Search Results for author:
Found 18 papers, 7 papers with code
Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation
Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks.
Ranked #1 on
Action Segmentation
on MPII Cooking 2 Dataset
Vi2CLR: Video and Image for Visual Contrastive Learning of Representation
In this paper, we introduce a novel self-supervised visual representation learning method which understands both images and videos in a joint learning fashion.
3D CNNs with Adaptive Temporal Feature Resolutions
While the GFLOPs of a 3D CNN can be decreased by reducing the temporal feature resolution within the network, there is no setting that is optimal for all input clips.
Self-Supervised Ranking for Representation Learning
We present a new framework for self-supervised representation learning by formulating it as a ranking problem in an image retrieval context on a large number of random views (augmentations) obtained from images.
Large Scale Holistic Video Understanding
HVU is organized hierarchically in a semantic taxonomy that focuses on multi-label and multi-task video understanding as a comprehensive problem that encompasses the recognition of multiple semantic aspects in the dynamic scene.
Ranked #11 on
Action Recognition
on UCF101
DynamoNet: Dynamic Action and Motion Network
With these overall objectives, to this end, we introduce a novel unified spatio-temporal 3D-CNN architecture (DynamoNet) that jointly optimizes the video classification and learning motion representation by predicting future frames as a multi-task learning problem.
Spatio-Temporal Channel Correlation Networks for Action Classification
Our experiments show that adding STC blocks to current state-of-the-art architectures outperforms the state-of-the-art methods on the HMDB51, UCF101 and Kinetics datasets.
Classification-Driven Dynamic Image Enhancement
In this paper, we are interested in learning CNNs that can emulate image enhancement and restoration, but with the overall goal to improve image classification and not necessarily human perception.
Weakly Supervised Object Discovery by Generative Adversarial & Ranking Networks
We approach GANs with a novel training method and learning objective, to discover multiple object instances for three cases: 1) synthesizing a picture of a specific object within a cluttered scene; 2) localizing different categories in images for weakly supervised object detection; and 3) improving object discov- ery in object detection pipelines.
Ranked #2 on
Weakly Supervised Object Detection
on COCO test-dev
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
Thus, by finetuning this network, we beat the performance of generic and recent methods in 3D CNNs, which were trained on large video datasets, e. g. Sports-1M, and finetuned on the target datasets, e. g. HMDB51/UCF101.
Classification Driven Dynamic Image Enhancement
In this paper, we are interested in learning CNNs that can emulate image enhancement and restoration, but with the overall goal to improve image classification and not necessarily human perception.
Weakly Supervised Cascaded Convolutional Networks
The final stage of both architectures is a part of a convolutional neural network that performs multiple instance learning on proposals extracted in the previous stage(s).
Ranked #2 on
Weakly Supervised Object Detection
on ImageNet
Deep Temporal Linear Encoding Networks
Advantages of TLEs are: (a) they encode the entire video into a compact feature representation, learning the semantics and a discriminative feature space; (b) they are applicable to all kinds of networks like 2D and 3D CNNs for video classification; and (c) they model feature interactions in a more expressive way and without loss of information.
Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification
The video and action classification have extremely evolved by deep neural networks specially with two stream CNN using RGB and optical flow as inputs and they present outstanding performance in terms of video analysis.
DeepCAMP: Deep Convolutional Action & Attribute Mid-Level Patterns
On the other hand, we let an iteration of feature learning and patch clustering purify the set of dedicated patches that we use.
DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers
In this paper, a new method for generating object and action proposals in images and videos is proposed.
DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers
We generate hypotheses in a sliding-window fashion over different activation layers and show that the final convolutional layers can find the object of interest with high recall but poor localization due to the coarseness of the feature maps.
Multi-attribute Queries: To Merge or Not to Merge?
We exploit a discriminative binary space to compute these geometric quantities efficiently.
