In this paper we share our solution to the Biomedia ACM MM Grand Challenge 2019, which focuses on the gastrointestinal tract with an aim to detect and classify abnormalities. We firstly identify the challenges in this task, including the scarce and ...

Inspired by recent advances in computer vision and deep learning, we propose new enhancements to tackle problems appearing in endoscopic image analysis, especially abnormality finding and anatomical landmark detection. In details, a combination of ...

The development of live broadcasting represents many new technical challenges on adaptive bitrate(ABR) algorithms, which not only requires stable and high-quality transmission but also low end-to-end latency. Reinforcement learning(RL) achieves ...

In this paper, we present a method to automatically identify diseases from videos of gastrointestinal (GI) tract examinations using a Deep Convolutional Neural Network (DCNN) that processes images from digital endoscopes. Our goal is to aid domain ...

In this paper, we introduce a continuous bitrate control and latency control model for the Live Video Streaming Challenge. Our model is based on Deep Deterministic Policy Gradient, popular on continuous control tasks. Simultaneously, it can take a fine-...

The BioMedia 2019 ACM Multimedia Grand Challenge is the first in a series of competitions focusing on the use of multimedia for different medical use-cases. In this year's challenge, the participants are asked to develop efficient algorithms which ...

Sign Language is the primary means of communication for the majority of the Deaf and hard-of-hearing communities. Current computational approaches in this general research area have focused specifically on sign language recognition and the translation ...

In recent years, we have observed a rise of interest in the multimedia community towards research topics related to health. It can be observed that this goes into two interesting directions. One is personal health with a larger focus on well-being and ...

With the prevalence of accessible depth sensors, dynamic human body skeletons have attracted much attention as a robust modality for action recognition. Previous methods model skeletons based on RNN or CNN, which has limited expressive power for ...

This companion paper supports the replication of scene image recognition experiments using Adaptive Discriminative Region Discovery (Adi-Red), an approach presented at ACM Multimedia 2018. We provide a set of artifacts that allow the replication of the ...

In our recent papers, we proposed a new family of residual convolutional neural networks trained for semi-dense and sparse depth reconstruction without use of RGB channel. The proposed models can be used in low-resolution depth sensors or SLAM methods ...

Zero-Shot Learning (ZSL) seeks to recognize a sample from either seen or unseen domain by projecting the image data and semantic labels into a joint embedding space. However, most existing methods directly adapt a well-trained projection from one domain ...

Recognizing ongoing events based on acoustic clues has been a critical research problem for a variety of AI applications. Compared to visual inputs, acoustic cues tend to be less descriptive and less consistent in time domain. The duration of a sound ...

Polyphonic Acoustic Event Detection (AED) is a challenging task as the sounds are mixed with the signals from different events, and the features extracted from the mixture do not match well with features calculated from sounds in isolation, leading to ...

Instance-level image retrieval is a long-standing and challenging problem in multimedia. Recently, fine-tuning Convolutional Neural Networks (CNNs) has become a promising direction, and a number of successful strategies based on global CNN descriptors ...

Semantic understanding of 3D scenes is essential for autonomous driving. Although a number of efforts have been devoted to semantic segmentation of dense point clouds, the great sparsity of 3D LiDAR data poses significant challenges in autonomous ...

The image-based virtual try-on system has attracted a lot of research attention. The virtual try-on task is challenging since synthesizing try-on images involves the estimation of 3D transformation from 2D images, which is an ill-posed problem. ...

Weakly supervised referring expression grounding (REG) aims at localizing the referential entity in an image according to linguistic query, where the mapping between the image region (proposal) and the query is unknown in the training stage. In ...

We propose a novel self-supervised method for fine-tuning deep face representations called Face-Grouping on Graphs. We apply our method to automatic face grouping, where characters are to be separated based on their identity. To solve this problem, a ...

We focus on estimating the 3D orientation of the ground plane from a single image. We formulate the problem as an inter-mingled multi-task prediction problem by jointly optimizing for pixel-wise surface normal direction, ground plane segmentation, and ...