Search Results for author:
Found 105 papers, 44 papers with code
DataMix: Efficient Privacy-Preserving Edge-Cloud Inference
Third, our solution is extit{efficient} on the edge since the majority of the workload is delegated to the cloud, and our mixing and de-mixing processes introduce very few extra computations.
On-Device Training Under 256KB Memory
Our framework is the first practical solution for on-device transfer learning of visual recognition on tiny IoT devices (e. g., a microcontroller with only 256KB SRAM), using less than 1/100 of the memory of existing frameworks while matching the accuracy of cloud training+edge deployment for the tinyML application VWW.
Prompting Decision Transformer for Few-Shot Policy Generalization
Humans can leverage prior experience and learn novel tasks from a handful of demonstrations.
SNAKE: Shape-aware Neural 3D Keypoint Field
Detecting 3D keypoints from point clouds is important for shape reconstruction, while this work investigates the dual question: can shape reconstruction benefit 3D keypoint detection?
EfficientViT: Enhanced Linear Attention for High-Resolution Low-Computation Visual Recognition
Existing methods (e. g., Swin, PVT) restrict the softmax attention within local windows or reduce the resolution of key/value tensors to reduce the cost, which sacrifices ViT's core advantages on global feature extractions.
RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation
To train this predictor, we formulate a new loss on rendering variances using gradients from differentiable rendering.
Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics
Extensive experimental results suggest that: 1) on multi-stage tasks that are infeasible for the vanilla differentiable physics solver, our approach discovers contact points that efficiently guide the solver to completion; 2) on tasks where the vanilla solver performs sub-optimally or near-optimally, our contact point discovery method performs better than or on par with the manipulation performance obtained with handcrafted contact points.
Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction
Specifically, FixNet consists of a perception module to extract the structured representation from the 3D point cloud, a physical dynamics prediction module to simulate the results of interactions on 3D objects, and a functionality prediction module to evaluate the functionality and choose the correct fix.
ComPhy: Compositional Physical Reasoning of Objects and Events from Videos
In this paper, we take an initial step to highlight the importance of inferring the hidden physical properties not directly observable from visual appearances, by introducing the Compositional Physical Reasoning (ComPhy) dataset.
Learning Neural Acoustic Fields
By modeling acoustic propagation in a scene as a linear time-invariant system, NAFs learn to continuously map all emitter and listener location pairs to a neural impulse response function that can then be applied to arbitrary sounds.
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools
We consider the problem of sequential robotic manipulation of deformable objects using tools.
FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations
We present a meta-learning framework for learning new visual concepts quickly, from just one or a few examples, guided by multiple naturally occurring data streams: simultaneously looking at images, reading sentences that describe the objects in the scene, and interpreting supplemental sentences that relate the novel concept with other concepts.
Linking Emergent and Natural Languages via Corpus Transfer
In this work, we propose a novel way to establish such a link by corpus transfer, i. e. pretraining on a corpus of emergent language for downstream natural language tasks, which is in contrast to prior work that directly transfers speaker and listener parameters.
AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation
To solve the above issues, we propose AutoGPart, a generic method enabling training generalizable 3D part segmentation networks with the task prior considered.
Finding Fallen Objects via Asynchronous Audio-Visual Integration
To study this problem, we have generated a large-scale dataset -- the Fallen Objects dataset -- that includes 8000 instances of 30 physical object categories in 64 rooms.
PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning
A critical aspect of human visual perception is the ability to parse visual scenes into individual objects and further into object parts, forming part-whole hierarchies.
STAR: A Benchmark for Situated Reasoning in Real-World Videos
This paper introduces a new benchmark that evaluates the situated reasoning ability via situation abstraction and logic-grounded question answering for real-world videos, called Situated Reasoning in Real-World Videos (STAR).
Memory-efficient Patch-based Inference for Tiny Deep Learning
We further propose receptive field redistribution to shift the receptive field and FLOPs to the later stage and reduce the computation overhead.
Graph Convolutional Module for Temporal Action Localization in Videos
To this end, we propose a general graph convolutional module (GCM) that can be easily plugged into existing action localization methods, including two-stage and one-stage paradigms.
Ranked #2 on
Temporal Action Localization
on THUMOS’14
(mAP IOU@0.1 metric)
When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?
We show that AdvCL is able to enhance cross-task robustness transferability without loss of model accuracy and finetuning efficiency.
Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language
This is achieved by seamlessly integrating three components: a visual perception module, a concept learner, and a differentiable physics engine.
MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning
We further propose network redistribution to shift the receptive field and FLOPs to the later stage and reduce the computation overhead.
Network Augmentation for Tiny Deep Learning
We introduce Network Augmentation (NetAug), a new training method for improving the performance of tiny neural networks.
Counterfactual Debiasing Inference for Compositional Action Recognition
By doing that, our proposed CDN method can better recognize unseen action instances by debiasing the effect of appearances.
OPEn: An Open-ended Physics Environment for Learning Without a Task
We test several existing RL-based exploration methods on this benchmark and find that an agent using unsupervised contrastive learning for representation learning, and impact-driven learning for exploration, achieved the best results.
Inducing Reusable Skills From Demonstrations with Option-Controller Network
With the isolation of information and the synchronous calling mechanism, we can impose a division of works between the controller and options in an end-to-end training regime.
TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Device
Secondly, TSM has high efficiency; it achieves a high frame rate of 74fps and 29fps for online video recognition on Jetson Nano and Galaxy Note8.
Self-supervised Audiovisual Representation Learning for Remote Sensing Data
By fine-tuning the models on a number of commonly used remote sensing datasets, we show that our approach outperforms existing pre-training strategies for remote sensing imagery.
Certifiably Robust Interpretation via Renyi Differential Privacy
The advantages of our Renyi-Robust-Smooth (RDP-based interpretation method) are three-folds.
Global Rhythm Style Transfer Without Text Transcriptions
In this paper, we propose AutoPST, which can disentangle global prosody style from speech without relying on any text transcriptions.
Cross-Modal Attention Consistency for Video-Audio Unsupervised Learning
Cross-modal correlation provides an inherent supervision for video unsupervised representation learning.
Temporal and Object Quantification Networks
We present Temporal and Object Quantification Networks (TOQ-Nets), a new class of neuro-symbolic networks with a structural bias that enables them to learn to recognize complex relational-temporal events.
Adversarial Option-Aware Hierarchical Imitation Learning
In particular, we propose an Expectation-Maximization(EM)-style algorithm: an E-step that samples the options of expert conditioned on the current learned policy, and an M-step that updates the low- and high-level policies of agent simultaneously to minimize the newly proposed option-occupancy measurement between the expert and the agent.
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules
In this paper, we focus on a more relaxed setting: the grounding of relevant visual entities in a weakly supervised manner by training on the VQA task alone.
Curious Representation Learning for Embodied Intelligence
Instead, it must explore its environment to acquire the data it will learn from.
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics
Experimental results suggest that 1) RL-based approaches struggle to solve most of the tasks efficiently; 2) gradient-based approaches, by optimizing open-loop control sequences with the built-in differentiable physics engine, can rapidly find a solution within tens of iterations, but still fall short on multi-stage tasks that require long-term planning.
Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning
We study the problem of dynamic visual reasoning on raw videos.
TransCenter: Transformers with Dense Representations for Multiple-Object Tracking
Despite this wave, building an accurate and efficient multiple-object tracking (MOT) method with transformers is not a trivial task.
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI
To complete the task, an embodied agent must plan a sequence of actions to change the state of a large number of objects in the face of realistic physical constraints.
Learning Task Decomposition with Ordered Memory Policy Network
The discovered subtask hierarchy could be used to perform task decomposition, recovering the subtask boundaries in an unstruc-tured demonstration.
AGENT: A Benchmark for Core Psychological Reasoning
For machine agents to successfully interact with humans in real-world settings, they will need to develop an understanding of human mental life.
Ranked #1 on
Core Psychological Reasoning
on AGENT
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
Despite the generalization power of the meta-model, it remains elusive that how adversarial robustness can be maintained by MAML in few-shot learning.
Grounding Physical Object and Event Concepts Through Dynamic Visual Reasoning
We study the problem of dynamic visual reasoning on raw videos.
Temporal and Object Quantification Nets
We aim to learn generalizable representations for complex activities by quantifying over both entities and time, as in “the kicker is behind all the other players,” or “the player controls the ball until it moves toward the goal.” Such a structural inductive bias of object relations, object quantification, and temporal orders will enable the learned representation to generalize to situations with varying numbers of agents, objects, and time courses.
Learning Task Decomposition with Order-Memory Policy Network
Many complex real-world tasks are composed of several levels of sub-tasks.
Augmenting Policy Learning with Routines Discovered from a Single Demonstration
Humans can abstract prior knowledge from very little data and use it to boost skill learning.
Object-Centric Diagnosis of Visual Reasoning
In contrast, symbolic and modular models have a relatively better grounding and robustness, though at the cost of accuracy.
MVFNet: Multi-View Fusion Network for Efficient Video Recognition
Existing state-of-the-art methods have achieved excellent accuracy regardless of the complexity meanwhile efficient spatiotemporal modeling solutions are slightly inferior in performance.
Ranked #18 on
Action Recognition
on Something-Something V1
RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning
We study unsupervised video representation learning that seeks to learn both motion and appearance features from unlabeled video only, which can be reused for downstream tasks such as action recognition.
Ranked #5 on
Self-Supervised Action Recognition
on UCF101
Representation Learning
Self-Supervised Action Recognition
+1
Synthetic Training for Monocular Human Mesh Recovery
To solve this problem, we design a multi-branch framework to disentangle the regression of different body properties, enabling us to separate each component's training in a synthetic training manner using unpaired data available.
Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning
Interactive Fiction (IF) games with real human-written natural language texts provide a new natural evaluation for language understanding techniques.
Location-aware Graph Convolutional Networks for Video Question Answering
In this work, we propose to represent the contents in the video as a location-aware graph by incorporating the location information of an object into the graph construction.
Noisy Agents: Self-supervised Exploration by Predicting Auditory Events
Humans integrate multiple sensory modalities (e. g. visual and audio) to build a causal understanding of the physical world.
TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device Learning
Furthermore, combined with feature extractor adaptation, TinyTL provides 7. 3-12. 9x memory saving without sacrificing accuracy compared to fine-tuning the full Inception-V3.
Foley Music: Learning to Generate Music from Videos
In this paper, we introduce Foley Music, a system that can synthesize plausible music for a silent video clip about people playing musical instruments.
MCUNet: Tiny Deep Learning on IoT Devices
Machine learning on tiny IoT devices based on microcontroller units (MCU) is appealing but challenging: the memory of microcontrollers is 2-3 orders of magnitude smaller even than mobile phones.
Generating Visually Aligned Sound from Videos
During testing, the audio forwarding regularizer is removed to ensure that REGNET can produce purely aligned sound only from visual features.
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation.
Language Guided Networks for Cross-modal Moment Retrieval
In this paper, we present Language Guided Networks (LGN), a new framework that leverages the sentence embedding to guide the whole process of moment retrieval.
A Real-time Action Representation with Temporal Encoding and Deep Compression
Our method achieves clear improvements on UCF101 action recognition benchmark against state-of-the-art real-time methods by 5. 4% in terms of accuracy and 2 times faster in terms of inference speed with a less than 5MB storage model.
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
To enable low-latency inference on resource-constrained hardware platforms, we propose to design Hardware-Aware Transformers (HAT) with neural architecture search.
Once for All: Train One Network and Specialize it for Efficient Deployment
Most of the traditional approaches either manually design or use neural architecture search (NAS) to find a specialized neural network and train it from scratch for each case, which is computationally expensive and unscalable.
Deep Audio Priors Emerge From Harmonic Convolutional Networks
We show that networks using Harmonic Convolution can reliably model audio priors and achieve high performance in unsupervised audio restoration tasks.
Music Gesture for Visual Sound Separation
Recent deep learning approaches have achieved impressive performance on visual sound separation tasks.
Dense Regression Network for Video Grounding
The key idea of this paper is to use the distances between the frame within the ground truth and the starting (ending) frame as dense supervisions to improve the video grounding accuracy.
Natural Language Moment Retrieval
Natural Language Queries
+1
Visual Concept-Metaconcept Learning
Humans reason with concepts and metaconcepts: we recognize red and green from visual input; we also understand that they describe the same property of objects (i. e., the color).
Look, Listen, and Act: Towards Audio-Visual Embodied Navigation
In this paper, we attempt to approach the problem of Audio-Visual Embodied Navigation, the task of planning the shortest path from a random starting location in a scene to the sound source in an indoor environment, given only raw egocentric visual and audio sensory data.
Cross-channel Communication Networks
Convolutional neural networks process input data by sending channel-wise feature response maps to subsequent layers.
Self-supervised Moving Vehicle Tracking with Stereo Sound
At test time, the stereo-sound student network can work independently to perform object localization us-ing just stereo audio and camera meta-data, without any visual input.
TruNet: Short Videos Generation from Long Videos via Story-Preserving Truncation
In this work, we introduce a new problem, named as {\em story-preserving long video truncation}, that requires an algorithm to automatically truncate a long-duration video into multiple short and attractive sub-videos with each one containing an unbroken story.
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
This paper studies Learning from Observations (LfO) for imitation learning with access to state-only demonstrations.
CLEVRER: CoLlision Events for Video REpresentation and Reasoning
While these models thrive on the perception-based task (descriptive), they perform poorly on the causal tasks (explanatory, predictive and counterfactual), suggesting that a principled approach for causal reasoning should incorporate the capability of both perceiving complex visual and language inputs, and understanding the underlying dynamics and causal relations.
Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos
With such hardware-aware model design, we are able to scale up the training on Summit supercomputer and reduce the training time on Kinetics dataset from 49 hours 55 minutes to 14 minutes 13 seconds, achieving a top-1 accuracy of 74. 0%, which is 1. 6x and 2. 9x faster than previous 3D video models with higher accuracy.
Graph Convolutional Networks for Temporal Action Localization
Then we apply the GCNs over the graph to model the relations among different proposals and learn powerful representations for the action classification and localization.
Ranked #4 on
Temporal Action Localization
on THUMOS’14
(mAP IOU@0.1 metric)
Once-for-All: Train One Network and Specialize it for Efficient Deployment
On diverse edge devices, OFA consistently outperforms state-of-the-art (SOTA) NAS methods (up to 4. 0% ImageNet top1 accuracy improvement over MobileNetV3, or same accuracy but 1. 5x faster than MobileNetV3, 2. 6x faster than EfficientNet w. r. t measured latency) while reducing many orders of magnitude GPU hours and $CO_2$ emission.
Ranked #55 on
Neural Architecture Search
on ImageNet
Deep Concept-wise Temporal Convolutional Networks for Action Localization
In this paper, we empirically find that stacking more conventional temporal convolution layers actually deteriorates action classification performance, possibly ascribing to that all channels of 1D feature map, which generally are highly abstract and can be regarded as latent concepts, are excessively recombined in temporal convolution.
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation.
Ranked #5 on
Visual Question Answering
on CLEVR
Self-Supervised Audio-Visual Co-Segmentation
Segmenting objects in images and separating sound sources in audio are challenging tasks, in part because traditional approaches require large amounts of labeled data.
Defensive Quantization: When Efficiency Meets Robustness
This paper aims to raise people's awareness about the security of the quantized models, and we designed a novel quantization methodology to jointly optimize the efficiency and robustness of deep learning models.
Interpreting Adversarial Examples by Activation Promotion and Suppression
It is widely known that convolutional neural networks (CNNs) are vulnerable to adversarial examples: images with imperceptible perturbations crafted to fool classifiers.
Weakly Supervised Dense Event Captioning in Videos
Dense event captioning aims to detect and describe all events of interest contained in a video.
TSM: Temporal Shift Module for Efficient Video Understanding
The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost.
Ranked #13 on
Video Object Detection
on ImageNet VID
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
In this paper, in contrast to the existing CNN+RNN or pure 3D convolution based approaches, we explore a novel spatial temporal network (StNet) architecture for both local and global spatial-temporal modeling in videos.
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
Second, the model is more data- and memory-efficient: it performs well after learning on a small number of training data; it can also encode an image into a compact representation, requiring less storage than existing methods for offline question answering.
Ranked #1 on
Visual Question Answering
on CLEVR
Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation
The recent advances in deep learning have made it possible to generate photo-realistic images by using neural networks and even to extrapolate video frames from an input video clip.
Sparse, Smart Contours to Represent and Edit Images
We study the problem of reconstructing an image from information stored at contour locations.
Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning
In addition, we also find that a progressive training strategy can foster a better neural network for the video recognition task than blindly pooling the distinct sources of geometry cues together.
The Sound of Pixels
We introduce PixelPlayer, a system that, by leveraging large amounts of unlabeled videos, learns to locate image regions which produce sounds and separate the input sounds into a set of components that represents the sound from each pixel.
End-to-End Learning of Motion Representation for Video Understanding
Despite the recent success of end-to-end learned representations, hand-crafted optical flow features are still widely used in video analysis tasks.
Ranked #37 on
Action Recognition
on UCF101
Smart, Sparse Contours to Represent and Edit Images
We study the problem of reconstructing an image from information stored at contour locations.
Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency
In this paper, we introduce a novel unsupervised domain adaptation technique for the task of 3D keypoint prediction from a single depth scan or image.
Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification
In this paper, however, we show that temporal information, especially longer-term patterns, may not be necessary to achieve competitive results on common video classification datasets.
VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation
Many seemingly distant annotations (e. g., semantic segmentation and visual question answering (VQA)) are inherently connected in that they reveal different levels and perspectives of human understandings about the same visual scenes --- and even the same set of images (e. g., of COCO).
Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classification
Experiment results on the challenging Kinetics dataset demonstrate that our proposed temporal modeling approaches can significantly improve existing approaches in the large-scale video recognition tasks.
Ranked #109 on
Action Classification
on Kinetics-400
Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding
This paper describes our solution for the video recognition task of the Google Cloud and YouTube-8M Video Understanding Challenge that ranked the 3rd place.
StyleNet: Generating Attractive Visual Captions With Styles
We propose a novel framework named StyleNet to address the task of generating attractive captions for images and videos with different styles.
Recurrent Topic-Transition GAN for Visual Paragraph Generation
The proposed Recurrent Topic-Transition Generative Adversarial Network (RTT-GAN) builds an adversarial framework between a structured paragraph generator and multi-level paragraph discriminators.
Video Captioning with Multi-Faceted Attention
Recently, video captioning has been attracting an increasing amount of interest, due to its potential for improving accessibility and information retrieval.
Semantic Compositional Networks for Visual Captioning
The degree to which each member of the ensemble is used to generate an image caption is tied to the image-dependent probability of the corresponding tag.
Strategies for Searching Video Content with Text Queries or Video Examples
The large number of user-generated videos uploaded on to the Internet everyday has led to many commercial video search engines, which mainly rely on text metadata for search.
You Lead, We Exceed: Labor-Free Video Concept Learning by Jointly Exploiting Web Videos and Images
The Web images are then filtered by the learnt network and the selected images are additionally fed into the network to enhance the architecture and further trim the videos.
Learning Attributes Equals Multi-Source Domain Generalization
Attributes possess appealing properties and benefit many computer vision problems, such as object recognition, learning with humans in the loop, and image retrieval.
Automatic Concept Discovery from Parallel Text and Visual Corpora
Humans connect language and vision to perceive the world.
DevNet: A Deep Event Network for Multimedia Event Detection and Evidence Recounting
Taking key frames of videos as input, we first detect the event of interest at the video level by aggregating the CNN features of the key frames.
