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Found 58 papers, 27 papers with code
Adaptive Unsupervised Self-training for Disfluency Detection
Supervised methods have achieved remarkable results in disfluency detection.
Table Fact Verification with Structure-Aware Transformer
Verifying fact on semi-structured evidence like tables requires the ability to encode structural information and perform symbolic reasoning.
Improving Prosody for Cross-Speaker Style Transfer by Semi-Supervised Style Extractor and Hierarchical Modeling in Speech Synthesis
Cross-speaker style transfer in speech synthesis aims at transferring a style from source speaker to synthesized speech of a target speaker's timbre.
LSTFE-Net:Long Short-Term Feature Enhancement Network for Video Small Object Detection
We find that context information from the long-term frame and temporal information from the short-term frame are two useful cues for video small object detection.
Implicit Identity Driven Deepfake Face Swapping Detection
Face swapping aims to replace the target face with the source face and generate the fake face that the human cannot distinguish between real and fake.
Style-Label-Free: Cross-Speaker Style Transfer by Quantized VAE and Speaker-wise Normalization in Speech Synthesis
In order to improve the style extraction ability of the reference encoder, a style invariant and contrastive data augmentation method is proposed.
A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information
Image super-resolution (SR) is a technique to recover lost high-frequency information in low-resolution (LR) images.
Ranking Aggregation with Interactive Feedback for Collaborative Person Re-identification
Existing RA work can be generally divided into unsupervised methods and fully-supervised methods.
A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset
Furthermore, based on this dataset, we propose an end-to-end model that jointly optimizes the video understanding objective with knowledge graph embedding, which can not only better inject factual knowledge into video understanding but also generate effective multi-modal entity embedding for KG.
Back-Translation-Style Data Augmentation for Mandarin Chinese Polyphone Disambiguation
In this paper we propose a simple back-translation-style data augmentation method for mandarin Chinese polyphone disambiguation, utilizing a large amount of unlabeled text data.
Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia
In this paper, we propose Kuaipedia, a large-scale multi-modal encyclopedia consisting of items, aspects, and short videos lined to them, which was extracted from billions of videos of Kuaishou (Kwai), a well-known short-video platform in China.
RaP: Redundancy-aware Video-language Pre-training for Text-Video Retrieval
Sparse sampling is also likely to miss important frames corresponding to some text portions, resulting in textual redundancy.
Bridging CLIP and StyleGAN through Latent Alignment for Image Editing
More specifically, our efforts consist of three parts: 1) a data-free training strategy to train latent mappers to bridge the latent space of CLIP and StyleGAN; 2) for more precise mapping, temporal relative consistency is proposed to address the knowledge distribution bias problem among different latent spaces; 3) to refine the mapped latent in s space, adaptive style mixing is also proposed.
InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings
Contrastive learning has been extensively studied in sentence embedding learning, which assumes that the embeddings of different views of the same sentence are closer.
TokenFlow: Rethinking Fine-grained Cross-modal Alignment in Vision-Language Retrieval
Most existing methods in vision-language retrieval match two modalities by either comparing their global feature vectors which misses sufficient information and lacks interpretability, detecting objects in images or videos and aligning the text with fine-grained features which relies on complicated model designs, or modeling fine-grained interaction via cross-attention upon visual and textual tokens which suffers from inferior efficiency.
ConTextual Masked Auto-Encoder for Dense Passage Retrieval
Dense passage retrieval aims to retrieve the relevant passages of a query from a large corpus based on dense representations (i. e., vectors) of the query and the passages.
Magic ELF: Image Deraining Meets Association Learning and Transformer
Convolutional neural network (CNN) and Transformer have achieved great success in multimedia applications.
Real-time End-to-End Video Text Spotter with Contrastive Representation Learning
Our contributions are three-fold: 1) CoText simultaneously address the three tasks (e. g., text detection, tracking, recognition) in a real-time end-to-end trainable framework.
Deepfake Face Traceability with Disentangling Reversing Network
Deepfake face not only violates the privacy of personal identity, but also confuses the public and causes huge social harm.
Diagnosing Ensemble Few-Shot Classifiers
The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly affect the model performance.
Augmentation-Aware Self-Supervision for Data-Efficient GAN Training
Training generative adversarial networks (GANs) with limited data is challenging because discriminator is prone to overfitting.
ITTR: Unpaired Image-to-Image Translation with Transformers
Unpaired image-to-image translation is to translate an image from a source domain to a target domain without paired training data.
Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing
A novel Shuffled Style Assembly Network (SSAN) is proposed to extract and reassemble different content and style features for a stylized feature space.
Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks
Before entering the neural network, a token is generally converted to the corresponding one-hot representation, which is a discrete distribution of the vocabulary.
Contrastive Learning of Semantic and Visual Representations for Text Tracking
Semantic representation is of great benefit to the video text tracking(VTT) task that requires simultaneously classifying, detecting, and tracking texts in the video.
DistilCSE: Effective Knowledge Distillation For Contrastive Sentence Embeddings
To tackle that, we propose an effective knowledge distillation framework for contrastive sentence embeddings, termed DistilCSE.
Consistency Regularization for Deep Face Anti-Spoofing
Face anti-spoofing (FAS) plays a crucial role in securing face recognition systems.
whu-nercms at trecvid2021:instance search task
We will make a brief introduction of the experimental methods and results of the WHU-NERCMS in the TRECVID2021 in the paper.
TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network
Recently, face super-resolution (FSR) methods either feed whole face image into convolutional neural networks (CNNs) or utilize extra facial priors (e. g., facial parsing maps, facial landmarks) to focus on facial structure, thereby maintaining the consistency of the facial structure while restoring facial details.
Smoothed Contrastive Learning for Unsupervised Sentence Embedding
Contrastive learning has been gradually applied to learn high-quality unsupervised sentence embedding.
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding
Unsup-SimCSE takes dropout as a minimal data augmentation method, and passes the same input sentence to a pre-trained Transformer encoder (with dropout turned on) twice to obtain the two corresponding embeddings to build a positive pair.
MlTr: Multi-label Classification with Transformer
The task of multi-label image classification is to recognize all the object labels presented in an image.
Ranked #12 on
Multi-Label Classification
on MS-COCO
CAT: Cross Attention in Vision Transformer
In this paper, we propose a new attention mechanism in Transformer termed Cross Attention, which alternates attention inner the image patch instead of the whole image to capture local information and apply attention between image patches which are divided from single-channel feature maps capture global information.
Omniscient Video Super-Resolution
Most recent video super-resolution (SR) methods either adopt an iterative manner to deal with low-resolution (LR) frames from a temporally sliding window, or leverage the previously estimated SR output to help reconstruct the current frame recurrently.
HiT: Hierarchical Transformer with Momentum Contrast for Video-Text Retrieval
Video-Text Retrieval has been a hot research topic with the growth of multimedia data on the internet.
Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement
Different from existing methods tending to accomplish the relighting task directly by ignoring the fidelity and naturalness recovery, we investigate the intrinsic degradation and relight the low-light image while refining the details and color in two steps.
Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection
Face forgery detection is raising ever-increasing interest in computer vision since facial manipulation technologies cause serious worries.
Metric Learning for Anti-Compression Facial Forgery Detection
Detecting facial forgery images and videos is an increasingly important topic in multimedia forensics.
When Face Recognition Meets Occlusion: A New Benchmark
In particular, we first collect a variety of glasses and masks as occlusion, and randomly combine the occlusion attributes (occlusion objects, textures, and colors) to achieve a large number of more realistic occlusion types.
Converse, Focus and Guess -- Towards Multi-Document Driven Dialogue
We propose a novel task, Multi-Document Driven Dialogue (MD3), in which an agent can guess the target document that the user is interested in by leading a dialogue.
Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension
Weakly supervised machine reading comprehension (MRC) task is practical and promising for its easily available and massive training data, but inevitablely introduces noise.
Syntactic Graph Convolutional Network for Spoken Language Understanding
Slot filling and intent detection are two major tasks for spoken language understanding.
Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection
Most existing approaches to disfluency detection heavily rely on human-annotated corpora, which is expensive to obtain in practice.
Face Hallucination via Split-Attention in Split-Attention Network
However, most of them fail to take into account the overall facial profile and fine texture details simultaneously, resulting in reduced naturalness and fidelity of the reconstructed face, and further impairing the performance of downstream tasks (e. g., face detection, facial recognition).
Query-aware Tip Generation for Vertical Search
Existing work on tip generation does not take query into consideration, which limits the impact of tips in search scenarios.
Combining ResNet and Transformer for Chinese Grammatical Error Diagnosis
Grammatical error diagnosis is an important task in natural language processing.
Answer-Driven Visual State Estimator for Goal-Oriented Visual Dialogue
In this paper, we propose an Answer-Driven Visual State Estimator (ADVSE) to impose the effects of different answers on visual states.
Leveraging Historical Interaction Data for Improving Conversational Recommender System
Most of the existing CRS methods focus on learning effective preference representations for users from conversation data alone.
S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
To tackle this problem, we propose the model S^3-Rec, which stands for Self-Supervised learning for Sequential Recommendation, based on the self-attentive neural architecture.
Learning Inverse Rendering of Faces from Real-world Videos
To alleviate this problem, we propose a weakly supervised training approach to train our model on real face videos, based on the assumption of consistency of albedo and normal across different frames, thus bridging the gap between real and synthetic face images.
Multi-Scale Progressive Fusion Network for Single Image Deraining
In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi-scale progressive fusion network (MSPFN) for single image rain streak removal.
Ranked #6 on
Single Image Deraining
on Test2800
Masked Face Recognition Dataset and Application
These datasets are freely available to industry and academia, based on which various applications on masked faces can be developed.
An End-to-End Network for Co-Saliency Detection in One Single Image
Co-saliency detection within a single image is a common vision problem that has received little attention and has not yet been well addressed.
Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis
This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism.
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations.
Ranked #1 on
Recommendation Systems
on Dianping-Food
SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery
(1) Unlike the traditional PCA method based on a whole image, SuperPCA takes into account the diversity in different homogeneous regions, that is, different regions should have different projections.
Probabilistic Prototype Model for Serendipitous Property Mining
Besides providing the relevant information, amusing users has been an important role of the web.
