Search Results for author:
Found 34 papers, 16 papers with code
Character-Preserving Coherent Story Visualization
Different from video generation that focuses on maintaining the continuity of generated images (frames), story visualization emphasizes preserving the global consistency of characters and scenes across different story pictures, which is very challenging since story sentences only provide sparse signals for generating images.
Ranked #2 on
Story Visualization
on Pororo
(using extra training data)
EmoVIT: Revolutionizing Emotion Insights with Visual Instruction Tuning
Visual Instruction Tuning represents a novel learning paradigm involving the fine-tuning of pre-trained language models using task-specific instructions.
Training-and-prompt-free General Painterly Harmonization Using Image-wise Attention Sharing
To surmount these hurdles, we design a Training-and-prompt-Free General Painterly Harmonization method using image-wise attention sharing (TF-GPH), which integrates a novel "share-attention module".
Lightweight Deep Learning for Resource-Constrained Environments: A Survey
Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing.
MonoTAKD: Teaching Assistant Knowledge Distillation for Monocular 3D Object Detection
Subsequently, we introduce the cross-modal residual distillation to transfer the 3D spatial cues.
DQ-DETR: DETR with Dynamic Query for Tiny Object Detection
Despite previous DETR-like methods having performed successfully in generic object detection, tiny object detection is still a challenging task for them since the positional information of object queries is not customized for detecting tiny objects, whose scale is extraordinarily smaller than general objects.
An Improved Traditional Chinese Evaluation Suite for Foundation Model
We included benchmark results in TMMLU+ from closed-source models and 24 open-weight Chinese large language models of parameters ranging from 1. 8B to 72B.
SINC: Self-Supervised In-Context Learning for Vision-Language Tasks
To this end, we raise a question: ``How can we enable in-context learning without relying on the intrinsic in-context ability of large language models?".
Shilling Black-box Review-based Recommender Systems through Fake Review Generation
Review-Based Recommender Systems (RBRS) have attracted increasing research interest due to their ability to alleviate well-known cold-start problems.
SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization
The generated summaries could therefore be constrained by the preference bias in the training set, especially under low-resource settings.
Size Does Matter: Size-aware Virtual Try-on via Clothing-oriented Transformation Try-on Network
COTTON leverages clothing structure with landmarks and segmentation to design a novel landmark-guided transformation for precisely deforming clothes, allowing for size adjustment during try-on.
Most Important Person-guided Dual-branch Cross-Patch Attention for Group Affect Recognition
Then, the Cross-Patch Attention module is proposed to fuse the features of MIP and global context together to complement each other.
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices.
Vision Transformers: State of the Art and Research Challenges
Transformers have achieved great success in natural language processing.
Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness
A notable challenge in Multi-Document Summarization (MDS) is the extremely-long length of the input.
ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton
The increasing demand for analyzing the insights in sports has stimulated a line of productive studies from a variety of perspectives, e. g., health state monitoring, outcome prediction.
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial Attack
To improve efficiency, we introduce the limit-aware random gradient-free estimation and the gradient sliding mechanism to estimate the gradient that adheres to the adversarial limit, i. e., the pixel value limitations of the adversarial example.
Live Multi-Streaming and Donation Recommendations via Coupled Donation-Response Tensor Factorization
In contrast to traditional online videos, live multi-streaming supports real-time social interactions between multiple streamers and viewers, such as donations.
Mask or Non-Mask? Robust Face Mask Detector via Triplet-Consistency Representation Learning
To automate the monitoring process, one of the promising solutions is to leverage existing object detection models to detect the faces with or without masks.
Gradient Normalization for Generative Adversarial Networks
In this paper, we propose a novel normalization method called gradient normalization (GN) to tackle the training instability of Generative Adversarial Networks (GANs) caused by the sharp gradient space.
Technical Report for Valence-Arousal Estimation in ABAW2 Challenge
In this work, we describe our method for tackling the valence-arousal estimation challenge from ABAW2 ICCV-2021 Competition.
Reproducibility Companion Paper: Knowledge Enhanced Neural Fashion Trend Forecasting
This companion paper supports the replication of the fashion trend forecasting experiments with the KERN (Knowledge Enhanced Recurrent Network) method that we presented in the ICMR 2020.
ZYELL-NCTU NetTraffic-1.0: A Large-Scale Dataset for Real-World Network Anomaly Detection
Network security has been an active research topic for long.
Meta-Transfer Learning for Low-Resource Abstractive Summarization
Neural abstractive summarization has been studied in many pieces of literature and achieves great success with the aid of large corpora.
Template-Free Try-on Image Synthesis via Semantic-guided Optimization
Afterward, given an in-shop clothing image, a user image, and a synthesized pose, we propose a novel model for synthesizing a human try-on image with the target clothing in the best fitting pose.
Spatiotemporal Dilated Convolution with Uncertain Matching for Video-based Crowd Estimation
In this paper, we propose a novel SpatioTemporal convolutional Dense Network (STDNet) to address the video-based crowd counting problem, which contains the decomposition of 3D convolution and the 3D spatiotemporal dilated dense convolution to alleviate the rapid growth of the model size caused by the Conv3D layer.
FashionMirror: Co-Attention Feature-Remapping Virtual Try-On With Sequential Template Poses
In the second stage, we first remove the clothes on the source human via the removed mask and warp the clothing features conditioning on the try-on clothing mask to fit the next frame human.
An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge
Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication.
Micro Expression Recognition
Micro-Expression Recognition
+2
Domain-Adaptive Object Detection via Uncertainty-Aware Distribution Alignment
Domain adaptation aims to transfer knowledge from the sourcedata with annotations to scarcely-labeled data in the target domain, which has attracted a lot of attention in recent years and facilitatedmany multimedia applications.
MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network
Micro-Expression (ME) is the spontaneous, involuntary movement of a face that can reveal the true feeling.
Optimizing Item and Subgroup Configurations for Social-Aware VR Shopping
Shopping in VR malls has been regarded as a paradigm shift for E-commerce, but most of the conventional VR shopping platforms are designed for a single user.
Data Structures and Algorithms
Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline Generation
To generate inspired headlines, we propose a novel framework called POpularity-Reinforced Learning for inspired Headline Generation (PORL-HG).
Communications and Networking Technologies for Intelligent Drone Cruisers
Therefore, we propose to develop an "Artificial Intelligence (AI) Drone-Cruiser" base station that can help 5G mobile communication systems and beyond quickly recover the network after a disaster and handle the instant communications by the flash crowd.
