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Found 37 papers, 17 papers with code
Token Pooling in Vision Transformers
Token Pooling is a simple and effective operator that can benefit many architectures.
LCS: Learning Compressible Subspaces for Adaptive Network Compression at Inference Time
Our models require no retraining, thus our subspace of models can be deployed entirely on-device to allow adaptive network compression at inference time.
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer
In this paper, we ask the following question: is it possible to combine the strengths of CNNs and ViTs to build a light-weight and low latency network for mobile vision tasks?
Ranked #348 on
Image Classification
on ImageNet
DKM: Differentiable K-Means Clustering Layer for Neural Network Compression
For MobileNet-v1, which is a challenging DNN to compress, DKM delivers 63. 9% top-1 ImageNet1k accuracy with 0. 72 MB model size (22. 4x model compression factor).
Learning Neural Network Subspaces
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.
Layer-Wise Data-Free CNN Compression
In the high-computation regime, we show how to combine our method with generative methods to improve upon state-of-the-art performance for several networks.
Supermasks in Superposition
We present the Supermasks in Superposition (SupSup) model, capable of sequentially learning thousands of tasks without catastrophic forgetting.
What's Hidden in a Randomly Weighted Neural Network?
Training a neural network is synonymous with learning the values of the weights.
Ranked #441 on
Image Classification
on ImageNet
DeFINE: DEep Factorized INput Token Embeddings for Neural Sequence Modeling
For sequence models with large vocabularies, a majority of network parameters lie in the input and output layers.
Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors
We present a simple and effective learning technique that significantly improves mAP of YOLO object detectors without compromising their speed.
DiCENet: Dimension-wise Convolutions for Efficient Networks
When DiCE units are stacked to build the DiCENet model, we observe significant improvements over state-of-the-art models across various computer vision tasks including image classification, object detection, and semantic segmentation.
Ranked #17 on
Semantic Segmentation
on PASCAL VOC 2012 val
Butterfly Transform: An Efficient FFT Based Neural Architecture Design
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.
Discovering Neural Wirings
In this work we propose a method for discovering neural wirings.
OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge
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.
Two Body Problem: Collaborative Visual Task Completion
Collaboration is a necessary skill to perform tasks that are beyond one agent's capabilities.
ELASTIC: Improving CNNs with Dynamic Scaling Policies
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.
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning
In this paper we study the problem of learning to learn at both training and test time in the context of visual navigation.
Ranked #2 on
Visual Navigation
on AI2-THOR
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
Compared to YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4. 4% higher accuracy with 6x fewer FLOPs.
Ranked #37 on
Object Detection
on PASCAL VOC 2007
Pyramidal Recurrent Unit for Language Modeling
We introduce the Pyramidal Recurrent Unit (PRU), which enables learning representations in high dimensional space with more generalization power and fewer parameters.
Label Refinery: Improving ImageNet Classification through Label Progression
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.
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints.
Ranked #54 on
Semantic Segmentation
on PASCAL VOC 2012 test
IQA: Visual Question Answering in Interactive Environments
Our experiments show that our proposed model outperforms popular single controller based methods on IQUAD V1.
LCNN: Lookup-based Convolutional Neural Network
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.
CNN-aware Binary Map for General Semantic Segmentation
To the best of our knowledge our method is the first attempt on general semantic image segmentation using CNN.
Newtonian Scene Understanding: Unfolding the Dynamics of Objects in Static Images
Direct and explicit estimation of the forces and the motion of objects from a single image is extremely challenging.
"What happens if..." Learning to Predict the Effect of Forces in Images
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.
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks.
G-CNN: an Iterative Grid Based Object Detector
G-CNN starts with a multi-scale grid of fixed bounding boxes.
Action Recognition with Image Based CNN Features
Action recognition tasks usually relies on complex handcrafted structures as features to represent the human action model.
Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images
Direct and explicit estimation of the forces and the motion of objects from a single image is extremely challenging.
On Large-Scale Retrieval: Binary or n-ary Coding?
To make large-scale search feasible, Distance Estimation and Subset Indexing are the main approaches.
Class Consistent Multi-Modal Fusion With Binary Features
Many existing recognition algorithms combine different modalities based on training accuracy but do not consider the possibility of noise at test time.
Discriminative and Consistent Similarities in Instance-Level Multiple Instance Learning
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.
Computationally Bounded Retrieval
We demonstrate this technique on large retrieval databases, specifically ImageNET, GIST1M and SUN-attribute for the task of nearest neighbor retrieval, and show that our method achieves a speed-up of up to a factor of 100 over state-of-the-art methods, while having on-par and in some cases even better accuracy.
Comparing apples to apples in the evaluation of binary coding methods
We discuss methodological issues related to the evaluation of unsupervised binary code construction methods for nearest neighbor search.
Multi-attribute Queries: To Merge or Not to Merge?
We exploit a discriminative binary space to compute these geometric quantities efficiently.
Adding Unlabeled Samples to Categories by Learned Attributes
We propose a method to expand the visual coverage of training sets that consist of a small number of labeled examples using learned attributes.