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Found 34 papers, 13 papers with code
A CLIP-Hitchhiker's Guide to Long Video Retrieval
Our goal in this paper is the adaptation of image-text models for long video retrieval.
Learning Audio-Video Modalities from Image Captions
To close this gap we propose a new video mining pipeline which involves transferring captions from image captioning datasets to video clips with no additional manual effort.
End-to-end Generative Pretraining for Multimodal Video Captioning
Recent video and language pretraining frameworks lack the ability to generate sentences.
Audio-Visual Synchronisation in the wild
Finally, we set the first benchmark for general audio-visual synchronisation with over 160 diverse classes in the new VGG-Sound Sync video dataset.
Masking Modalities for Cross-modal Video Retrieval
Our proposal is to pre-train a video encoder using all the available video modalities as supervision, namely, appearance, sound, and transcribed speech.
With a Little Help from my Temporal Context: Multimodal Egocentric Action Recognition
We capitalise on the action's temporal context and propose a method that learns to attend to surrounding actions in order to improve recognition performance.
Recognizing Multimodal Entailment
How information is created, shared and consumed has changed rapidly in recent decades, in part thanks to new social platforms and technologies on the web.
Attention Bottlenecks for Multimodal Fusion
Humans perceive the world by concurrently processing and fusing high-dimensional inputs from multiple modalities such as vision and audio.
Ranked #1 on
Audio Classification
on VGGSound
(Top 5 Accuracy metric)
Localizing Visual Sounds the Hard Way
We show that our algorithm achieves state-of-the-art performance on the popular Flickr SoundNet dataset.
Composable Augmentation Encoding for Video Representation Learning
We focus on contrastive methods for self-supervised video representation learning.
Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
Our objective in this work is video-text retrieval - in particular a joint embedding that enables efficient text-to-video retrieval.
Ranked #6 on
Video Retrieval
on DiDeMo
(using extra training data)
Slow-Fast Auditory Streams For Audio Recognition
We propose a two-stream convolutional network for audio recognition, that operates on time-frequency spectrogram inputs.
WiCV 2020: The Seventh Women In Computer Vision Workshop
It provides a voice to a minority (female) group in computer vision community and focuses on increasingly the visibility of these researchers, both in academia and industry.
VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge
We held the second installment of the VoxCeleb Speaker Recognition Challenge in conjunction with Interspeech 2020.
Look Before you Speak: Visually Contextualized Utterances
Leveraging recent advances in multimodal learning, our model consists of a novel co-attentional multimodal video transformer, and when trained on both textual and visual context, outperforms baselines that use textual inputs alone.
Cough Against COVID: Evidence of COVID-19 Signature in Cough Sounds
Testing capacity for COVID-19 remains a challenge globally due to the lack of adequate supplies, trained personnel, and sample-processing equipment.
The End-of-End-to-End: A Video Understanding Pentathlon Challenge (2020)
This report summarizes the results of the first edition of the challenge together with the findings of the participants.
Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos
Despite the recent advances in video classification, progress in spatio-temporal action recognition has lagged behind.
Spot the conversation: speaker diarisation in the wild
Finally, we use this pipeline to create a large-scale diarisation dataset called VoxConverse, collected from 'in the wild' videos, which we will release publicly to the research community.
Condensed Movies: Story Based Retrieval with Contextual Embeddings
Our objective in this work is long range understanding of the narrative structure of movies.
Speech2Action: Cross-modal Supervision for Action Recognition
We train a BERT-based Speech2Action classifier on over a thousand movie screenplays, to predict action labels from transcribed speech segments.
Disentangled Speech Embeddings using Cross-modal Self-supervision
The objective of this paper is to learn representations of speaker identity without access to manually annotated data.
VoxSRC 2019: The first VoxCeleb Speaker Recognition Challenge
The VoxCeleb Speaker Recognition Challenge 2019 aimed to assess how well current speaker recognition technology is able to identify speakers in unconstrained or `in the wild' data.
WiCV 2019: The Sixth Women In Computer Vision Workshop
In this paper we present the Women in Computer Vision Workshop - WiCV 2019, organized in conjunction with CVPR 2019.
Count, Crop and Recognise: Fine-Grained Recognition in the Wild
The goal of this paper is to label all the animal individuals present in every frame of a video.
EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognition
We focus on multi-modal fusion for egocentric action recognition, and propose a novel architecture for multi-modal temporal-binding, i. e. the combination of modalities within a range of temporal offsets.
Ranked #2 on
Egocentric Activity Recognition
on EPIC-KITCHENS-55
Use What You Have: Video Retrieval Using Representations From Collaborative Experts
The rapid growth of video on the internet has made searching for video content using natural language queries a significant challenge.
Ranked #8 on
Video Retrieval
on DiDeMo
Utterance-level Aggregation For Speaker Recognition In The Wild
The objective of this paper is speaker recognition "in the wild"-where utterances may be of variable length and also contain irrelevant signals.
Emotion Recognition in Speech using Cross-Modal Transfer in the Wild
We make the following contributions: (i) we develop a strong teacher network for facial emotion recognition that achieves the state of the art on a standard benchmark; (ii) we use the teacher to train a student, tabula rasa, to learn representations (embeddings) for speech emotion recognition without access to labelled audio data; and (iii) we show that the speech emotion embedding can be used for speech emotion recognition on external benchmark datasets.
Ranked #3 on
Facial Expression Recognition
on FERPlus
(using extra training data)
VoxCeleb2: Deep Speaker Recognition
The objective of this paper is speaker recognition under noisy and unconstrained conditions.
Ranked #1 on
Speaker Verification
on VoxCeleb2
(using extra training data)
Learnable PINs: Cross-Modal Embeddings for Person Identity
We propose and investigate an identity sensitive joint embedding of face and voice.
Seeing Voices and Hearing Faces: Cross-modal biometric matching
We make the following contributions: (i) we introduce CNN architectures for both binary and multi-way cross-modal face and audio matching, (ii) we compare dynamic testing (where video information is available, but the audio is not from the same video) with static testing (where only a single still image is available), and (iii) we use human testing as a baseline to calibrate the difficulty of the task.
From Benedict Cumberbatch to Sherlock Holmes: Character Identification in TV series without a Script
The goal of this paper is the automatic identification of characters in TV and feature film material.
VoxCeleb: a large-scale speaker identification dataset
Our second contribution is to apply and compare various state of the art speaker identification techniques on our dataset to establish baseline performance.
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