Search Results for author: Ali Farhadi

Found 130 papers, 74 papers with code

Exposing the Limits of Video-Text Models through Contrast Sets

1 code implementation NAACL 2022 Jae Sung Park, Sheng Shen, Ali Farhadi, Trevor Darrell, Yejin Choi, Anna Rohrbach

We test the robustness of recent methods on the proposed automatic contrast sets, and compare them to additionally collected human-generated counterparts, to assess their effectiveness.

Language Modelling Multiple-choice +2

On the Connection between Pre-training Data Diversity and Fine-tuning Robustness

no code implementations24 Jul 2023 Vivek Ramanujan, Thao Nguyen, Sewoong Oh, Ludwig Schmidt, Ali Farhadi

Pre-training has been widely adopted in deep learning to improve model performance, especially when the training data for a target task is limited.

Neural Priming for Sample-Efficient Adaptation

1 code implementation16 Jun 2023 Matthew Wallingford, Vivek Ramanujan, Alex Fang, Aditya Kusupati, Roozbeh Mottaghi, Aniruddha Kembhavi, Ludwig Schmidt, Ali Farhadi

Performing lightweight updates on the recalled data significantly improves accuracy across a variety of distribution shift and transfer learning benchmarks.

Transfer Learning

Bytes Are All You Need: Transformers Operating Directly On File Bytes

1 code implementation31 May 2023 Maxwell Horton, Sachin Mehta, Ali Farhadi, Mohammad Rastegari

Our model, \emph{ByteFormer}, achieves an ImageNet Top-1 classification accuracy of $77. 33\%$ when training and testing directly on TIFF file bytes using a transformer backbone with configuration similar to DeiT-Ti ($72. 2\%$ accuracy when operating on RGB images).

Classification Image Classification +1

AdANNS: A Framework for Adaptive Semantic Search

1 code implementation30 May 2023 Aniket Rege, Aditya Kusupati, Sharan Ranjit S, Alan Fan, Qingqing Cao, Sham Kakade, Prateek Jain, Ali Farhadi

Finally, we demonstrate that AdANNS can enable inference-time adaptivity for compute-aware search on ANNS indices built non-adaptively on matryoshka representations.

Natural Questions Quantization +1

Stable and low-precision training for large-scale vision-language models

1 code implementation25 Apr 2023 Mitchell Wortsman, Tim Dettmers, Luke Zettlemoyer, Ari Morcos, Ali Farhadi, Ludwig Schmidt

We introduce new methods for 1) accelerating and 2) stabilizing training for large language-vision models.

Moving Forward by Moving Backward: Embedding Action Impact over Action Semantics

no code implementations24 Apr 2023 Kuo-Hao Zeng, Luca Weihs, Roozbeh Mottaghi, Ali Farhadi

A common assumption when training embodied agents is that the impact of taking an action is stable; for instance, executing the "move ahead" action will always move the agent forward by a fixed distance, perhaps with some small amount of actuator-induced noise.

Visual Navigation

FastFill: Efficient Compatible Model Update

1 code implementation8 Mar 2023 Florian Jaeckle, Fartash Faghri, Ali Farhadi, Oncel Tuzel, Hadi Pouransari

The task of retrieving the most similar data from a gallery set to a given query data is performed through a similarity comparison on features.

Representation Learning Retrieval

Neural Radiance Field Codebooks

1 code implementation10 Jan 2023 Matthew Wallingford, Aditya Kusupati, Alex Fang, Vivek Ramanujan, Aniruddha Kembhavi, Roozbeh Mottaghi, Ali Farhadi

Compositional representations of the world are a promising step towards enabling high-level scene understanding and efficient transfer to downstream tasks.

Representation Learning Scene Understanding

RangeAugment: Efficient Online Augmentation with Range Learning

1 code implementation20 Dec 2022 Sachin Mehta, Saeid Naderiparizi, Fartash Faghri, Maxwell Horton, Lailin Chen, Ali Farhadi, Oncel Tuzel, Mohammad Rastegari

To answer the open question on the importance of magnitude ranges for each augmentation operation, we introduce RangeAugment that allows us to efficiently learn the range of magnitudes for individual as well as composite augmentation operations.

Knowledge Distillation object-detection +3

Objaverse: A Universe of Annotated 3D Objects

no code implementations CVPR 2023 Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, Ali Farhadi

Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and LAION have propelled recent dramatic progress in AI.


Self-Supervised Object Goal Navigation with In-Situ Finetuning

no code implementations9 Dec 2022 So Yeon Min, Yao-Hung Hubert Tsai, Wei Ding, Ali Farhadi, Ruslan Salakhutdinov, Yonatan Bisk, Jian Zhang

In contrast, our LocCon shows the most robust transfer in the real world among the set of models we compare to, and that the real-world performance of all models can be further improved with self-supervised LocCon in-situ training.

Contrastive Learning Navigate +1

Editing Models with Task Arithmetic

1 code implementation8 Dec 2022 Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi

Changing how pre-trained models behave -- e. g., improving their performance on a downstream task or mitigating biases learned during pre-training -- is a common practice when developing machine learning systems.

lo-fi: distributed fine-tuning without communication

no code implementations19 Oct 2022 Mitchell Wortsman, Suchin Gururangan, Shen Li, Ali Farhadi, Ludwig Schmidt, Michael Rabbat, Ari S. Morcos

When fine-tuning DeiT-base and DeiT-large on ImageNet, this procedure matches accuracy in-distribution and improves accuracy under distribution shift compared to the baseline, which observes the same amount of data but communicates gradients at each step.

Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents

no code implementations27 Sep 2022 Yao-Hung Hubert Tsai, Hanlin Goh, Ali Farhadi, Jian Zhang

The perception system in personalized mobile agents requires developing indoor scene understanding models, which can understand 3D geometries, capture objectiveness, analyze human behaviors, etc.

3D Object Detection Autonomous Driving +7

What does a platypus look like? Generating customized prompts for zero-shot image classification

2 code implementations ICCV 2023 Sarah Pratt, Ian Covert, Rosanne Liu, Ali Farhadi

Unlike traditional classification models, open-vocabulary models classify among any arbitrary set of categories specified with natural language during inference.

Descriptive Image Classification +1

Patching open-vocabulary models by interpolating weights

1 code implementation10 Aug 2022 Gabriel Ilharco, Mitchell Wortsman, Samir Yitzhak Gadre, Shuran Song, Hannaneh Hajishirzi, Simon Kornblith, Ali Farhadi, Ludwig Schmidt

We study model patching, where the goal is to improve accuracy on specific tasks without degrading accuracy on tasks where performance is already adequate.

Image Classification

Break and Make: Interactive Structural Understanding Using LEGO Bricks

2 code implementations27 Jul 2022 Aaron Walsman, Muru Zhang, Klemen Kotar, Karthik Desingh, Ali Farhadi, Dieter Fox

We pair this simulator with a new dataset of fan-made LEGO creations that have been uploaded to the internet in order to provide complex scenes containing over a thousand unique brick shapes.

Matryoshka Representation Learning

1 code implementation26 May 2022 Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, KaiFeng Chen, Sham Kakade, Prateek Jain, Ali Farhadi

The flexibility within the learned Matryoshka Representations offer: (a) up to 14x smaller embedding size for ImageNet-1K classification at the same level of accuracy; (b) up to 14x real-world speed-ups for large-scale retrieval on ImageNet-1K and 4K; and (c) up to 2% accuracy improvements for long-tail few-shot classification, all while being as robust as the original representations.

Ranked #24 on Image Classification on ObjectNet (using extra training data)

Image Classification Representation Learning +1

Object Manipulation via Visual Target Localization

no code implementations15 Mar 2022 Kiana Ehsani, Ali Farhadi, Aniruddha Kembhavi, Roozbeh Mottaghi

Object manipulation is a critical skill required for Embodied AI agents interacting with the world around them.

object-detection Object Detection

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

5 code implementations10 Mar 2022 Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, Ludwig Schmidt

The conventional recipe for maximizing model accuracy is to (1) train multiple models with various hyperparameters and (2) pick the individual model which performs best on a held-out validation set, discarding the remainder.

 Ranked #1 on Out-of-Distribution Generalization on ImageNet-W (using extra training data)

Domain Generalization Image Classification +2

The Introspective Agent: Interdependence of Strategy, Physiology, and Sensing for Embodied Agents

1 code implementation2 Jan 2022 Sarah Pratt, Luca Weihs, Ali Farhadi

While traditional embodied agents manipulate an environment to best achieve a goal, we argue for an introspective agent, which considers its own abilities in the context of its environment.

Forward Compatible Training for Large-Scale Embedding Retrieval Systems

1 code implementation CVPR 2022 Vivek Ramanujan, Pavan Kumar Anasosalu Vasu, Ali Farhadi, Oncel Tuzel, Hadi Pouransari

To avoid the cost of backfilling, BCT modifies training of the new model to make its representations compatible with those of the old model.

Representation Learning Retrieval

LCS: Learning Compressible Subspaces for Adaptive Network Compression at Inference Time

1 code implementation8 Oct 2021 Elvis Nunez, Maxwell Horton, Anish Prabhu, Anurag Ranjan, Ali Farhadi, Mohammad Rastegari

Our models require no retraining, thus our subspace of models can be deployed entirely on-device to allow adaptive network compression at inference time.


Robust fine-tuning of zero-shot models

3 code implementations CVPR 2022 Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo-Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt

Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution.

Ranked #10 on Image Classification on ObjectNet (using extra training data)

Image Classification Transfer Learning

LanguageRefer: Spatial-Language Model for 3D Visual Grounding

no code implementations7 Jul 2021 Junha Roh, Karthik Desingh, Ali Farhadi, Dieter Fox

Specifically, given a reconstructed 3D scene in the form of point clouds with 3D bounding boxes of potential object candidates, and a language utterance referring to a target object in the scene, our model successfully identifies the target object from a set of potential candidates.

Language Modelling Visual Grounding

MERLOT: Multimodal Neural Script Knowledge Models

1 code implementation NeurIPS 2021 Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, Yejin Choi

As humans, we understand events in the visual world contextually, performing multimodal reasoning across time to make inferences about the past, present, and future.

Visual Commonsense Reasoning

PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World

no code implementations ACL 2021 Rowan Zellers, Ari Holtzman, Matthew Peters, Roozbeh Mottaghi, Aniruddha Kembhavi, Ali Farhadi, Yejin Choi

We propose PIGLeT: a model that learns physical commonsense knowledge through interaction, and then uses this knowledge to ground language.

Language Modelling

Pushing it out of the Way: Interactive Visual Navigation

1 code implementation CVPR 2021 Kuo-Hao Zeng, Luca Weihs, Ali Farhadi, Roozbeh Mottaghi

In this paper, we study the problem of interactive navigation where agents learn to change the environment to navigate more efficiently to their goals.

Navigate Visual Navigation

Learning Neural Network Subspaces

1 code implementation20 Feb 2021 Mitchell Wortsman, Maxwell Horton, Carlos Guestrin, Ali Farhadi, Mohammad Rastegari

Recent observations have advanced our understanding of the neural network optimization landscape, revealing the existence of (1) paths of high accuracy containing diverse solutions and (2) wider minima offering improved performance.

Learning Flexible Visual Representations via Interactive Gameplay

no code implementations ICLR 2021 Luca Weihs, Aniruddha Kembhavi, Kiana Ehsani, Sarah M Pratt, Winson Han, Alvaro Herrasti, Eric Kolve, Dustin Schwenk, Roozbeh Mottaghi, Ali Farhadi

A growing body of research suggests that embodied gameplay, prevalent not just in human cultures but across a variety of animal species including turtles and ravens, is critical in developing the neural flexibility for creative problem solving, decision making and socialization.

Decision Making Representation Learning

Learning Visual Representation from Human Interactions

no code implementations ICLR 2021 Kiana Ehsani, Daniel Gordon, Thomas Hai Dang Nguyen, Roozbeh Mottaghi, Ali Farhadi

Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision.

Action Recognition Depth Estimation +2

Layer-Wise Data-Free CNN Compression

no code implementations18 Nov 2020 Maxwell Horton, Yanzi Jin, Ali Farhadi, Mohammad Rastegari

We also show how to precondition the network to improve the accuracy of our layer-wise compression method.


What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions

1 code implementation16 Oct 2020 Kiana Ehsani, Daniel Gordon, Thomas Nguyen, Roozbeh Mottaghi, Ali Farhadi

Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision.

Action Recognition Depth Estimation +2

FLUID: A Unified Evaluation Framework for Flexible Sequential Data

2 code implementations6 Jul 2020 Matthew Wallingford, Aditya Kusupati, Keivan Alizadeh-Vahid, Aaron Walsman, Aniruddha Kembhavi, Ali Farhadi

To foster research towards the goal of general ML methods, we introduce a new unified evaluation framework - FLUID (Flexible Sequential Data).

Continual Learning Representation Learning +1

Supermasks in Superposition

2 code implementations NeurIPS 2020 Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, Ali Farhadi

We present the Supermasks in Superposition (SupSup) model, capable of sequentially learning thousands of tasks without catastrophic forgetting.

VisualCOMET: Reasoning about the Dynamic Context of a Still Image

no code implementations ECCV 2020 Jae Sung Park, Chandra Bhagavatula, Roozbeh Mottaghi, Ali Farhadi, Yejin Choi

In addition, we provide person-grounding (i. e., co-reference links) between people appearing in the image and people mentioned in the textual commonsense descriptions, allowing for tighter integration between images and text.

Visual Commonsense Reasoning

RoboTHOR: An Open Simulation-to-Real Embodied AI Platform

1 code implementation CVPR 2020 Matt Deitke, Winson Han, Alvaro Herrasti, Aniruddha Kembhavi, Eric Kolve, Roozbeh Mottaghi, Jordi Salvador, Dustin Schwenk, Eli VanderBilt, Matthew Wallingford, Luca Weihs, Mark Yatskar, Ali Farhadi

We argue that interactive and embodied visual AI has reached a stage of development similar to visual recognition prior to the advent of these ecosystems.

Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects

2 code implementations CVPR 2020 Kiana Ehsani, Shubham Tulsiani, Saurabh Gupta, Ali Farhadi, Abhinav Gupta

Our quantitative and qualitative results show that (a) we can predict meaningful forces from videos whose effects lead to accurate imitation of the motions observed, (b) by jointly optimizing for contact point and force prediction, we can improve the performance on both tasks in comparison to independent training, and (c) we can learn a representation from this model that generalizes to novel objects using few shot examples.

Human-Object Interaction Detection

Grounded Situation Recognition

1 code implementation ECCV 2020 Sarah Pratt, Mark Yatskar, Luca Weihs, Ali Farhadi, Aniruddha Kembhavi

We introduce Grounded Situation Recognition (GSR), a task that requires producing structured semantic summaries of images describing: the primary activity, entities engaged in the activity with their roles (e. g. agent, tool), and bounding-box groundings of entities.

Grounded Situation Recognition Image Retrieval +1

Watching the World Go By: Representation Learning from Unlabeled Videos

1 code implementation18 Mar 2020 Daniel Gordon, Kiana Ehsani, Dieter Fox, Ali Farhadi

Recent single image unsupervised representation learning techniques show remarkable success on a variety of tasks.

Data Augmentation Representation Learning

Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

4 code implementations15 Feb 2020 Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, Noah Smith

We publicly release all of our experimental data, including training and validation scores for 2, 100 trials, to encourage further analysis of training dynamics during fine-tuning.

Learning Generalizable Visual Representations via Interactive Gameplay

no code implementations17 Dec 2019 Luca Weihs, Aniruddha Kembhavi, Kiana Ehsani, Sarah M Pratt, Winson Han, Alvaro Herrasti, Eric Kolve, Dustin Schwenk, Roozbeh Mottaghi, Ali Farhadi

A growing body of research suggests that embodied gameplay, prevalent not just in human cultures but across a variety of animal species including turtles and ravens, is critical in developing the neural flexibility for creative problem solving, decision making, and socialization.

Decision Making Representation Learning

Visual Reaction: Learning to Play Catch with Your Drone

1 code implementation CVPR 2020 Kuo-Hao Zeng, Roozbeh Mottaghi, Luca Weihs, Ali Farhadi

In this paper we address the problem of visual reaction: the task of interacting with dynamic environments where the changes in the environment are not necessarily caused by the agent itself.

Conditional Driving from Natural Language Instructions

no code implementations16 Oct 2019 Junha Roh, Chris Paxton, Andrzej Pronobis, Ali Farhadi, Dieter Fox

Widespread adoption of self-driving cars will depend not only on their safety but largely on their ability to interact with human users.

Imitation Learning Self-Driving Cars

Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index

1 code implementation ACL 2019 Minjoon Seo, Jinhyuk Lee, Tom Kwiatkowski, Ankur P. Parikh, Ali Farhadi, Hannaneh Hajishirzi

Existing open-domain question answering (QA) models are not suitable for real-time usage because they need to process several long documents on-demand for every input query.

Open-Domain Question Answering

Butterfly Transform: An Efficient FFT Based Neural Architecture Design

1 code implementation CVPR 2020 Keivan Alizadeh Vahid, Anish Prabhu, Ali Farhadi, Mohammad Rastegari

By replacing pointwise convolutions with BFT, we reduce the computational complexity of these layers from O(n^2) to O(n\log n) with respect to the number of channels.

Neural Architecture Search

OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge

1 code implementation CVPR 2019 Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi

In this paper, we address the task of knowledge-based visual question answering and provide a benchmark, called OK-VQA, where the image content is not sufficient to answer the questions, encouraging methods that rely on external knowledge resources.

object-detection Object Detection +3

Defending Against Neural Fake News

4 code implementations NeurIPS 2019 Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, Yejin Choi

We find that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data.

Computer Security Fake News Detection +1

HellaSwag: Can a Machine Really Finish Your Sentence?

2 code implementations ACL 2019 Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, Yejin Choi

In this paper, we show that commonsense inference still proves difficult for even state-of-the-art models, by presenting HellaSwag, a new challenge dataset.

Natural Language Inference

What Should I Do Now? Marrying Reinforcement Learning and Symbolic Planning

no code implementations6 Jan 2019 Daniel Gordon, Dieter Fox, Ali Farhadi

In this work we propose Hierarchical Planning and Reinforcement Learning (HIP-RL), a method for merging the benefits and capabilities of Symbolic Planning with the learning abilities of Deep Reinforcement Learning.

Question Answering reinforcement-learning +1

ELASTIC: Improving CNNs with Dynamic Scaling Policies

1 code implementation CVPR 2019 Huiyu Wang, Aniruddha Kembhavi, Ali Farhadi, Alan Yuille, Mohammad Rastegari

We formulate the scaling policy as a non-linear function inside the network's structure that (a) is learned from data, (b) is instance specific, (c) does not add extra computation, and (d) can be applied on any network architecture.

General Classification Multi-Label Classification +1

From Recognition to Cognition: Visual Commonsense Reasoning

4 code implementations CVPR 2019 Rowan Zellers, Yonatan Bisk, Ali Farhadi, Yejin Choi

While this task is easy for humans, it is tremendously difficult for today's vision systems, requiring higher-order cognition and commonsense reasoning about the world.

Multiple-choice Multiple Choice Question Answering (MCQA) +1

Visual Semantic Navigation using Scene Priors

1 code implementation ICLR 2019 Wei Yang, Xiaolong Wang, Ali Farhadi, Abhinav Gupta, Roozbeh Mottaghi

Do we use the semantic/functional priors we have built over years to efficiently search and navigate?


PhotoShape: Photorealistic Materials for Large-Scale Shape Collections

1 code implementation26 Sep 2018 Keunhong Park, Konstantinos Rematas, Ali Farhadi, Steven M. Seitz

Existing online 3D shape repositories contain thousands of 3D models but lack photorealistic appearance.

Label Refinery: Improving ImageNet Classification through Label Progression

4 code implementations7 May 2018 Hessam Bagherinezhad, Maxwell Horton, Mohammad Rastegari, Ali Farhadi

Among the three main components (data, labels, and models) of any supervised learning system, data and models have been the main subjects of active research.

Classification General Classification

Charades-Ego: A Large-Scale Dataset of Paired Third and First Person Videos

no code implementations25 Apr 2018 Gunnar A. Sigurdsson, Abhinav Gupta, Cordelia Schmid, Ali Farhadi, Karteek Alahari

In this paper we describe the egocentric aspect of the dataset and present annotations for Charades-Ego with 68, 536 activity instances in 68. 8 hours of first and third-person video, making it one of the largest and most diverse egocentric datasets available.

General Classification Video Classification +1

Actor and Observer: Joint Modeling of First and Third-Person Videos

1 code implementation CVPR 2018 Gunnar A. Sigurdsson, Abhinav Gupta, Cordelia Schmid, Ali Farhadi, Karteek Alahari

Several theories in cognitive neuroscience suggest that when people interact with the world, or simulate interactions, they do so from a first-person egocentric perspective, and seamlessly transfer knowledge between third-person (observer) and first-person (actor).

Action Recognition Temporal Action Localization

Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension

1 code implementation EMNLP 2018 Minjoon Seo, Tom Kwiatkowski, Ankur P. Parikh, Ali Farhadi, Hannaneh Hajishirzi

We formalize a new modular variant of current question answering tasks by enforcing complete independence of the document encoder from the question encoder.

Question Answering Reading Comprehension +1

Imagine This! Scripts to Compositions to Videos

4 code implementations ECCV 2018 Tanmay Gupta, Dustin Schwenk, Ali Farhadi, Derek Hoiem, Aniruddha Kembhavi

Imagining a scene described in natural language with realistic layout and appearance of entities is the ultimate test of spatial, visual, and semantic world knowledge.


DOCK: Detecting Objects by transferring Common-sense Knowledge

no code implementations ECCV 2018 Krishna Kumar Singh, Santosh Divvala, Ali Farhadi, Yong Jae Lee

We present a scalable approach for Detecting Objects by transferring Common-sense Knowledge (DOCK) from source to target categories.

Common Sense Reasoning Semantic Similarity +2

Neural Speed Reading via Skim-RNN

1 code implementation ICLR 2018 Minjoon Seo, Sewon Min, Ali Farhadi, Hannaneh Hajishirzi

Inspired by the principles of speed reading, we introduce Skim-RNN, a recurrent neural network (RNN) that dynamically decides to update only a small fraction of the hidden state for relatively unimportant input tokens.

AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video

no code implementations13 Sep 2017 Nancy Xin Ru Wang, Ali Farhadi, Rajesh Rao, Bingni Brunton

This paper describes our approach to detect and to predict natural human arm movements in the future, a key challenge in brain computer interfacing that has never before been attempted.

Future prediction Multimodal Deep Learning

Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension

no code implementations CVPR 2017 Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Ali Farhadi, Hannaneh Hajishirzi

Our analysis shows that a significant portion of questions require complex parsing of the text and the diagrams and reasoning, indicating that our dataset is more complex compared to previous machine comprehension and visual question answering datasets.

Question Answering Reading Comprehension +1

Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects

10 code implementations17 May 2017 Daniel Gordon, Ali Farhadi, Dieter Fox

Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, its motion, and how it changes over time.

Object Tracking regression +1

SeGAN: Segmenting and Generating the Invisible

1 code implementation CVPR 2018 Kiana Ehsani, Roozbeh Mottaghi, Ali Farhadi

Objects often occlude each other in scenes; Inferring their appearance beyond their visible parts plays an important role in scene understanding, depth estimation, object interaction and manipulation.

Depth Estimation Scene Understanding

See the Glass Half Full: Reasoning about Liquid Containers, their Volume and Content

no code implementations ICCV 2017 Roozbeh Mottaghi, Connor Schenck, Dieter Fox, Ali Farhadi

Doing so requires estimating the volume of the cup, approximating the amount of water in the pitcher, and predicting the behavior of water when we tilt the pitcher.

Commonly Uncommon: Semantic Sparsity in Situation Recognition

2 code implementations CVPR 2017 Mark Yatskar, Vicente Ordonez, Luke Zettlemoyer, Ali Farhadi

Semantic sparsity is a common challenge in structured visual classification problems; when the output space is complex, the vast majority of the possible predictions are rarely, if ever, seen in the training set.

Grounded Situation Recognition Structured Prediction

LCNN: Lookup-based Convolutional Neural Network

no code implementations CVPR 2017 Hessam Bagherinezhad, Mohammad Rastegari, Ali Farhadi

We introduce LCNN, a lookup-based convolutional neural network that encodes convolutions by few lookups to a dictionary that is trained to cover the space of weights in CNNs.

Few-Shot Learning

Bidirectional Attention Flow for Machine Comprehension

24 code implementations5 Nov 2016 Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, Hannaneh Hajishirzi

Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query.

Cloze Test Open-Domain Question Answering +1

Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning

2 code implementations16 Sep 2016 Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J. Lim, Abhinav Gupta, Li Fei-Fei, Ali Farhadi

To address the second issue, we propose AI2-THOR framework, which provides an environment with high-quality 3D scenes and physics engine.

3D Reconstruction Feature Engineering +3

Much Ado About Time: Exhaustive Annotation of Temporal Data

no code implementations25 Jul 2016 Gunnar A. Sigurdsson, Olga Russakovsky, Ali Farhadi, Ivan Laptev, Abhinav Gupta

We conclude that the optimal strategy is to ask as many questions as possible in a HIT (up to 52 binary questions after watching a 30-second video clip in our experiments).

Situation Recognition: Visual Semantic Role Labeling for Image Understanding

1 code implementation CVPR 2016 Mark Yatskar, Luke Zettlemoyer, Ali Farhadi

This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including: (1) the main activity (e. g., clipping), (2) the participating actors, objects, substances, and locations (e. g., man, shears, sheep, wool, and field) and most importantly (3) the roles these participants play in the activity (e. g., the man is clipping, the shears are his tool, the wool is being clipped from the sheep, and the clipping is in a field).

Activity Recognition Grounded Situation Recognition +2

A Task-Oriented Approach for Cost-Sensitive Recognition

no code implementations CVPR 2016 Roozbeh Mottaghi, Hannaneh Hajishirzi, Ali Farhadi

With the recent progress in visual recognition, we have already started to see a surge of vision related real-world applications.

Scene Understanding

Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks

4 code implementations13 Apr 2016 Junyuan Xie, Ross Girshick, Ali Farhadi

As 3D movie viewing becomes mainstream and Virtual Reality (VR) market emerges, the demand for 3D contents is growing rapidly.

Depth Estimation

A Diagram Is Worth A Dozen Images

1 code implementation24 Mar 2016 Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, Ali Farhadi

We define syntactic parsing of diagrams as learning to infer DPGs for diagrams and study semantic interpretation and reasoning of diagrams in the context of diagram question answering.

Visual Question Answering (VQA)

"What happens if..." Learning to Predict the Effect of Forces in Images

no code implementations17 Mar 2016 Roozbeh Mottaghi, Mohammad Rastegari, Abhinav Gupta, Ali Farhadi

To build a dataset of forces in scenes, we reconstructed all images in SUN RGB-D dataset in a physics simulator to estimate the physical movements of objects caused by external forces applied to them.

Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects

no code implementations2 Feb 2016 Hessam Bagherinezhad, Hannaneh Hajishirzi, Yejin Choi, Ali Farhadi

In this paper, we introduce a method to automatically infer object sizes, leveraging visual and textual information from web.

Visual Reasoning

Toward a Taxonomy and Computational Models of Abnormalities in Images

no code implementations4 Dec 2015 Babak Saleh, Ahmed Elgammal, Jacob Feldman, Ali Farhadi

In this paper we study various types of atypicalities in images in a more comprehensive way than has been done before.

Actions ~ Transformations

1 code implementation CVPR 2016 Xiaolong Wang, Ali Farhadi, Abhinav Gupta

In this paper, we propose a novel representation for actions by modeling an action as a transformation which changes the state of the environment before the action happens (precondition) to the state after the action (effect).

Action Recognition Temporal Action Localization

Generating Notifications for Missing Actions: Don't Forget to Turn the Lights Off!

no code implementations ICCV 2015 Bilge Soran, Ali Farhadi, Linda Shapiro

The overall prediction accuracy is 46. 2% when only 10 frames of an action are seen (2/3 of a sec).

Unsupervised Deep Embedding for Clustering Analysis

18 code implementations19 Nov 2015 Junyuan Xie, Ross Girshick, Ali Farhadi

Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms.

Clustering Image Clustering +1

Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images

no code implementations12 Nov 2015 Roozbeh Mottaghi, Hessam Bagherinezhad, Mohammad Rastegari, Ali Farhadi

Direct and explicit estimation of the forces and the motion of objects from a single image is extremely challenging.

Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing

no code implementations ICCV 2015 Hamid Izadinia, Fereshteh Sadeghi, Santosh Kumar Divvala, Yejin Choi, Ali Farhadi

Next, we show that the association of high-quality segmentations to textual phrases aids in richer semantic understanding and reasoning of these textual phrases.

Natural Language Understanding Object Recognition +2

Discriminative and Consistent Similarities in Instance-Level Multiple Instance Learning

no code implementations CVPR 2015 Mohammad Rastegari, Hannaneh Hajishirzi, Ali Farhadi

In this paper we present a bottom-up method to instance-level Multiple Instance Learning (MIL) that learns to discover positive instances with globally constrained reasoning about local pairwise similarities.

Multiple Instance Learning Text Categorization

Abnormal Object Recognition: A Comprehensive Study

no code implementations9 Nov 2014 Babak Saleh, Ali Farhadi, Ahmed Elgammal

When describing images, humans tend not to talk about the obvious, but rather mention what they find interesting.

Anomaly Detection Object Recognition

Predicting Failures of Vision Systems

no code implementations CVPR 2014 Peng Zhang, Jiuling Wang, Ali Farhadi, Martial Hebert, Devi Parikh

We show that a surprisingly straightforward and general approach, that we call ALERT, can predict the likely accuracy (or failure) of a variety of computer vision systems – semantic segmentation, vanishing point and camera parameter estimation, and image memorability prediction – on individual input images.

Semantic Segmentation Zero-Shot Learning

Learning Everything about Anything: Webly-Supervised Visual Concept Learning

no code implementations CVPR 2014 Santosh K. Divvala, Ali Farhadi, Carlos Guestrin

How can we learn a model for any concept that exhaustively covers all its appearance variations, while requiring minimal or no human supervision for compiling the vocabulary of visual variance, gathering the training images and annotations, and learning the models?

Incorporating Scene Context and Object Layout into Appearance Modeling

no code implementations CVPR 2014 Hamid Izadinia, Fereshteh Sadeghi, Ali Farhadi

In this paper, we propose a method to learn scene structures that can encode three main interlacing components of a scene: the scene category, the context-specific appearance of objects, and their layout.

Scene Understanding

Adding Unlabeled Samples to Categories by Learned Attributes

no code implementations CVPR 2013 Jonghyun Choi, Mohammad Rastegari, Ali Farhadi, Larry S. Davis

We propose a method to expand the visual coverage of training sets that consist of a small number of labeled examples using learned attributes.

Object-Centric Anomaly Detection by Attribute-Based Reasoning

no code implementations CVPR 2013 Babak Saleh, Ali Farhadi, Ahmed Elgammal

When describing images, humans tend not to talk about the obvious, but rather mention what they find interesting.

Anomaly Detection Image Categorization

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