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Found 48 papers, 20 papers with code
DialogueEIN: Emotion Interaction Network for Dialogue Affective Analysis
Emotion Recognition in Conversation (ERC) has attracted increasing attention in the affective computing research field.
A Joint Gradient and Loss Based Clustered Federated Learning Design
In particular, our designed clustered FL algorithm must overcome two challenges associated with FL training.
From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models
By simply equipping it with an MLP layer for alignment, DINO surpasses CLIP in fine-grained related perception tasks.
BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation Writing
One is a cascaded approach where outputs (tokens or states) of a separately trained speech recognition system are used as inputs for LLMs, which limits their potential in modeling alignment between speech and text.
Gentopia: A Collaborative Platform for Tool-Augmented LLMs
We present gentopia, an ALM framework enabling flexible customization of agents through simple configurations, seamlessly integrating various language models, task formats, prompting modules, and plugins into a unified paradigm.
How to Detect Unauthorized Data Usages in Text-to-image Diffusion Models
In this paper, we propose a method for detecting such unauthorized data usage by planting injected memorization into the text-to-image diffusion models trained on the protected dataset.
NBMOD: Find It and Grasp It in Noisy Background
In the past few years, researchers have proposed many methods to address the above-mentioned issues and achieved very good results on publicly available datasets such as the Cornell dataset and the Jacquard dataset.
Ranked #1 on
Robotic Grasping
on NBMOD
(using extra training data)
Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats
Secondly, to rearm current security strategies, an finetuning-based deployment mechanism is proposed to transfer learned knowledge into the student model, while minimizing the defense cost.
ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models
Augmented Language Models (ALMs) blend the reasoning capabilities of Large Language Models (LLMs) with tools that allow for knowledge retrieval and action execution.
MMViT: Multiscale Multiview Vision Transformers
Our model encodes different views of the input signal and builds several channel-resolution feature stages to process the multiple views of the input at different resolutions in parallel.
Ethylene Leak Detection Based on Infrared Imaging: A Benchmark
We find that the detection criteria used in infrared imaging ethylene leakage detection research cannot fully reflect real-world production conditions, which is not conducive to evaluate the performance of current image-based target detection methods.
Multi-modal Expression Recognition with Ensemble Method
This paper presents our submission to the Expression Classification Challenge of the fifth Affective Behavior Analysis in-the-wild (ABAW) Competition.
Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting
In order to address this issue, we propose GAS, a \shorten approach that can successfully adapt existing robust AGRs to non-IID settings.
A Composite T60 Regression and Classification Approach for Speech Dereverberation
More specifically, we develop a joint learning approach that uses a composite T60 module and a separate dereverberation module to simultaneously perform reverberation time estimation and dereverberation.
Towards Unsupervised Domain Generalization for Face Anti-Spoofing
In this paper, we propose the first Unsupervised Domain Generalization framework for Face Anti-Spoofing, namely UDG-FAS, which could exploit large amounts of easily accessible unlabeled data to learn generalizable features for enhancing the low-data regime of FAS.
Promoting Semantic Connectivity: Dual Nearest Neighbors Contrastive Learning for Unsupervised Domain Generalization
Then, we propose a novel unsupervised domain generalization approach, namely Dual Nearest Neighbors contrastive learning with strong Augmentation (DN^2A).
Adapting Shortcut With Normalizing Flow: An Efficient Tuning Framework for Visual Recognition
To mitigate the computational and storage demands, recent research has explored Parameter-Efficient Fine-Tuning (PEFT), which focuses on tuning a minimal number of parameters for efficient adaptation.
Improving Training and Inference of Face Recognition Models via Random Temperature Scaling
Intuitively, FR algorithms can benefit from both the estimation of uncertainty and the detection of out-of-distribution (OOD) samples.
CCATMos: Convolutional Context-aware Transformer Network for Non-intrusive Speech Quality Assessment
Speech quality assessment has been a critical component in many voice communication related applications such as telephony and online conferencing.
CodePAD: Sequence-based Code Generation with Pushdown Automaton
CodePAD can leverage existing sequence-based models, and we show that it can achieve 100\% grammatical correctness percentage on these benchmark datasets.
Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation
Existing zero-shot methods fail to align the two modalities of speech and text into a shared semantic space, resulting in much worse performance compared to the supervised ST methods.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
3D-FM GAN: Towards 3D-Controllable Face Manipulation
While concatenating GAN inversion and a 3D-aware, noise-to-image GAN is a straight-forward solution, it is inefficient and may lead to noticeable drop in editing quality.
Court Judgement Labeling Using Topic Modeling and Syntactic Parsing
In regions that practice common law, relevant historical cases are essential references for sentencing.
SdAE: Self-distillated Masked Autoencoder
We also analyze how to build good views for the teacher branch to produce latent representation from the perspective of information bottleneck.
Multi-Task Learning Framework for Emotion Recognition in-the-wild
This paper presents our system for the Multi-Task Learning (MTL) Challenge in the 4th Affective Behavior Analysis in-the-wild (ABAW) competition.
Scalable Model-based Policy Optimization for Decentralized Networked Systems
Reinforcement learning algorithms require a large amount of samples; this often limits their real-world applications on even simple tasks.
CalFAT: Calibrated Federated Adversarial Training with Label Skewness
In this paper, we study the problem of FAT under label skewness, and reveal one root cause of the training instability and natural accuracy degradation issues: skewed labels lead to non-identical class probabilities and heterogeneous local models.
M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database
In this work, we propose a Multi-modal Multi-scene Multi-label Emotional Dialogue dataset, M3ED, which contains 990 dyadic emotional dialogues from 56 different TV series, a total of 9, 082 turns and 24, 449 utterances.
Cultural Vocal Bursts Intensity Prediction
Emotion Recognition
Subspace Nonnegative Matrix Factorization for Feature Representation
Traditional nonnegative matrix factorization (NMF) learns a new feature representation on the whole data space, which means treating all features equally.
Multi-modal Emotion Estimation for in-the-wild Videos
In this paper, we briefly introduce our submission to the Valence-Arousal Estimation Challenge of the 3rd Affective Behavior Analysis in-the-wild (ABAW) competition.
Evolving Transferable Neural Pruning Functions
While most prior works in evolutionary learning aim at directly searching the structure of a network, few attempts have been made on another promising track, channel pruning, which recently has made major headway in designing efficient deep learning models.
Class-Discriminative CNN Compression
We then propose a novel layer-adaptive hierarchical pruning approach, where we use a coarse class discrimination scheme for early layers and a fine one for later layers.
Fully Decentralized Model-based Policy Optimization with Networked Agents
Model-based RL is an effective approach for reducing sample complexity.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation
Emotion recognition in conversation (ERC) is a crucial component in affective dialogue systems, which helps the system understand users' emotions and generate empathetic responses.
Content-Aware GAN Compression
We then propose a novel content-aware method to guide the processes of both pruning and distillation.
Finite generation for valuations computing stability thresholds and applications to K-stability
We prove that on any log Fano pair of dimension $n$ whose stability threshold is less than $\frac{n+1}{n}$, any valuation computing the stability threshold has a finitely generated associated graded ring.
Algebraic Geometry Differential Geometry
Learning Latent Architectural Distribution in Differentiable Neural Architecture Search via Variational Information Maximization
Existing differentiable neural architecture search approaches simply assume the architectural distribution on each edge is independent of each other, which conflicts with the intrinsic properties of architecture.
Emotions in Online Content Diffusion
We apply a partial-linear instrumental variable approach with a double machine learning framework to causally identify the impact of the negative discrete emotions on online content diffusion.
Bridging the Modality Gap for Speech-to-Text Translation
End-to-end speech translation aims to translate speech in one language into text in another language via an end-to-end way.
CASIA's System for IWSLT 2020 Open Domain Translation
This paper describes the CASIA{'}s system for the IWSLT 2020 open domain translation task.
Rethinking Class-Discrimination Based CNN Channel Pruning
To this end, we initiate the first study on the effectiveness of a broad range of discriminant functions on channel pruning.
Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding
Speech-to-text translation (ST), which translates source language speech into target language text, has attracted intensive attention in recent years.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Synchronously Generating Two Languages with Interactive Decoding
In this paper, we introduce a novel interactive approach to translate a source language into two different languages simultaneously and interactively.
End-to-End Speech Translation with Knowledge Distillation
End-to-end speech translation (ST), which directly translates from source language speech into target language text, has attracted intensive attentions in recent years.
Language-Independent Representor for Neural Machine Translation
Current Neural Machine Translation (NMT) employs a language-specific encoder to represent the source sentence and adopts a language-specific decoder to generate target translation.
Unsupervised Learning of Neural Networks to Explain Neural Networks
Given feature maps of a certain conv-layer of the CNN, the explainer performs like an auto-encoder, which first disentangles the feature maps into object-part features and then inverts object-part features back to features of higher conv-layers of the CNN.
Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time
Furthermore, we develop a graph pruning strategy at that leads to an additional 58% improvement in recommendations.
Human Curation and Convnets: Powering Item-to-Item Recommendations on Pinterest
This paper presents Pinterest Related Pins, an item-to-item recommendation system that combines collaborative filtering with content-based ranking.
