b'Rogerio Feris'

research

∙ 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...

0 Sivan Doveh, et al. ∙

research

∙ 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...

0 M. Jehanzeb Mirza, et al. ∙

research

∙ 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...

0 Andrew Rouditchenko, et al. ∙

research

∙ 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...

4 Roei Herzig, et al. ∙

research

∙ 03/30/2023

Going Beyond Nouns With Vision Language Models Using Synthetic Data

Large-scale pre-trained Vision Language (VL) models have shown remar...

0 Paola Cascante-Bonilla, et al. ∙

research

∙ 03/29/2023

What, when, and where? – Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions

Spatio-temporal grounding describes the task of localizing events in spa...

0 Brian Chen, et al. ∙

research

∙ 03/26/2023

Mind the Backbone: Minimizing Backbone Distortion for Robust Object Detection

Building object detectors that are robust to domain shifts is critical f...

0 Kuniaki Saito, et al. ∙

research

∙ 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...

0 Wei Lin, et al. ∙

research

∙ 03/06/2023

Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning

Prompt tuning, in which a base pretrained model is adapted to each task ...

0 Zhen Wang, et al. ∙

research

∙ 03/02/2023

Learning to Grow Pretrained Models for Efficient Transformer Training

Scaling transformers has led to significant breakthroughs in many domain...

0 Peihao Wang, et al. ∙

research

∙ 12/19/2022

Synthetic Pre-Training Tasks for Neural Machine Translation

Pre-training is an effective technique for ensuring robust performance o...

0 Zexue He, et al. ∙

research

∙ 11/29/2022

Procedural Image Programs for Representation Learning

Learning image representations using synthetic data allows training neur...

0 Manel Baradad, et al. ∙

research

∙ 11/27/2022

Exploring Consistency in Cross-Domain Transformer for Domain Adaptive Semantic Segmentation

While transformers have greatly boosted performance in semantic segmenta...

0 Kaihong Wang, et al. ∙

research

∙ 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...

0 James Seale Smith, et al. ∙

research

∙ 11/21/2022

Teaching Structured Vision Language Concepts to Vision Language Models

Vision and Language (VL) models have demonstrated remarkable zero-shot p...

11 Sivan Doveh, et al. ∙

research

∙ 11/17/2022

ConStruct-VL: Data-Free Continual Structured VL Concepts Learning

Recently, large-scale pre-trained Vision-and-Language (VL) foundation mo...

0 James Seale Smith, et al. ∙

research

∙ 10/07/2022

C2KD: Cross-Lingual Cross-Modal Knowledge Distillation for Multilingual Text-Video Retrieval

Multilingual text-video retrieval methods have improved significantly in...

0 Andrew Rouditchenko, et al. ∙

research

∙ 09/08/2022

FETA: Towards Specializing Foundation Models for Expert Task Applications

Foundation Models (FMs) have demonstrated unprecedented capabilities inc...

4 Amit Alfassy, et al. ∙

research

∙ 05/31/2022

VALHALLA: Visual Hallucination for Machine Translation

Designing better machine translation systems by considering auxiliary in...

0 Yi Li, et al. ∙

research

∙ 03/31/2022

SimVQA: Exploring Simulated Environments for Visual Question Answering

Existing work on VQA explores data augmentation to achieve better genera...

0 Paola Cascante-Bonilla, et al. ∙

research

∙ 12/08/2021

Everything at Once – Multi-modal Fusion Transformer for Video Retrieval

Multi-modal learning from video data has seen increased attention recent...

0 Nina Shvetsova, et al. ∙

research

∙ 12/04/2021

Unsupervised Domain Generalization by Learning a Bridge Across Domains

The ability to generalize learned representations across significantly d...

6 Sivan Harary, et al. ∙

research

∙ 11/08/2021

Cascaded Multilingual Audio-Visual Learning from Videos

In this paper, we explore self-supervised audio-visual models that learn...

0 Andrew Rouditchenko, et al. ∙

research

∙ 08/23/2021

Dynamic Network Quantization for Efficient Video Inference

Deep convolutional networks have recently achieved great success in vide...

5 Ximeng Sun, et al. ∙

research

∙ 07/19/2021

Separating Skills and Concepts for Novel Visual Question Answering

Generalization to out-of-distribution data has been a problem for Visual...

2 Spencer Whitehead, et al. ∙

research

∙ 06/23/2021

IA-RED^2: Interpretability-Aware Redundancy Reduction for Vision Transformers

The self-attention-based model, transformer, is recently becoming the le...

0 Bowen Pan, et al. ∙

research

∙ 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...

0 Ashraful Islam, et al. ∙

research

∙ 05/11/2021

AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition

Multi-modal learning, which focuses on utilizing various modalities to i...

0 Rameswar Panda, et al. ∙

research

∙ 05/10/2021

Spoken Moments: Learning Joint Audio-Visual Representations from Video Descriptions

When people observe events, they are able to abstract key information an...

4 Mathew Monfort, et al. ∙

research

∙ 04/26/2021

Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos

Multimodal self-supervised learning is getting more and more attention a...

0 Brian Chen, et al. ∙

research

∙ 04/20/2021

Detector-Free Weakly Supervised Grounding by Separation

Nowadays, there is an abundance of data involving images and surrounding...

7 Assaf Arbelle, et al. ∙

research

∙ 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...

0 Ashraful Islam, et al. ∙

research

∙ 03/02/2021

All at Once Network Quantization via Collaborative Knowledge Transfer

Network quantization has rapidly become one of the most widely used meth...

0 Ximeng Sun, et al. ∙

research

∙ 02/15/2021

VA-RED^2: Video Adaptive Redundancy Reduction

Performing inference on deep learning models for videos remains a challe...

0 Bowen Pan, et al. ∙

research

∙ 02/10/2021

AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition

Temporal modelling is the key for efficient video action recognition. Wh...

0 Yue Meng, et al. ∙

research

∙ 02/04/2021

Semi-Supervised Action Recognition with Temporal Contrastive Learning

Learning to recognize actions from only a handful of labeled videos is a...

8 Ankit Singh, et al. ∙

research

∙ 12/30/2020

A Maximal Correlation Approach to Imposing Fairness in Machine Learning

As machine learning algorithms grow in popularity and diversify to many ...

0 Joshua Lee, et al. ∙

research

∙ 12/07/2020

Fine-grained Angular Contrastive Learning with Coarse Labels

Few-shot learning methods offer pre-training techniques optimized for ea...

0 Guy Bukchin, et al. ∙

research

∙ 12/06/2020

Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation

Partial domain adaptation which assumes that the unknown target label sp...

6 Aadarsh Sahoo, et al. ∙

research

∙ 11/26/2020

Learning from Lexical Perturbations for Consistent Visual Question Answering

Existing Visual Question Answering (VQA) models are often fragile and se...

0 Spencer Whitehead, et al. ∙

research

∙ 11/20/2020

Large Scale Neural Architecture Search with Polyharmonic Splines

Neural Architecture Search (NAS) is a powerful tool to automatically des...

0 Ulrich Finkler, et al. ∙

research

∙ 10/22/2020

Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition

In recent years, a number of approaches based on 2D CNNs and 3D CNNs hav...

0 Chun-Fu Chen, et al. ∙

research

∙ 08/12/2020

We Have So Much In Common: Modeling Semantic Relational Set Abstractions in Videos

Identifying common patterns among events is a key ability in human and m...

8 Alex Andonian, et al. ∙

research

∙ 08/12/2020

Mitigating Dataset Imbalance via Joint Generation and Classification

Supervised deep learning methods are enjoying enormous success in many p...

5 Aadarsh Sahoo, et al. ∙

research

∙ 07/31/2020

AR-Net: Adaptive Frame Resolution for Efficient Action Recognition

Action recognition is an open and challenging problem in computer vision...

4 Yue Meng, et al. ∙

research

∙ 07/17/2020

OnlineAugment: Online Data Augmentation with Less Domain Knowledge

Data augmentation is one of the most important tools in training modern ...

15 Zhiqiang Tang, et al. ∙

research

∙ 06/23/2020

NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search

Neural Architecture Search (NAS) is an open and challenging problem in m...

9 Rameswar Panda, et al. ∙

research

∙ 06/16/2020

AVLnet: Learning Audio-Visual Language Representations from Instructional Videos

Current methods for learning visually grounded language from videos ofte...

14 Andrew Rouditchenko, et al. ∙

research

∙ 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,...

25 Leonid Karlinsky, et al. ∙

research

∙ 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...

12 Moshe Lichtenstein, et al. ∙

Rogerio Feris

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