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Found 35 papers, 21 papers with code
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement
LLM2LLM (1) fine-tunes a baseline student LLM on the initial seed data, (2) evaluates and extracts data points that the model gets wrong, and (3) uses a teacher LLM to generate synthetic data based on these incorrect data points, which are then added back into the training data.
xT: Nested Tokenization for Larger Context in Large Images
Modern computer vision pipelines handle large images in one of two sub-optimal ways: down-sampling or cropping.
Do Vision and Language Encoders Represent the World Similarly?
In the absence of statistical similarity in aligned encoders like CLIP, we show that a possible matching of unaligned encoders exists without any training.
Dr$^2$Net: Dynamic Reversible Dual-Residual Networks for Memory-Efficient Finetuning
We use two coefficients on either type of residual connections respectively, and introduce a dynamic training strategy that seamlessly transitions the pretrained model to a reversible network with much higher numerical precision.
Adaptive Human Trajectory Prediction via Latent Corridors
We formalize the problem of scene-specific adaptive trajectory prediction and propose a new adaptation approach inspired by prompt tuning called latent corridors.
Sequential Modeling Enables Scalable Learning for Large Vision Models
We introduce a novel sequential modeling approach which enables learning a Large Vision Model (LVM) without making use of any linguistic data.
EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding
We introduce EgoSchema, a very long-form video question-answering dataset, and benchmark to evaluate long video understanding capabilities of modern vision and language systems.
PaReprop: Fast Parallelized Reversible Backpropagation
We present PaReprop, a fast Parallelized Reversible Backpropagation algorithm that parallelizes the additional activation re-computation overhead in reversible training with the gradient computation itself in backpropagation phase.
Diffusion Models as Masked Autoencoders
There has been a longstanding belief that generation can facilitate a true understanding of visual data.
Speculative Decoding with Big Little Decoder
To address this, we propose Big Little Decoder (BiLD), a framework that can improve inference efficiency and latency for a wide range of text generation applications.
Reversible Vision Transformers
Reversible Vision Transformers achieve a reduced memory footprint of up to 15. 5x at roughly identical model complexity, parameters and accuracy, demonstrating the promise of reversible vision transformers as an efficient backbone for hardware resource limited training regimes.
Latency Matters: Real-Time Action Forecasting Transformer
We present RAFTformer, a real-time action forecasting transformer for latency aware real-world action forecasting applications.
Re2TAL: Rewiring Pretrained Video Backbones for Reversible Temporal Action Localization
Temporal action localization (TAL) requires long-form reasoning to predict actions of various durations and complex content.
Re-evaluating the Need for Multimodal Signals in Unsupervised Grammar Induction
Are multimodal inputs necessary for grammar induction?
Re^2TAL: Rewiring Pretrained Video Backbones for Reversible Temporal Action Localization
Temporal action localization (TAL) requires long-form reasoning to predict actions of various durations and complex content.
Structured Video Tokens @ Ego4D PNR Temporal Localization Challenge 2022
First, as both images and videos contain structured information, we enrich a transformer model with a set of \emph{object tokens} that can be used across images and videos.
Point- of-no-return (PNR) temporal localization
Temporal Localization
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens
We explore a particular instantiation of scene structure, namely a \emph{Hand-Object Graph}, consisting of hands and objects with their locations as nodes, and physical relations of contact/no-contact as edges.
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
After re-examining the design choices for both the macro and micro-architecture of Conformer, we propose Squeezeformer which consistently outperforms the state-of-the-art ASR models under the same training schemes.
Ranked #30 on
Speech Recognition
on LibriSpeech test-clean
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition
Instead of trying to process more frames at once like most existing methods, we propose to process videos in an online fashion and cache "memory" at each iteration.
Ranked #3 on
Action Anticipation
on EPIC-KITCHENS-100
(using extra training data)
Overcoming Mode Collapse with Adaptive Multi Adversarial Training
Generative Adversarial Networks (GANs) are a class of generative models used for various applications, but they have been known to suffer from the mode collapse problem, in which some modes of the target distribution are ignored by the generator.
MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
In this paper, we study Multiscale Vision Transformers (MViTv2) as a unified architecture for image and video classification, as well as object detection.
Ranked #1 on
Action Classification
on Kinetics-600
(GFLOPs metric)
Object-Region Video Transformers
In this work, we present Object-Region Video Transformers (ORViT), an \emph{object-centric} approach that extends video transformer layers with a block that directly incorporates object representations.
Ranked #6 on
Action Recognition
on Diving-48
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
LOKI: Long Term and Key Intentions for Trajectory Prediction
We also propose a model that jointly performs trajectory and intention prediction, showing that recurrently reasoning about intention can assist with trajectory prediction.
Multiscale Vision Transformers
We evaluate this fundamental architectural prior for modeling the dense nature of visual signals for a variety of video recognition tasks where it outperforms concurrent vision transformers that rely on large scale external pre-training and are 5-10x more costly in computation and parameters.
Ranked #14 on
Action Classification
on Charades
Mitigating Mode Collapse by Sidestepping Catastrophic Forgetting
Generative Adversarial Networks (GANs) are a class of generative models used for various applications, but they have been known to suffer from the mode collapse problem, in which some modes of the target distribution are ignored by the generator.
From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting
Uncertainty in future trajectories stems from two sources: (a) sources that are known to the agent but unknown to the model, such as long term goals and (b)sources that are unknown to both the agent & the model, such as intent of other agents & irreducible randomness indecisions.
Ranked #3 on
Trajectory Prediction
on ETH/UCY
Long-term Human Motion Prediction with Scene Context
Human movement is goal-directed and influenced by the spatial layout of the objects in the scene.
It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction.
Ranked #1 on
Multi Future Trajectory Prediction
on ETH/UCY
Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision
In contrast to the previous work that aims to solve either the task of pose prediction or trajectory forecasting in isolation, we propose a framework to unify the two problems and address the practically useful task of pedestrian locomotion prediction in the wild.
Do deep neural networks learn shallow learnable examples first?
In this paper, we empirically investigate the training journey of deep neural networks relative to fully trained shallow machine learning models.
On Compressing U-net Using Knowledge Distillation
We study the use of knowledge distillation to compress the U-net architecture.
Learning Spontaneity to Improve Emotion Recognition In Speech
We investigate the effect and usefulness of spontaneity (i. e. whether a given speech is spontaneous or not) in speech in the context of emotion recognition.
Future Person Localization in First-Person Videos
We present a new task that predicts future locations of people observed in first-person videos.
Bitwise Operations of Cellular Automaton on Gray-scale Images
The results indicate several interesting invariances in the application of the CA, such as the particular noise realization and the choice of sub-sampling of pixels to determine recombination weights.
