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09/13/2023
TAP: Targeted Prompting for Task Adaptive Generation of Textual Training Instances for Visual Classification
Vision and Language Models (VLMs), such as CLIP, have enabled visual rec...
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05/31/2023
Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models
Vision and Language (VL) models offer an effective method for aligning r...
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05/29/2023
LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and Unlabeled Image Collections
Recently, large-scale pre-trained Vision and Language (VL) models have s...
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05/21/2023
Comparison of Multilingual Self-Supervised and Weakly-Supervised Speech Pre-Training for Adaptation to Unseen Languages
Recent models such as XLS-R and Whisper have made multilingual speech te...
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05/10/2023
Incorporating Structured Representations into Pretrained Vision Language Models Using Scene Graphs
Vision and Language (VL) models have demonstrated remarkable zero-shot p...
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04/02/2023
Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies
Transformations based on domain expertise (expert transformations), such...
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03/30/2023
Going Beyond Nouns With Vision Language Models Using Synthetic Data
Large-scale pre-trained Vision Language (VL) models have shown remar...
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03/15/2023
MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge
Large scale Vision-Language (VL) models have shown tremendous success in...
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03/06/2023
Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning
Prompt tuning, in which a base pretrained model is adapted to each task ...
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03/02/2023
Learning to Grow Pretrained Models for Efficient Transformer Training
Scaling transformers has led to significant breakthroughs in many domain...
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12/08/2022
PromptonomyViT: Multi-Task Prompt Learning Improves Video Transformers using Synthetic Scene Data
Action recognition models have achieved impressive results by incorporat...
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11/25/2022
MAEDAY: MAE for few and zero shot AnomalY-Detection
The goal of Anomaly-Detection (AD) is to identify outliers, or outlying ...
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11/23/2022
CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning
Computer vision models suffer from a phenomenon known as catastrophic fo...
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11/22/2022
On the Transferability of Visual Features in Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) aims to train a classifier that ca...
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11/21/2022
Teaching Structured Vision Language Concepts to Vision Language Models
Vision and Language (VL) models have demonstrated remarkable zero-shot p...
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11/17/2022
ConStruct-VL: Data-Free Continual Structured VL Concepts Learning
Recently, large-scale pre-trained Vision-and-Language (VL) foundation mo...
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10/10/2022
On the Importance of Calibration in Semi-supervised Learning
State-of-the-art (SOTA) semi-supervised learning (SSL) methods have been...
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10/07/2022
C2KD: Cross-Lingual Cross-Modal Knowledge Distillation for Multilingual Text-Video Retrieval
Multilingual text-video retrieval methods have improved significantly in...
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09/12/2022
VL-Taboo: An Analysis of Attribute-based Zero-shot Capabilities of Vision-Language Models
Vision-language models trained on large, randomly collected data had sig...
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09/08/2022
FETA: Towards Specializing Foundation Models for Expert Task Applications
Foundation Models (FMs) have demonstrated unprecedented capabilities inc...
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06/15/2022
Structured Video Tokens @ Ego4D PNR Temporal Localization Challenge 2022
This technical report describes the SViT approach for the Ego4D Point of...
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06/13/2022
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens
Recent action recognition models have achieved impressive results by int...
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12/04/2021
Unsupervised Domain Generalization by Learning a Bridge Across Domains
The ability to generalize learned representations across significantly d...
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11/30/2021
Task2Sim : Towards Effective Pre-training and Transfer from Synthetic Data
Pre-training models on Imagenet or other massive datasets of real images...
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11/28/2021
CHARTER: heatmap-based multi-type chart data extraction
The digital conversion of information stored in documents is a great sou...
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06/14/2021
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data
Most existing works in few-shot learning rely on meta-learning the netwo...
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04/20/2021
Detector-Free Weakly Supervised Grounding by Separation
Nowadays, there is an abundance of data involving images and surrounding...
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03/24/2021
A Broad Study on the Transferability of Visual Representations with Contrastive Learning
Tremendous progress has been made in visual representation learning, not...
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02/10/2021
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition
Temporal modelling is the key for efficient video action recognition. Wh...
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12/30/2020
A Maximal Correlation Approach to Imposing Fairness in Machine Learning
As machine learning algorithms grow in popularity and diversify to many ...
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12/07/2020
Fine-grained Angular Contrastive Learning with Coarse Labels
Few-shot learning methods offer pre-training techniques optimized for ea...
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07/31/2020
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition
Action recognition is an open and challenging problem in computer vision...
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07/17/2020
OnlineAugment: Online Data Augmentation with Less Domain Knowledge
Data augmentation is one of the most important tools in training modern ...
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03/15/2020
StarNet: towards weakly supervised few-shot detection and explainable few-shot classification
In this paper, we propose a new few-shot learning method called StarNet,...
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03/14/2020
TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification
The field of Few-Shot Learning (FSL), or learning from very few (typical...
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12/16/2019
A New Benchmark for Evaluation of Cross-Domain Few-Shot Learning
Recent progress on few-shot learning has largely re-lied on annotated da...
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12/01/2019
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification
Few-Shot Learning (FSL) is a topic of rapidly growing interest. Typicall...
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06/05/2019
Baby steps towards few-shot learning with multiple semantics
Learning from one or few visual examples is one of the key capabilities ...
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02/26/2019
LaSO: Label-Set Operations networks for multi-label few-shot learning
Example synthesis is one of the leading methods to tackle the problem of...
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11/13/2018
Co-regularized Alignment for Unsupervised Domain Adaptation
Deep neural networks, trained with large amount of labeled data, can fai...
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06/12/2018
Delta-encoder: an effective sample synthesis method for few-shot object recognition
Learning to classify new categories based on just one or a few examples ...
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06/12/2018
