Search Results for author:
Found 26 papers, 14 papers with code
Privileged Prior Information Distillation for Image Matting
Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance.
DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models
The commonly-used fast sampler for guided sampling is DDIM, a first-order diffusion ODE solver that generally needs 100 to 250 steps for high-quality samples.
Speech Emotion Recognition via an Attentive Time-Frequency Neural Network
The F-Encoder and T-Encoder model the correlations within frequency bands and time frames, respectively, and they are embedded into a time-frequency joint learning strategy to obtain the time-frequency patterns for speech emotions.
Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling
To address this problem, we adopt a generative approach by decoupling the learned policy into two parts: an expressive generative behavior model and an action evaluation model.
Domain Adaptation with Adversarial Training on Penultimate Activations
We show that this strategy is more efficient and better correlated with the objective of boosting prediction confidence than adversarial training on input images or intermediate features, as used in previous works.
Local Context-Aware Active Domain Adaptation
In this paper, we propose a Local context-aware ADA framework, named LADA, to address this issue.
Prior Knowledge Guided Unsupervised Domain Adaptation
We propose a general rectification module that uses such prior knowledge to refine model generated pseudo labels.
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation
To bridge the gap between Transformer and CNN features, we propose a Trans&CNN Feature Calibration block (TCFC) in the decoder.
3DG-STFM: 3D Geometric Guided Student-Teacher Feature Matching
To the best of our knowledge, 3DG-STFM is the first student-teacher learning method for the local feature matching task.
Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score Matching
To fill up this gap, we show that the negative likelihood of the ODE can be bounded by controlling the first, second, and third-order score matching errors; and we further present a novel high-order denoising score matching method to enable maximum likelihood training of score-based diffusion ODEs.
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
In this work, we propose an exact formulation of the solution of diffusion ODEs.
Situational Perception Guided Image Matting
In this paper, we propose a Situational Perception Guided Image Matting (SPG-IM) method that mitigates subjective bias of matting annotations and captures sufficient situational perception information for better global saliency distilled from the visual-to-textual task.
Safe Self-Refinement for Transformer-based Domain Adaptation
Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source domain to solve tasks on a related unlabeled target domain.
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma
According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS.
Data Augmentation
Weakly supervised Semantic Segmentation
+1
Semantic Distillation Guided Salient Object Detection
Most existing CNN-based salient object detection methods can identify local segmentation details like hair and animal fur, but often misinterpret the real saliency due to the lack of global contextual information caused by the subjectiveness of the SOD task and the locality of convolution layers.
Shuffle Augmentation of Features from Unlabeled Data for Unsupervised Domain Adaptation
Unsupervised Domain Adaptation (UDA), a branch of transfer learning where labels for target samples are unavailable, has been widely researched and developed in recent years with the help of adversarially trained models.
Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation
In this paper, we propose a new framework Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation (DTS-VIBE), to generate 3D human pose and mesh from RGB videos.
Ranked #3 on
3D Human Pose Estimation
on MPI-INF-3DHP
(PA-MPJPE metric)
Virtual Multi-Modality Self-Supervised Foreground Matting for Human-Object Interaction
In this paper, we propose a Virtual Multi-modality Foreground Matting (VMFM) method to learn human-object interactive foreground (human and objects interacted with him or her) from a raw RGB image.
Implicit Normalizing Flows
Through theoretical analysis, we show that the function space of ImpFlow is strictly richer than that of ResFlows.
DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wild
Experimental results show that DFEW is a well-designed and challenging database, and the proposed EC-STFL can promisingly improve the performance of existing spatiotemporal deep neural networks in coping with the problem of dynamic FER in the wild.
Discriminative Multi-modality Speech Recognition
Vision is often used as a complementary modality for audio speech recognition (ASR), especially in the noisy environment where performance of solo audio modality significantly deteriorates.
Ranked #4 on
Audio-Visual Speech Recognition
on LRS3-TED
Learning to Detect Head Movement in Unconstrained Remote Gaze Estimation in the Wild
To better demonstrate the advantage of our methods, we further propose a new benchmark dataset with the most rich distribution of head-gaze combination reflecting real-world scenarios.
VFlow: More Expressive Generative Flows with Variational Data Augmentation
Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations.
Ranked #25 on
Image Generation
on CIFAR-10
(bits/dimension metric)
Dually Supervised Feature Pyramid for Object Detection and Segmentation
Feature pyramid architecture has been broadly adopted in object detection and segmentation to deal with multi-scale problem.
Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling
From traditional Web search engines to virtual assistants and Web accelerators, services that rely on online information need to continually keep track of remote content changes by explicitly requesting content updates from remote sources (e. g., web pages).
Model-based Iterative Restoration for Binary Document Image Compression with Dictionary Learning
Experimental results with a variety of document images demonstrate that our method improves the image quality compared with the observed image, and simultaneously improves the compression ratio.
