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Found 20 papers, 10 papers with code
Face-voice Association in Multilingual Environments (FAME) Challenge 2024 Evaluation Plan
The Face-voice Association in Multilingual Environments (FAME) Challenge 2024 focuses on exploring face-voice association under a unique condition of multilingual scenario.
DCTM: Dilated Convolutional Transformer Model for Multimodal Engagement Estimation in Conversation
Conversational engagement estimation is posed as a regression problem, entailing the identification of the favorable attention and involvement of the participants in the conversation.
Single-branch Network for Multimodal Training
With the rapid growth of social media platforms, users are sharing billions of multimedia posts containing audio, images, and text.
Speaker Recognition in Realistic Scenario Using Multimodal Data
To achieve this task, we proposed a two-branch network to learn joint representations of faces and voices in a multimodal system.
Learning Branched Fusion and Orthogonal Projection for Face-Voice Association
In addition, we leverage cross-modal verification and matching tasks to analyze the impact of multiple languages on face-voice association.
Guiding Attention using Partial-Order Relationships for Image Captioning
A pairwise ranking objective is used for training this embedding space which allows similar images, topics and captions in the shared semantic space to maintain a partial order in the visual-semantic hierarchy and hence, helps the model to produce more visually accurate captions.
Semantically Grounded Visual Embeddings for Zero-Shot Learning
Zero-shot learning methods rely on fixed visual and semantic embeddings, extracted from independent vision and language models, both pre-trained for other large-scale tasks.
Fusion and Orthogonal Projection for Improved Face-Voice Association
Prior works adopt pairwise or triplet loss formulations to learn an embedding space amenable for associated matching and verification tasks.
Visual Word Embedding for Text Classification
The question we answer with this paper is: ‘can we convert a text document into an image to take advantage of image neural models to classify text documents?’ To answer this question we present a novel text classification method that converts a document into an encoded image, using word embedding.
Cross-modal Speaker Verification and Recognition: A Multilingual Perspective
Recent years have seen a surge in finding association between faces and voices within a cross-modal biometric application along with speaker recognition.
Are These Birds Similar: Learning Branched Networks for Fine-grained Representations
In recent years, natural language descriptions are used to obtain information on discriminative parts of the object.
Ranked #1 on
Multi-Modal Document Classification
on CUB-200-2011
Deep Latent Space Learning for Cross-modal Mapping of Audio and Visual Signals
We quantitatively and qualitatively evaluate the proposed approach on VoxCeleb, a benchmarks audio-visual dataset on a multitude of tasks including cross-modal verification, cross-modal matching, and cross-modal retrieval.
Picture What you Read
Visualization refers to our ability to create an image in our head based on the text we read or the words we hear.
Do Cross Modal Systems Leverage Semantic Relationships?
Our proposed measure evaluates the semantic similarity between the image and text representations in the latent embedding space.
Aiding Intra-Text Representations with Visual Context for Multimodal Named Entity Recognition
With massive explosion of social media such as Twitter and Instagram, people daily share billions of multimedia posts, containing images and text.
Learning Inward Scaled Hypersphere Embedding: Exploring Projections in Higher Dimensions
Majority of the current dimensionality reduction or retrieval techniques rely on embedding the learned feature representations onto a computable metric space.
Image and Encoded Text Fusion for Multi-Modal Classification
To learn feature representations of resulting images, standard Convolutional Neural Networks (CNNs) are employed for the classification task.
Seeing Colors: Learning Semantic Text Encoding for Classification
The question we answer with this work is: can we convert a text document into an image to exploit best image classification models to classify documents?
Git Loss for Deep Face Recognition
Conventionally, CNNs have been trained with softmax as supervision signal to penalize the classification loss.
Ranked #8 on
Face Verification
on YouTube Faces DB
Revisiting Cross Modal Retrieval
We show that text encodings can capture semantic relationships between multiple modalities.
