Search Results for author:
Found 37 papers, 15 papers with code
Re-parameterized Low-rank Prompt: Generalize a Vision-Language Model within 0.5K Parameters
On the other hand, complicated structures and update rules largely increase the computation and storage cost.
MACP: Efficient Model Adaptation for Cooperative Perception
We approach this objective by identifying the key challenges of shifting from single-agent to cooperative settings, adapting the model by freezing most of its parameters and adding a few lightweight modules.
Confidence-based Visual Dispersal for Few-shot Unsupervised Domain Adaptation
To address this issue, recent works consider the Few-shot Unsupervised Domain Adaptation (FUDA) where only a few source samples are labeled, and conduct knowledge transfer via self-supervised learning methods.
CAIT: Triple-Win Compression towards High Accuracy, Fast Inference, and Favorable Transferability For ViTs
We further employ a consistent dynamic channel pruning (CDCP) strategy to dynamically prune unimportant channels in ViTs.
Dynamic Causal Disentanglement Model for Dialogue Emotion Detection
Specifically, we propose a dynamic temporal disentanglement model to infer the propagation of utterances and hidden variables, enabling the accumulation of emotion-related information throughout the conversation.
Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion
The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible.
2nd Place Solution for VisDA 2021 Challenge -- Universally Domain Adaptive Image Recognition
The Visual Domain Adaptation (VisDA) 2021 Challenge calls for unsupervised domain adaptation (UDA) methods that can deal with both input distribution shift and label set variance between the source and target domains.
Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies
In this tutorial, we discuss several key aspects of multi-modal emotion recognition (MER).
Multi-Source Domain Adaptation for Object Detection
To reduce annotation labor associated with object detection, an increasing number of studies focus on transferring the learned knowledge from a labeled source domain to another unlabeled target domain.
Affective Image Content Analysis: Two Decades Review and New Perspectives
Images can convey rich semantics and induce various emotions in viewers.
Spatio-temporal Contrastive Domain Adaptation for Action Recognition
Unsupervised domain adaptation (UDA) for human action recognition is a practical and challenging problem.
Computational Emotion Analysis From Images: Recent Advances and Future Directions
Recently, extensive research efforts have been dedicated to understanding the emotions of images.
Domain-Invariant Disentangled Network for Generalizable Object Detection
By disentangling representations on both image and instance levels, DIDN is able to learn domain-invariant representations that are suitable for generalized object detection.
Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation
First, we generate an adapted domain to align the source and target domains on the pixel-level by improving CycleGAN with a multi-scale structured cycle-consistency loss.
Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources
C-CycleGAN transfers source samples at instance-level to an intermediate domain that is closer to the target domain with sentiment semantics preserved and without losing discriminative features.
ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation
They require prior knowledge of real-world statistics and ignore the pixel-level dropout noise gap and the spatial feature gap between different domains.
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.
Emotion-Based End-to-End Matching Between Image and Music in Valence-Arousal Space
In this paper, we study end-to-end matching between image and music based on emotions in the continuous valence-arousal (VA) space.
Rethinking Distributional Matching Based Domain Adaptation
In this paper, in order to devise robust DA algorithms, we first systematically analyze the limitations of DM based methods, and then build new benchmarks with more realistic domain shifts to evaluate the well-accepted DM methods.
Personalized Image Aesthetics Assessment via Meta-Learning With Bilevel Gradient Optimization
Typical image aesthetics assessment (IAA) is modeled for the generic aesthetics perceived by an ``average'' user.
Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Survey
Therefore, transferring the learned knowledge from a separate, labeled source domain to an unlabeled or sparsely labeled target domain becomes an appealing alternative.
MADAN: Multi-source Adversarial Domain Aggregation Network for Domain Adaptation
Finally, feature-level alignment is performed between the aggregated domain and the target domain while training the task network.
An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos
Emotion recognition in user-generated videos plays an important role in human-centered computing.
Ranked #4 on
Video Emotion Recognition
on Ekman6
Multi-source Domain Adaptation for Visual Sentiment Classification
Existing domain adaptation methods on visual sentiment classification typically are investigated under the single-source scenario, where the knowledge learned from a source domain of sufficient labeled data is transferred to the target domain of loosely labeled or unlabeled data.
Multi-source Distilling Domain Adaptation
Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA).
Domain Adaptation
Multi-Source Unsupervised Domain Adaptation
Multi-source Domain Adaptation for Semantic Segmentation
In this paper, we propose to investigate multi-source domain adaptation for semantic segmentation.
Ranked #2 on
Domain Adaptation
on GTA5+Synscapes to Cityscapes
Sketch-Specific Data Augmentation for Freehand Sketch Recognition
Conventional methods for this task often rely on the availability of the temporal order of sketch strokes, additional cues acquired from different modalities and supervised augmentation of sketch datasets with real images, which also limit the applicability and feasibility of these methods in real scenarios.
Affective Computing for Large-Scale Heterogeneous Multimedia Data: A Survey
Affective computing (AC) of these data can help to understand human behaviors and enable wide applications.
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression
By optimizing the PCR loss, PDANet can generate a polarity preserved attention map and thus improve the emotion regression performance.
Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Data
To this end, we propose a new approach of domain randomization and pyramid consistency to learn a model with high generalizability.
A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization
State-of-the-art studies have demonstrated the superiority of joint modelling over pipeline implementation for medical named entity recognition and normalization due to the mutual benefits between the two processes.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
A Novel Domain Adaptation Framework for Medical Image Segmentation
Our biophysics based domain adaptation achieves better results, as compared to the existing state-of-the-art GAN model used to create synthetic data for training.
SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud
When training our new model on synthetic data using the proposed domain adaptation pipeline, we nearly double test accuracy on real-world data, from 29. 0% to 57. 4%.
Ranked #21 on
Robust 3D Semantic Segmentation
on SemanticKITTI-C
Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld
To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled target domain.
SqueezeNext: Hardware-Aware Neural Network Design
One of the main barriers for deploying neural networks on embedded systems has been large memory and power consumption of existing neural networks.
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
Neural networks rely on convolutions to aggregate spatial information.
