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Found 46 papers, 20 papers with code
Online Detection and Classification of Dynamic Hand Gestures With Recurrent 3D Convolutional Neural Network
Automatic detection and classification of dynamic hand gestures in real-world systems intended for human computer interaction is challenging as: 1) there is a large diversity in how people perform gestures, making detection and classification difficult; 2) the system must work online in order to avoid noticeable lag between performing a gesture and its classification; in fact, a negative lag (classification before the gesture is finished) is desirable, as feedback to the user can then be truly instantaneous.
Pruning Convolutional Neural Networks for Resource Efficient Inference
We propose a new criterion based on Taylor expansion that approximates the change in the cost function induced by pruning network parameters.
A Lightweight Approach for On-the-Fly Reflectance Estimation
In addition, we have created a large synthetic dataset, SynBRDF, which comprises a total of $500$K RGBD images rendered with a physically-based ray tracer under a variety of natural illumination, covering $5000$ materials and $5000$ shapes.
Improving Landmark Localization with Semi-Supervised Learning
First, we propose the framework of sequential multitasking and explore it here through an architecture for landmark localization where training with class labels acts as an auxiliary signal to guide the landmark localization on unlabeled data.
Ranked #41 on
Face Alignment
on 300W
Budget-Aware Activity Detection with A Recurrent Policy Network
In this paper, we address the challenging problem of efficient temporal activity detection in untrimmed long videos.
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
Official Torch7 implementation of "V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map", CVPR 2018
Ranked #5 on
Hand Pose Estimation
on HANDS 2017
Hand Pose Estimation via Latent 2.5D Heatmap Regression
Estimating the 3D pose of a hand is an essential part of human-computer interaction.
IamNN: Iterative and Adaptive Mobile Neural Network for Efficient Image Classification
Deep residual networks (ResNets) made a recent breakthrough in deep learning.
Making Convolutional Networks Recurrent for Visual Sequence Learning
Recurrent neural networks (RNNs) have emerged as a powerful model for a broad range of machine learning problems that involve sequential data.
Towards annotation-efficient segmentation via image-to-image translation
Specifically, we propose a semi-supervised framework that employs unpaired image-to-image translation between two domains, presence vs. absence of cancer, as the unsupervised objective.
SCOPS: Self-Supervised Co-Part Segmentation
Parts provide a good intermediate representation of objects that is robust with respect to the camera, pose and appearance variations.
Ranked #4 on
Unsupervised Keypoint Estimation
on CUB
Few-Shot Adaptive Gaze Estimation
Inter-personal anatomical differences limit the accuracy of person-independent gaze estimation networks.
Ranked #1 on
Gaze Estimation
on MPII Gaze
(using extra training data)
Importance Estimation for Neural Network Pruning
On ResNet-101, we achieve a 40% FLOPS reduction by removing 30% of the parameters, with a loss of 0. 02% in the top-1 accuracy on ImageNet.
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion
We introduce DeepInversion, a new method for synthesizing images from the image distribution used to train a deep neural network.
HarDNN: Feature Map Vulnerability Evaluation in CNNs
As Convolutional Neural Networks (CNNs) are increasingly being employed in safety-critical applications, it is important that they behave reliably in the face of hardware errors.
Weakly-Supervised 3D Human Pose Learning via Multi-view Images in the Wild
One major challenge for monocular 3D human pose estimation in-the-wild is the acquisition of training data that contains unconstrained images annotated with accurate 3D poses.
Monocular 3D Human Pose Estimation
Weakly-superavised 3D Human Pose Estimation
+1
Weakly Supervised 3D Hand Pose Estimation via Biomechanical Constraints
Estimating 3D hand pose from 2D images is a difficult, inverse problem due to the inherent scale and depth ambiguities.
Ranked #10 on
3D Hand Pose Estimation
on DexYCB
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction
To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set.
DexYCB: A Benchmark for Capturing Hand Grasping of Objects
We introduce DexYCB, a new dataset for capturing hand grasping of objects.
See through Gradients: Image Batch Recovery via GradInversion
In this work, we introduce GradInversion, using which input images from a larger batch (8 - 48 images) can also be recovered for large networks such as ResNets (50 layers), on complex datasets such as ImageNet (1000 classes, 224x224 px).
KAMA: 3D Keypoint Aware Body Mesh Articulation
We present KAMA, a 3D Keypoint Aware Mesh Articulation approach that allows us to estimate a human body mesh from the positions of 3D body keypoints.
Ranked #48 on
3D Human Pose Estimation
on 3DPW
Adversarial Motion Modelling helps Semi-supervised Hand Pose Estimation
Hand pose estimation is difficult due to different environmental conditions, object- and self-occlusion as well as diversity in hand shape and appearance.
Optimal Quantization Using Scaled Codebook
We study the problem of quantizing N sorted, scalar datapoints with a fixed codebook containing K entries that are allowed to be rescaled.
LANA: Latency Aware Network Acceleration
We analyze three popular network architectures: EfficientNetV1, EfficientNetV2 and ResNeST, and achieve accuracy improvement for all models (up to $3. 0\%$) when compressing larger models to the latency level of smaller models.
Privacy Vulnerability of Split Computing to Data-Free Model Inversion Attacks
Prior works usually assume that SC offers privacy benefits as only intermediate features, instead of private data, are shared from devices to the cloud.
Hardware-Aware Network Transformation
In the second phase, it solves the combinatorial selection of efficient operations using a novel constrained integer linear optimization approach.
Global Vision Transformer Pruning with Hessian-Aware Saliency
This work aims on challenging the common design philosophy of the Vision Transformer (ViT) model with uniform dimension across all the stacked blocks in a model stage, where we redistribute the parameters both across transformer blocks and between different structures within the block via the first systematic attempt on global structural pruning.
HALP: Hardware-Aware Latency Pruning
We propose Hardware-Aware Latency Pruning (HALP) that formulates structural pruning as a global resource allocation optimization problem, aiming at maximizing the accuracy while constraining latency under a predefined budget.
When to Prune? A Policy towards Early Structural Pruning
Through extensive experiments on ImageNet, we show that EPI empowers a quick tracking of early training epochs suitable for pruning, offering same efficacy as an otherwise ``oracle'' grid-search that scans through epochs and requires orders of magnitude more compute.
GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras
Since the joint reconstruction of human motions and camera poses is underconstrained, we propose a global trajectory predictor that generates global human trajectories based on local body movements.
Ranked #1 on
Global 3D Human Pose Estimation
on EMDB
AdaViT: Adaptive Tokens for Efficient Vision Transformer
A-ViT achieves this by automatically reducing the number of tokens in vision transformers that are processed in the network as inference proceeds.
Ranked #34 on
Efficient ViTs
on ImageNet-1K (with DeiT-S)
Do Gradient Inversion Attacks Make Federated Learning Unsafe?
Federated learning (FL) allows the collaborative training of AI models without needing to share raw data.
GradViT: Gradient Inversion of Vision Transformers
In this work we demonstrate the vulnerability of vision transformers (ViTs) to gradient-based inversion attacks.
DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars
In this work, we present, DRaCoN, a framework for learning full-body volumetric avatars which exploits the advantages of both the 2D and 3D neural rendering techniques.
Global Context Vision Transformers
Pre-trained GC ViT backbones in downstream tasks of object detection, instance segmentation, and semantic segmentation using MS COCO and ADE20K datasets outperform prior work consistently.
Ranked #132 on
Semantic Segmentation
on ADE20K
Structural Pruning via Latency-Saliency Knapsack
We propose Hardware-Aware Latency Pruning (HALP) that formulates structural pruning as a global resource allocation optimization problem, aiming at maximizing the accuracy while constraining latency under a predefined budget on targeting device.
RANA: Relightable Articulated Neural Avatars
We propose RANA, a relightable and articulated neural avatar for the photorealistic synthesis of humans under arbitrary viewpoints, body poses, and lighting.
Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models
Our Landmark DEQ (LDEQ) achieves state-of-the-art performance on the challenging WFLW facial landmark dataset, reaching $3. 92$ NME with fewer parameters and a training memory cost of $\mathcal{O}(1)$ in the number of recurrent modules.
Ranked #2 on
Face Alignment
on WFLW
FasterViT: Fast Vision Transformers with Hierarchical Attention
At a high level, global self-attentions enable the efficient cross-window communication at lower costs.
Heterogeneous Continual Learning
We propose a novel framework and a solution to tackle the continual learning (CL) problem with changing network architectures.
Adaptive Sharpness-Aware Pruning for Robust Sparse Networks
Robustness and compactness are two essential attributes of deep learning models that are deployed in the real world.
PACE: Human and Camera Motion Estimation from in-the-wild Videos
This design combines the strengths of SLAM and motion priors, which leads to significant improvements in human and camera motion estimation.
AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One
A handful of visual foundation models (VFMs) have recently emerged as the backbones for numerous downstream tasks.
VILA: On Pre-training for Visual Language Models
Visual language models (VLMs) rapidly progressed with the recent success of large language models.
Ranked #21 on
Visual Question Answering
on MM-Vet
DoRA: Weight-Decomposed Low-Rank Adaptation
By employing DoRA, we enhance both the learning capacity and training stability of LoRA while avoiding any additional inference overhead.
LITA: Language Instructed Temporal-Localization Assistant
In addition to leveraging existing video datasets with timestamps, we propose a new task, Reasoning Temporal Localization (RTL), along with the dataset, ActivityNet-RTL, for learning and evaluating this task.
