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Found 21 papers, 12 papers with code
BootsTAP: Bootstrapped Training for Tracking-Any-Point
To endow models with greater understanding of physics and motion, it is useful to enable them to perceive how solid surfaces move and deform in real scenes.
Helping Hands: An Object-Aware Ego-Centric Video Recognition Model
We demonstrate the performance of the object-aware representations learnt by our model, by: (i) evaluating it for strong transfer, i. e. through zero-shot testing, on a number of downstream video-text retrieval and classification benchmarks; and (ii) by using the representations learned as input for long-term video understanding tasks (e. g. Episodic Memory in Ego4D).
TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement
We present a novel model for Tracking Any Point (TAP) that effectively tracks any queried point on any physical surface throughout a video sequence.
Ranked #1 on
Visual Tracking
on Kinetics
Perception Test: A Diagnostic Benchmark for Multimodal Video Models
We propose a novel multimodal video benchmark - the Perception Test - to evaluate the perception and reasoning skills of pre-trained multimodal models (e. g. Flamingo, SeViLA, or GPT-4).
SuS-X: Training-Free Name-Only Transfer of Vision-Language Models
Contrastive Language-Image Pre-training (CLIP) has emerged as a simple yet effective way to train large-scale vision-language models.
TAP-Vid: A Benchmark for Tracking Any Point in a Video
Generic motion understanding from video involves not only tracking objects, but also perceiving how their surfaces deform and move.
Perception Test: A Diagnostic Benchmark for Multimodal Models
We propose a novel multimodal benchmark – the Perception Test – that aims to extensively evaluate perception and reasoning skills of multimodal models.
Is an Object-Centric Video Representation Beneficial for Transfer?
The model learns a set of object-centric summary vectors for the video, and uses these vectors to fuse the visual and spatio-temporal trajectory 'modalities' of the video clip.
Development of an Enterprise-Grade Contract Understanding System
Contracts are arguably the most important type of business documents.
Temporal Query Networks for Fine-grained Video Understanding
It attends to relevant segments for each query with a temporal attention mechanism, and can be trained using only the labels for each query.
Ranked #12 on
Action Recognition
on Diving-48
Representation Matters: Improving Perception and Exploration for Robotics
Furthermore, the value of each representation is evaluated in terms of three properties: dimensionality, observability and disentanglement.
Adaptive Text Recognition through Visual Matching
In this work, our objective is to address the problems of generalization and flexibility for text recognition in documents.
CrossTransformers: spatially-aware few-shot transfer
In this work, we illustrate how the neural network representations which underpin modern vision systems are subject to supervision collapse, whereby they lose any information that is not necessary for performing the training task, including information that may be necessary for transfer to new tasks or domains.
Compliance Change Tracking in Business Process Services
Compliance officers responsible for maintaining adherence constantly struggle to keep up with the large amount of changes in regulatory requirements.
Self-supervised Learning of Interpretable Keypoints from Unlabelled Videos
We propose KeypointGAN, a new method for recognizing the pose of objects from a single image that for learning uses only unlabelled videos and a weak empirical prior on the object poses.
Unsupervised Learning of Object Keypoints for Perception and Control
In this work we aim to learn object representations that are useful for control and reinforcement learning (RL).
Learning to Read by Spelling: Towards Unsupervised Text Recognition
This work presents a method for visual text recognition without using any paired supervisory data.
Inductive Visual Localisation: Factorised Training for Superior Generalisation
End-to-end trained Recurrent Neural Networks (RNNs) have been successfully applied to numerous problems that require processing sequences, such as image captioning, machine translation, and text recognition.
Unsupervised Learning of Object Landmarks through Conditional Image Generation
We propose a method for learning landmark detectors for visual objects (such as the eyes and the nose in a face) without any manual supervision.
A Deep Generative Framework for Paraphrase Generation
In this paper, we address the problem of generating paraphrases automatically.
Synthetic Data for Text Localisation in Natural Images
In this paper we introduce a new method for text detection in natural images.
Ranked #12 on
Scene Text Detection
on ICDAR 2013
