Search Results for author:
Found 48 papers, 25 papers with code
PromptEVC: Controllable Emotional Voice Conversion with Natural Language Prompts
Experimental results demonstrate that PromptEVC outperforms state-of-the-art controllable EVC methods in emotion conversion, intensity control, mixed emotion synthesis, and prosody manipulation.
Towards Computation- and Communication-efficient Computational Pathology
Despite the impressive performance across a wide range of applications, current computational pathology models face significant diagnostic efficiency challenges due to their reliance on high-magnification whole-slide image analysis.
Page Classification for Print Imaging Pipeline
Digital copiers and printers are widely used nowadays.
GPT-4o System Card
We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.
Ranked #1 on
Visual Question Answering (VQA)
on VLM2-Bench
Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models
Consistency models (CMs) are a powerful class of diffusion-based generative models optimized for fast sampling.
Temporal Label Hierachical Network for Compound Emotion Recognition
The emotion recognition has attracted more attention in recent decades.
Energy-based Model for Accurate Shapley Value Estimation in Interpretable Deep Learning Predictive Modeling
Most existing accelerated estimation methods have to compromise on estimation accuracy with efficiency.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
PAVITS: Exploring Prosody-aware VITS for End-to-End Emotional Voice Conversion
In this paper, we propose Prosody-aware VITS (PAVITS) for emotional voice conversion (EVC), aiming to achieve two major objectives of EVC: high content naturalness and high emotional naturalness, which are crucial for meeting the demands of human perception.
Towards Efficient Exact Optimization of Language Model Alignment
This leads to the same mode-seeking solution, while enables efficient optimization by circumventing the complexities of RL.
Anything in Any Scene: Photorealistic Video Object Insertion
Our proposed general framework encompasses three key processes: 1) integrating a realistic object into a given scene video with proper placement to ensure geometric realism; 2) estimating the sky and environmental lighting distribution and simulating realistic shadows to enhance the light realism; 3) employing a style transfer network that refines the final video output to maximize photorealism.
Speech Swin-Transformer: Exploring a Hierarchical Transformer with Shifted Windows for Speech Emotion Recognition
These segment-level patches are then encoded using a stack of Swin blocks, in which a local window Transformer is utilized to explore local inter-frame emotional information across frame patches of each segment patch.
Improving Speaker-independent Speech Emotion Recognition Using Dynamic Joint Distribution Adaptation
In speaker-independent speech emotion recognition, the training and testing samples are collected from diverse speakers, leading to a multi-domain shift challenge across the feature distributions of data from different speakers.
The Blessing of Randomness: SDE Beats ODE in General Diffusion-based Image Editing
We present a unified probabilistic formulation for diffusion-based image editing, where a latent variable is edited in a task-specific manner and generally deviates from the corresponding marginal distribution induced by the original stochastic or ordinary differential equation (SDE or ODE).
DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model Statistics
In this work, we propose a novel formulation towards the optimal parameterization during sampling that minimizes the first-order discretization error of the ODE solution.
Score Regularized Policy Optimization through Diffusion Behavior
Recent developments in offline reinforcement learning have uncovered the immense potential of diffusion modeling, which excels at representing heterogeneous behavior policies.
Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition
This paper focuses on the research of micro-expression recognition (MER) and proposes a flexible and reliable deep learning method called learning to rank onset-occurring-offset representations (LTR3O).
ChatGPT Informed Graph Neural Network for Stock Movement Prediction
However, its potential for inferring dynamic network structures from temporal textual data, specifically financial news, remains an unexplored frontier.
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i. e., $7. 5$).
Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs
The probability flow ordinary differential equation (ODE) of diffusion models (i. e., diffusion ODEs) is a particular case of continuous normalizing flows (CNFs), which enables deterministic inference and exact likelihood evaluation.
Ranked #1 on
Density Estimation
on CIFAR-10
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning
The main challenge for this setting is that the intermediate guidance during the diffusion sampling procedure, which is jointly defined by the sampling distribution and the energy function, is unknown and is hard to estimate.
On Calibrating Diffusion Probabilistic Models
In this work, we observe that the stochastic reverse process of data scores is a martingale, from which concentration bounds and the optional stopping theorem for data scores can be derived.
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.
Ranked #4 on
Domain Adaptation
on Office-31
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 #53 on
3D Human Pose Estimation
on 3DPW
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.
Ranked #23 on
Dynamic Facial Expression Recognition
on DFEW
Dynamic Facial Expression Recognition
Facial Expression Recognition
+1
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 #11 on
Audio-Visual Speech Recognition
on LRS3-TED
(using extra training data)
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.
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.
