The PASCAL Visual Object Classes Challenge

As an interesting and emerging topic, co-saliency detection aims at simultaneously extracting common salient objects from a group of images On one hand, traditional co-saliency detection approaches rely heavily on human knowledge for designing hand-crafted metrics to possibly reflect the faithful properties of the co-salient regions Such strategies, however, always suffer from poor generalization capability to flexibly adapt various scenarios in real applications On the other hand, most current methods pursue co-saliency detection in unsupervised fashions This, however, tends to weaken their performance in real complex scenarios because they are lack of robust learning mechanism to make full use of the weak labels of each image To alleviate these two problems, this paper proposes a new SP-MIL framework for co-saliency detection, which integrates both multiple instance learning (MIL) and self-paced learning (SPL) into a unified learning framework Specifically, for the first problem, we formulate the co-saliency detection problem as a MIL paradigm to learn the discriminative classifiers to detect the co-saliency object in the “instance-level” The formulated MIL component facilitates our method capable of automatically producing the proper metrics to measure the intra-image contrast and the inter-image consistency for detecting co-saliency in a purely self-learning way For the second problem, the embedded SPL paradigm is able to alleviate the data ambiguity under the weak supervision of co-saliency detection and guide a robust learning manner in complex scenarios Experiments on benchmark datasets together with multiple extended computer vision applications demonstrate the superiority of the proposed framework beyond the state-of-the-arts

Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework

This paper addresses the problem of unsupervised video summarization, formulated as selecting a sparse subset of video frames that optimally represent the input video. Our key idea is to learn a deep summarizer network to minimize distance between training videos and a distribution of their summarizations, in an unsupervised way. Such a summarizer can then be applied on a new video for estimating its optimal summarization. For learning, we specify a novel generative adversarial framework, consisting of the summarizer and discriminator. The summarizer is the autoencoder long short-term memory network (LSTM) aimed at, first, selecting video frames, and then decoding the obtained summarization for reconstructing the input video. The discriminator is another LSTM aimed at distinguishing between the original video and its reconstruction from the summarizer. The summarizer LSTM is cast as an adversary of the discriminator, i.e., trained so as to maximally confuse the discriminator. This learning is also regularized for sparsity. Evaluation on four benchmark datasets, consisting of videos showing diverse events in first-and third-person views, demonstrates our competitive performance in comparison to fully supervised state-of-the-art approaches.

/pdf/unsupervised-video-summarization-with-adversarial-lstm-1hpbd4fwpj.pdf

Unsupervised Video Summarization with Adversarial LSTM Networks

We propose a novel supervised learning technique for summarizing videos by automatically selecting keyframes or key subshots. Casting the problem as a structured prediction problem on sequential data, our main idea is to use Long Short-Term Memory (LSTM), a special type of recurrent neural networks to model the variable-range dependencies entailed in the task of video summarization. Our learning models attain the state-of-the-art results on two benchmark video datasets. Detailed analysis justifies the design of the models. In particular, we show that it is crucial to take into consideration the sequential structures in videos and model them. Besides advances in modeling techniques, we introduce techniques to address the need of a large number of annotated data for training complex learning models. There, our main idea is to exploit the existence of auxiliary annotated video datasets, albeit heterogeneous in visual styles and contents. Specifically, we show domain adaptation techniques can improve summarization by reducing the discrepancies in statistical properties across those datasets.

Video Summarization with Long Short-term Memory

We propose a novel supervised learning technique for summarizing videos by automatically selecting keyframes or key subshots. Casting the task as a structured prediction problem, our main idea is to use Long Short-Term Memory (LSTM) to model the variable-range temporal dependency among video frames, so as to derive both representative and compact video summaries. The proposed model successfully accounts for the sequential structure crucial to generating meaningful video summaries, leading to state-of-the-art results on two benchmark datasets. In addition to advances in modeling techniques, we introduce a strategy to address the need for a large amount of annotated data for training complex learning approaches to summarization. There, our main idea is to exploit auxiliary annotated video summarization datasets, in spite of their heterogeneity in visual styles and contents. Specifically, we show that domain adaptation techniques can improve learning by reducing the discrepancies in the original datasets’ statistical properties.

Video Summarization with Long Short-Term Memory

Video summarization has unprecedented importance to help us digest, browse, and search today's ever-growing video collections. We propose a novel subset selection technique that leverages supervision in the form of humancreated summaries to perform automatic keyframe-based video summarization. The main idea is to nonparametrically transfer summary structures from annotated videos to unseen test videos. We show how to extend our method to exploit semantic side information about the video's category/ genre to guide the transfer process by those training videos semantically consistent with the test input. We also show how to generalize our method to subshot-based summarization, which not only reduces computational costs but also provides more flexible ways of defining visual similarity across subshots spanning several frames. We conduct extensive evaluation on several benchmarks and demonstrate promising results, outperforming existing methods in several settings.

/pdf/summary-transfer-exemplar-based-subset-selection-for-video-51mweotdc7.pdf

Summary Transfer: Exemplar-Based Subset Selection for Video Summarization

Supervised learning techniques have shown substantial progress on video summarization. State-of-the-art approaches mostly regard the predicted summary and the human summary as two sequences (sets), and minimize discriminative losses that measure element-wise discrepancy. Such training objectives do not explicitly model how well the predicted summary preserves semantic information in the video. Moreover, those methods often demand a large amount of human generated summaries. In this paper, we propose a novel sequence-to-sequence learning model to address these deficiencies. The key idea is to complement the discriminative losses with another loss which measures if the predicted summary preserves the same information as in the original video. To this end, we propose to augment standard sequence learning models with an additional “retrospective encoder” that embeds the predicted summary into an abstract semantic space. The embedding is then compared to the embedding of the original video in the same space. The intuition is that both embeddings ought to be close to each other for a video and its corresponding summary. Thus our approach adds to the discriminative loss a metric learning loss that minimizes the distance between such pairs while maximizing the distances between unmatched ones. One important advantage is that the metric learning loss readily allows learning from videos without human generated summaries. Extensive experimental results show that our model outperforms existing ones by a large margin in both supervised and semi-supervised settings.

/pdf/retrospective-encoders-for-video-summarization-1u6ngtbhzm.pdf

Retrospective Encoders for Video Summarization

Video summarization has unprecedented importance to help us digest, browse, and search today's ever-growing video collections. We propose a novel subset selection technique that leverages supervision in the form of human-created summaries to perform automatic keyframe-based video summarization. The main idea is to nonparametrically transfer summary structures from annotated videos to unseen test videos. We show how to extend our method to exploit semantic side information about the video's category/genre to guide the transfer process by those training videos semantically consistent with the test input. We also show how to generalize our method to subshot-based summarization, which not only reduces computational costs but also provides more flexible ways of defining visual similarity across subshots spanning several frames. We conduct extensive evaluation on several benchmarks and demonstrate promising results, outperforming existing methods in several settings.

Ke Zhang

Papers

Video Summarization with Long Short-term Memory

Video Summarization with Long Short-Term Memory

Summary Transfer: Exemplar-Based Subset Selection for Video Summarization

Retrospective Encoders for Video Summarization

Summary Transfer: Exemplar-based Subset Selection for Video Summarization