and relationship between pose estimation and health indicator prediction tasks, and then propose a paradigm enabling deep learning for small health indicator datasets by pre-training on the pose estimation task.
Proposal Distribution Calibration for Few-Shot Object Detection
Adapting object detectors learned with sufficient supervision to novel classes under low data regimes is charming yet challenging.
SST: Real-time End-to-end Monocular 3D Reconstruction via Sparse Spatial-Temporal Guidance
Real-time monocular 3D reconstruction is a challenging problem that remains unsolved.
ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentation
Unsupervised foreground-background segmentation aims at extracting salient objects from cluttered backgrounds, where Generative Adversarial Network (GAN) approaches, especially layered GANs, show great promise.
Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation is typically inspired by class activation maps, which serve as pseudo masks with class-discriminative regions highlighted.
Optical Character Recognition (OCR)
Weakly supervised Semantic Segmentation
+1
Learning to Annotate Part Segmentation with Gradient Matching
The success of state-of-the-art deep neural networks heavily relies on the presence of large-scale labelled datasets, which are extremely expensive and time-consuming to annotate.
Distilling Representations from GAN Generator via Squeeze and Span
In recent years, generative adversarial networks (GANs) have been an actively studied topic and shown to successfully produce high-quality realistic images in various domains.
Local Manifold Augmentation for Multiview Semantic Consistency
Multiview self-supervised representation learning roots in exploring semantic consistency across data of complex intra-class variation.
Beyond Instance Discrimination: Relation-aware Contrastive Self-supervised Learning
In this paper, we present relation-aware contrastive self-supervised learning (ReCo) to integrate instance relations, i. e., global distribution relation and local interpolation relation, into the CSL framework in a plug-and-play fashion.
Fuzzy Positive Learning for Semi-supervised Semantic Segmentation
Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations.
Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning
Meanwhile, we show that to achieve $\tilde{O}(\mathrm{poly}(S, A, H)\sqrt{K})$ regret, the number of batches is at least $\Omega\left(H/\log_A(K)+ \log_2\log_2(K) \right)$, which matches our upper bound up to logarithmic terms.
Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation
During the iterative update, the results of depth estimation are compared across cameras and the information of overlapping areas is propagated to the whole depth maps with the help of basis formulation.
Deep Lossy Plus Residual Coding for Lossless and Near-lossless Image Compression
In the lossless mode, the DLPR coding system first performs lossy compression and then lossless coding of residuals.
6D Robotic Assembly Based on RGB-only Object Pose Estimation
Vision-based robotic assembly is a crucial yet challenging task as the interaction with multiple objects requires high levels of precision.
SSP-Pose: Symmetry-Aware Shape Prior Deformation for Direct Category-Level Object Pose Estimation
In this paper, to handle these shortcomings, we propose an end-to-end trainable network SSP-Pose for category-level pose estimation, which integrates shape priors into a direct pose regression network.
RBP-Pose: Residual Bounding Box Projection for Category-Level Pose Estimation
Category-level object pose estimation aims to predict the 6D pose as well as the 3D metric size of arbitrary objects from a known set of categories.
CATRE: Iterative Point Clouds Alignment for Category-level Object Pose Refinement
While category-level 9DoF object pose estimation has emerged recently, previous correspondence-based or direct regression methods are both limited in accuracy due to the huge intra-category variances in object shape and color, etc.
$L_2$BN: Enhancing Batch Normalization by Equalizing the $L_2$ Norms of Features
In this paper, we analyze batch normalization from the perspective of discriminability and find the disadvantages ignored by previous studies: the difference in $l_2$ norms of sample features can hinder batch normalization from obtaining more distinguished inter-class features and more compact intra-class features.
Learning Towards the Largest Margins
One of the main challenges for feature representation in deep learning-based classification is the design of appropriate loss functions that exhibit strong discriminative power.
Prototype-Anchored Learning for Learning with Imperfect Annotations
We verify the effectiveness of PAL on class-imbalanced learning and noise-tolerant learning by extensive experiments on synthetic and real-world datasets.
An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation
With our empirical result obtained from 1, 330 models, we provide the following main observations: 1) ERM combined with data augmentation can achieve state-of-the-art performance if we choose a proper pre-trained model respecting the data property; 2) specialized algorithms further improve the robustness on top of ERM when handling a specific type of distribution shift, e. g., GroupDRO for spurious correlation and CORAL for large-scale out-of-distribution data; 3) Comparing different pre-training modes, architectures and data sizes, we provide novel observations about pre-training on distribution shift, which sheds light on designing or selecting pre-training strategy for different kinds of distribution shifts.
Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detection
Except for the backbone networks, however, other components such as the detector head and the feature pyramid network (FPN) remain trained from scratch, which hinders fully tapping the potential of representation models.
Ranked #5 on
Few-Shot Object Detection
on MS-COCO (30-shot)
CCMB: A Large-scale Chinese Cross-modal Benchmark
In this work, we build a large-scale high-quality Chinese Cross-Modal Benchmark named CCMB for the research community, which contains the currently largest public pre-training dataset Zero and five human-annotated fine-tuning datasets for downstream tasks.
Ranked #3 on
Image Retrieval
on Flickr30k-CN
PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI
Compressed Sensing MRI (CS-MRI) aims at reconstructing de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging, thus presenting two basic issues, i. e., where to sample and how to reconstruct.
Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation
Most existing methods either take the end-to-end supervised learning based manner, where large amounts of pairs of sparse input and dense ground-truth are exploited as supervision information; or treat up-scaling of different scale factors as independent tasks, and have to build multiple networks to handle upsampling with varying factors.
Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies
This paper gives the first polynomial-time algorithm for tabular Markov Decision Processes (MDP) that enjoys a regret bound \emph{independent on the planning horizon}.
Occlusion-Aware Self-Supervised Monocular 6D Object Pose Estimation
6D object pose estimation is a fundamental yet challenging problem in computer vision.
GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting
While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their applications.
Ranked #1 on
6D Pose Estimation
on LineMOD
(Mean ADD-S metric)
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon
A new type of non-invasive attacks emerged recently, which attempt to cast perturbation onto the target by optics based tools, such as laser beam and projector.
Which Style Makes Me Attractive? Interpretable Control Discovery and Counterfactual Explanation on StyleGAN
First, we propose a novel approach to disentangle latent subspace semantics by exploiting existing face analysis models, e. g., face parsers and face landmark detectors.
Towards End-to-End Image Compression and Analysis with Transformers
Meanwhile, we propose a feature aggregation module to fuse the compressed features with the selected intermediate features of the Transformer, and feed the aggregated features to a deconvolutional neural network for image reconstruction.
Deep Attentional Guided Image Filtering
Specifically, we propose an attentional kernel learning module to generate dual sets of filter kernels from the guidance and the target, respectively, and then adaptively combine them by modeling the pixel-wise dependency between the two images.
Improved Fine-Tuning by Better Leveraging Pre-Training Data
The generalization result of using pre-training data shows that the excess risk bound on a target task can be improved when the appropriate pre-training data is included in fine-tuning.
Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits
We study the optimal batch-regret tradeoff for batch linear contextual bandits.
Wasserstein Unsupervised Reinforcement Learning
These pre-trained policies can accelerate learning when endowed with external reward, and can also be used as primitive options in hierarchical reinforcement learning.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data
In this paper, we propose a novel weakly-supervised framework to train a monocular depth estimation network to generate HR depth maps with resolution-mismatched supervision, i. e., the inputs are HR color images and the ground-truth are low-resolution (LR) depth maps.
SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation
Directly regressing all 6 degrees-of-freedom (6DoF) for the object pose (e. g. the 3D rotation and translation) in a cluttered environment from a single RGB image is a challenging problem.
Ranked #1 on
6D Pose Estimation using RGB
on Occlusion LineMOD
Learning with Noisy Labels via Sparse Regularization
In this paper, we theoretically prove that \textbf{any loss can be made robust to noisy labels} by restricting the network output to the set of permutations over a fixed vector.
Physics-Based Iterative Projection Complex Neural Network for Phase Retrieval in Lensless Microscopy Imaging
Specifically, we unfold the iterative process of the alternative projection phase retrieval into a feed-forward neural network, whose layers mimic the processing flow.
Beyond Bounding-Box: Convex-Hull Feature Adaptation for Oriented and Densely Packed Object Detection
Detecting oriented and densely packed objects remains challenging for spatial feature aliasing caused by the intersection of reception fields between objects.
Ranked #34 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Learning Scalable lY=-Constrained Near-Lossless Image Compression via Joint Lossy Image and Residual Compression
To achieve scalable compression with the error bound larger than zero, we derive the probability model of the quantized residual by quantizing the learned probability model of the original residual, instead of training multiple networks.
Asymmetric Loss Functions for Learning with Noisy Labels
Symmetric loss functions are confirmed to be robust to label noise.
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
Multiple instance learning (MIL) is a powerful tool to solve the weakly supervised classification in whole slide image (WSI) based pathology diagnosis.
Ranked #6 on
Multiple Instance Learning
on TCGA
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation
Encouraging progress in few-shot semantic segmentation has been made by leveraging features learned upon base classes with sufficient training data to represent novel classes with few-shot examples.
Ranked #69 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Multiple instance active learning for object detection
Despite the substantial progress of active learning for image recognition, there still lacks an instance-level active learning method specified for object detection.
Ranked #1 on
Active Object Detection
on MS COCO
High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion
Specifically, to effectively extract and combine relevant information from LR depth and HR guidance, we propose a multi-modal attention based fusion (MMAF) strategy for hierarchical convolutional layers, including a feature enhance block to select valuable features and a feature recalibration block to unify the similarity metrics of modalities with different appearance characteristics.
Learning Foreground-Background Segmentation from Improved Layered GANs
Deep learning approaches heavily rely on high-quality human supervision which is nonetheless expensive, time-consuming, and error-prone, especially for image segmentation task.
Learning Scalable $\ell_\infty$-constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compression
To achieve scalable compression with the error bound larger than zero, we derive the probability model of the quantized residual by quantizing the learned probability model of the original residual, instead of training multiple networks.
Reducing Conservativeness Oriented Offline Reinforcement Learning
In offline reinforcement learning, a policy learns to maximize cumulative rewards with a fixed collection of data.
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning
Reward decomposition is a critical problem in centralized training with decentralized execution~(CTDE) paradigm for multi-agent reinforcement learning.
GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation
In this work, we perform an in-depth investigation on both direct and indirect methods, and propose a simple yet effective Geometry-guided Direct Regression Network (GDR-Net) to learn the 6D pose in an end-to-end manner from dense correspondence-based intermediate geometric representations.
Ranked #3 on
6D Pose Estimation using RGB
on Occlusion LineMOD
PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation
6D pose estimation from a single RGB image is a challenging and vital task in computer vision.
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP
With the new confidence sets, we obtain the follow regret bounds: For linear bandits, we obtain an $\tilde{O}(poly(d)\sqrt{1 + \sum_{k=1}^{K}\sigma_k^2})$ data-dependent regret bound, where $d$ is the feature dimension, $K$ is the number of rounds, and $\sigma_k^2$ is the \emph{unknown} variance of the reward at the $k$-th round.
Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition
We study the reinforcement learning problem in the setting of finite-horizon1episodic Markov Decision Processes (MDPs) with S states, A actions, and episode length H. We propose a model-free algorithm UCB-ADVANTAGE and prove that it achieves \tilde{O}(\sqrt{H^2 SAT}) regret where T=KH and K is the number of episodes to play.
Nearly Minimax Optimal Reward-free Reinforcement Learning
In the planning phase, the agent needs to return a near-optimal policy for arbitrary reward functions.
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon
Episodic reinforcement learning generalizes contextual bandits and is often perceived to be more difficult due to long planning horizon and unknown state-dependent transitions.
Robust RGB-based 6-DoF Pose Estimation without Real Pose Annotations
We achieve state of the art performance on LINEMOD, and OccludedLINEMOD in without real-pose setting, even outperforming methods that rely on real annotations during training on Occluded-LINEMOD.
Depth image denoising using nuclear norm and learning graph model
The depth images denoising are increasingly becoming the hot research topic nowadays because they reflect the three-dimensional (3D) scene and can be applied in various fields of computer vision.
Domain Contrast for Domain Adaptive Object Detection
We present Domain Contrast (DC), a simple yet effective approach inspired by contrastive learning for training domain adaptive detectors.
Skill Discovery of Coordination in Multi-agent Reinforcement Learning
Meanwhile it suppresses the empowerment of Z on the state of any single agent by adversarial training.
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity
In this paper we consider the problem of learning an $\epsilon$-optimal policy for a discounted Markov Decision Process (MDP).
Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition
We study the reinforcement learning problem in the setting of finite-horizon episodic Markov Decision Processes (MDPs) with $S$ states, $A$ actions, and episode length $H$.
Self6D: Self-Supervised Monocular 6D Object Pose Estimation
6D object pose estimation is a fundamental problem in computer vision.
Rapid Whole Slide Imaging via Learning-based Two-shot Virtual Autofocusing
Whole slide imaging (WSI) is an emerging technology for digital pathology.
CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning
Contemporary monocular 6D pose estimation methods can only cope with a handful of object instances.
PgNN: Physics-guided Neural Network for Fourier Ptychographic Microscopy
Since the optimal parameters of the PgNN can be derived by minimizing the difference between the model-generated images and real captured angle-varied images corresponding to the same scene, the proposed PgNN can get rid of the problem of massive training data as in traditional supervised methods.
Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function
We present an algorithm based on the \emph{Optimism in the Face of Uncertainty} (OFU) principle which is able to learn Reinforcement Learning (RL) modeled by Markov decision process (MDP) with finite state-action space efficiently.
C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors.
Ranked #15 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
Bi-stream Pose Guided Region Ensemble Network for Fingertip Localization from Stereo Images
To mitigate these limitations and promote further research on hand pose estimation from stereo images, we propose a new large-scale binocular hand pose dataset called THU-Bi-Hand, offering a new perspective for fingertip localization.
SHPR-Net: Deep Semantic Hand Pose Regression From Point Clouds
The semantic segmentation network assigns semantic labels for each point in the point set.
Ranked #7 on
Hand Pose Estimation
on MSRA Hands
DeepIM: Deep Iterative Matching for 6D Pose Estimation
Estimating the 6D pose of objects from images is an important problem in various applications such as robot manipulation and virtual reality.
Ranked #1 on
6D Pose Estimation using RGB
on YCB-Video
Dynamic Filtering with Large Sampling Field for ConvNets
We propose a dynamic filtering strategy with large sampling field for ConvNets (LS-DFN), where the position-specific kernels learn from not only the identical position but also multiple sampled neighbor regions.
A Graphical Social Topology Model for Multi-Object Tracking
Inspired with the social affinity property of moving objects, we propose a Graphical Social Topology (GST) model, which estimates the group dynamics by jointly modeling the group structure and the states of objects using a topological representation.
Fully Convolutional Instance-aware Semantic Segmentation
It inherits all the merits of FCNs for semantic segmentation and instance mask proposal.
Ranked #98 on
Instance Segmentation
on COCO test-dev
Action Recognition with Joint Attention on Multi-Level Deep Features
We propose a novel deep supervised neural network for the task of action recognition in videos, which implicitly takes advantage of visual tracking and shares the robustness of both deep Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN).
Action Recognition In Videos
Temporal Action Localization
+1
Fast and High Quality Highlight Removal from A Single Image
Specifically, this paper derives a normalized dichromatic model for the pixels with identical diffuse color: a unit circle equation of projection coefficients in two subspaces that are orthogonal to and parallel with the illumination, respectively.
Sliding-Window Optimization on an Ambiguity-Clearness Graph for Multi-object Tracking
Multi-object tracking remains challenging due to frequent occurrence of occlusions and outliers.
Efficient Divide-And-Conquer Classification Based on Feature-Space Decomposition
Firstly, we divide the feature space into several subspaces using the decomposition method proposed in this paper.
