Search Results for author:
Found 20 papers, 6 papers with code
CycleCL: Self-supervised Learning for Periodic Videos
We start from the insight that a good visual representation for periodic data should be sensitive to the phase of a cycle, but be invariant to the exact repetition, i. e. it should generate identical representations for a specific phase throughout all repetitions.
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types
Our study leads to several insights and concrete recommendations: (1) for most tasks there exists a source which significantly outperforms ILSVRC'12 pre-training; (2) the image domain is the most important factor for achieving positive transfer; (3) the source dataset should \emph{include} the image domain of the target dataset to achieve best results; (4) at the same time, we observe only small negative effects when the image domain of the source task is much broader than that of the target; (5) transfer across task types can be beneficial, but its success is heavily dependent on both the source and target task types.
Towards Reusable Network Components by Learning Compatible Representations
This paper proposes to make a first step towards compatible and hence reusable network components.
Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections
Our approach enables the adaptation to a particular object and its background, to distributions shifts in a test set, to specific object classes, and even to large domain changes, where the imaging modality changes between training and testing.
Ranked #1 on
Interactive Segmentation
on DRIONS-DB
Natural Vocabulary Emerges from Free-Form Annotations
This vocabulary represents the natural distribution of objects well and is learned directly from data, instead of being an educated guess done before collecting any labels.
Efficient Object Annotation via Speaking and Pointing
We then combine the two stages: annotators draw an object bounding box via the mouse and simultaneously provide its class label via speech.
Fast Object Class Labelling via Speech
Modern approaches rely on a hierarchical organization of the vocabulary to reduce annotation time, but remain expensive (several minutes per image for the 200 classes in ILSVRC).
PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation
Highlight detection models are typically trained to identify cues that make visual content appealing or interesting for the general public, with the objective of reducing a video to such moments.
Interactive Video Object Segmentation in the Wild
The backbone of our system is a method for one-shot video object segmentation.
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
Shot boundary detection (SBD) is an important component of many video analysis tasks, such as action recognition, video indexing, summarization and editing.
Query-adaptive Video Summarization via Quality-aware Relevance Estimation
Although the problem of automatic video summarization has recently received a lot of attention, the problem of creating a video summary that also highlights elements relevant to a search query has been less studied.
Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs
We approach structured output prediction by optimizing a deep value network (DVN) to precisely estimate the task loss on different output configurations for a given input.
PathTrack: Fast Trajectory Annotation with Path Supervision
We further validate our approach by crowdsourcing the PathTrack dataset, with more than 15, 000 person trajectories in 720 sequences.
AENet: Learning Deep Audio Features for Video Analysis
Instead, combining visual features with our AENet features, which can be computed efficiently on a GPU, leads to significant performance improvements on action recognition and video highlight detection.
Predicting When Saliency Maps Are Accurate and Eye Fixations Consistent
Many computational models of visual attention use image features and machine learning techniques to predict eye fixation locations as saliency maps.
Video2GIF: Automatic Generation of Animated GIFs from Video
We introduce the novel problem of automatically generating animated GIFs from video.
Deep Convolutional Neural Networks and Data Augmentation for Acoustic Event Detection
We propose a novel method for Acoustic Event Detection (AED).
Sound Multimedia
Video Summarization by Learning Submodular Mixtures of Objectives
We present a novel method for summarizing raw, casually captured videos.
Creating Summaries from User Videos
Thereby we focus on user videos, which are raw videos containing a set of interesting events.
Sparse Quantization for Patch Description
We demonstrate the capabilities of our formulation for both keypoint matching and image classification.
