Search Results for author:
Found 12 papers, 3 papers with code
Designing Category-Level Attributes for Discriminative Visual Recognition
In this paper, we propose a novel formulation to automatically design discriminative "category-level attributes", which can be efficiently encoded by a compact category-attribute matrix.
An exploration of parameter redundancy in deep networks with circulant projections
We explore the redundancy of parameters in deep neural networks by replacing the conventional linear projection in fully-connected layers with the circulant projection.
Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network
To address this problem, we propose a Dual Attribute-aware Ranking Network (DARN) for retrieval feature learning.
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection.
Ranked #24 on
Pedestrian Detection
on Caltech
A Recurrent Encoder-Decoder Network for Sequential Face Alignment
We propose a novel recurrent encoder-decoder network model for real-time video-based face alignment.
Automatic Curation of Golf Highlights using Multimodal Excitement Features
The production of sports highlight packages summarizing a game's most exciting moments is an essential task for broadcast media.
RED-Net: A Recurrent Encoder-Decoder Network for Video-based Face Alignment
We propose a novel method for real-time face alignment in videos based on a recurrent encoder-decoder network model.
Learning Motion in Feature Space: Locally-Consistent Deformable Convolution Networks for Fine-Grained Action Detection
Fine-grained action detection is an important task with numerous applications in robotics and human-computer interaction.
Diversity in Faces
We expect face recognition to work equally accurately for every face.
Cultural Vocal Bursts Intensity Prediction
Face Recognition
+1
Task2Sim : Towards Effective Pre-training and Transfer from Synthetic Data
It is thus better to tailor synthetic pre-training data to a specific downstream task, for best performance.
Everything at Once - Multi-Modal Fusion Transformer for Video Retrieval
In this work, we present a multi-modal, modality agnostic fusion transformer that learns to exchange information between multiple modalities, such as video, audio, and text, and integrate them into a fused representation in a joined multi-modal embedding space.
Task2Sim: Towards Effective Pre-Training and Transfer From Synthetic Data
It is thus better to tailor synthetic pre-training data to a specific downstream task, for best performance.
