With the surge in the amount of video data, video summarization techniques, including visual-modal(VM) and textual-modal(TM) summarization, are attracting more and more attention. However, unimodal summarization inevitably loses the rich semantics of the video. In this paper, we focus on a more comprehensive video summarization task named Bimodal Semantic Summarization of Videos(BiSSV). Specifically, we first construct a large-scale dataset, BIDS, in(video, VM-Summary, TM-Summary) triplet format. Unlike traditional processing methods, our construction procedure contains a VM-Summary extraction algorithm aiming to preserve the most salient content within long videos. Based on BIDS, we propose a Unified framework UBiSS for the BiSSV task, which models the saliency information in the video and generates a TM-summary and VM-summary simultaneously. We further optimize our model with a list-wise ranking-based objective to improve its capacity to capture highlights. Lastly, we propose a metric, NDCGMS, to provide a joint evaluation of the bimodal summary. Experiments show that our unified framework achieves better performance than multi-stage summarization pipelines. Code and data are available at https://github.com/MeiYutingg/UBiSS.

Image-text retrieval, as a fundamental and important branch of information retrieval, has attracted extensive research attentions. The main challenge of this task is cross-modal semantic understanding and matching. Some recent works focus more on fine-grained cross-modal semantic matching. With the prevalence of large scale multimodal pretraining models, several state-of-the-art models (e.g. X-VLM) have achieved near-perfect performance on widely-used image-text retrieval benchmarks, i.e. MSCOCO-Test-5K and Flickr30K-Test-1K. In this paper, we review the two common benchmarks and observe that they are insufficient to assess the true capability of models on fine-grained cross-modal semantic matching. The reason is that a large amount of images and texts in the benchmarks are coarse-grained. Based on the observation, we renovate the coarse-grained images and texts in the old benchmarks and establish the improved benchmarks called MSCOCO-FG and Flickr30K-FG. Specifically, on the image side, we enlarge the original image pool by adopting more similar images. On the text side, we propose a novel semi-automatic renovation approach to refine coarse-grained sentences into finer-grained ones with little human effort. Furthermore, we evaluate representative image-text retrieval models on our new benchmarks to demonstrate the effectiveness of our method. We also analyze the capability of models on fine-grained semantic comprehension through extensive experiments. The results show that even the state-of-the-art models have much room for improvement in fine-grained semantic understanding, especially in distinguishing attributes of close objects in images. Our code and improved benchmark datasets are publicly available1 which we hope will inspire further in-depth research on cross-modal retrieval.

Automatically generating textual descriptions for massive unlabeled images on the web can greatly benefit realistic web applications, e.g. multimodal retrieval and recommendation. However, existing models suffer from the problem of generating “over-generic” descriptions, such as their tendency to generate repetitive sentences with common concepts for different images. These generic descriptions fail to provide sufficient textual semantics for ever-changing web images. Inspired by the recent success of Vision-Language Pre-training (VLP) models that learn diverse image-text concept alignment during pretraining, we explore leveraging their cross-modal pre-trained knowledge to automatically enrich the textual semantics of image descriptions. With no need for additional human annotations, we propose a plug-and-play framework, i.e CapEnrich, to complement the generic image descriptions with more semantic details. Specifically, we first propose an automatic data-building strategy to get desired training sentences, based on which we then adopt prompting strategies, i.e. learnable and template prompts, to incentivize VLP models to generate more textual details. For learnable templates, we fix the whole VLP model and only tune the prompt vectors, which leads to two advantages: 1) the pre-training knowledge of VLP models can be reserved as much as possible to describe diverse visual concepts; 2) only lightweight trainable parameters are required, so it is friendly to low data resources. Extensive experiments show that our method significantly improves the descriptiveness and diversity of generated sentences for web images. The code is available at https://github.com/yaolinli/CapEnrich.

Image retrieval with hybrid-modality queries, also known as composing text and image for image retrieval (CTI-IR), is a retrieval task where the search intention is expressed in a more complex query format, involving both vision and text modalities. For example, a target product image is searched using a reference product image along with text about changing certain attributes of the reference image as the query. It is a more challenging image retrieval task that requires both semantic space learning and cross-modal fusion. Previous approaches that attempt to deal with both aspects achieve unsatisfactory performance. In this paper, we decompose the CTI-IR task into a three-stage learning problem to progressively learn the complex knowledge for image retrieval with hybrid-modality queries. We first leverage the semantic embedding space for open-domain image-text retrieval, and then transfer the learned knowledge to the fashion-domain with fashion-related pre-training tasks. Finally, we enhance the pre-trained model from single-query to hybrid-modality query for the CTI-IR task. Furthermore, as the contribution of individual modality in the hybrid-modality query varies for different retrieval scenarios, we propose a self-supervised adaptive weighting strategy to dynamically determine the importance of image and text in the hybrid-modality query for better retrieval. Extensive experiments show that our proposed model significantly outperforms state-of-the-art methods in the mean of [email protected] by 24.9% and 9.5% on the Fashion-IQ and Shoes benchmark datasets respectively.

Pre-training has been an emerging topic that provides a way to learn strong representation in many fields (e.g., natural language processing, computing vision). In the last few years, we have witnessed many research works on multi-modal pre-training which have achieved state-of-the-art performances on many multimedia tasks (e.g., image-text retrieval, video localization, speech recognition). In this workshop, we aim to gather peer researchers on related topics for more insightful discussion. We also intend to attract more researchers to explore and investigate more opportunities of designing and using innovative pre-training models for multimedia tasks.

Temporal Sentence Grounding aims to localize the relevant temporal region in a given video according to the query sentence. It is a challenging task due to the semantic gap between different modalities and diversity of the event duration. Proposal generation plays an important role in previous mainstream methods. However, previous proposal generation methods apply the same feature extraction without considering the diversity of event duration. In this paper, we propose a novel temporal sentence grounding model with an U-shaped Network for efficient proposal generation (UN-TSG), which utilizes U-shaped structure to encode proposals of different lengths hierarchically. Experiments on two benchmark datasets demonstrate that with more efficient proposal generation method, our model can achieve the state-of-the-art grounding performance in faster speed and with less computation cost.

Audio event recognition (AER) has been an important research problem with a wide range of applications. However, it is very challenging to develop large scale audio event recognition models. On the one hand, usually there are only "weak" labeled audio training data available, which only contains labels of audio events without temporal boundaries. On the other hand, the distribution of audio events is generally long-tailed, with only a few positive samples for large amounts of audio events. These two issues make it hard to learn discriminative acoustic features to recognize audio events especially for long-tailed events. In this paper, we propose a novel class-aware self-attention mechanism with attention factor sharing to generate discriminative clip-level features for audio event recognition. Since a target audio event only occurs in part of an entire audio clip and its corresponding temporal interval varies, the proposed class-aware self-attention approach learns to highlight relevant temporal intervals and to suppress irrelevant noises at the same time. In order to learn attention patterns effectively for those long-tailed events, we combine both the domain knowledge and data driven strategies to share attention factors in the proposed attention mechanism, which transfers the common knowledge learned from other similar events to the rare events. The proposed attention mechanism is a pluggable component and can be trained end-to-end in the overall AER model. We evaluate our model on a large-scale audio event corpus "Audio Set" with both short-term and long-term acoustic features. The experimental results demonstrate the effectiveness of our model, which improves the overall audio event recognition performance with different acoustic features especially for events with low resources. Moreover, the experiments also show that our proposed model is able to learn new audio events with a few training examples effectively and efficiently without disturbing the previously learned audio events.

Generating video descriptions in natural language (a.k.a. video captioning) is a more challenging task than image captioning as the videos are intrinsically more complicated than images in two aspects. First, videos cover a broader range of topics, such as news, music, sports and so on. Second, multiple topics could coexist in the same video. In this paper, we propose a novel caption model, topic-guided model (TGM), to generate topic-oriented descriptions for videos in the wild via exploiting topic information. In addition to predefined topics, i.e., category tags crawled from the web, we also mine topics in a data-driven way based on training captions by an unsupervised topic mining model. We show that data-driven topics reflect a better topic schema than the predefined topics. As for testing video topic prediction, we treat the topic mining model as teacher to train the student, the topic prediction model, by utilizing the full multi-modalities in the video especially the speech modality. We propose a series of caption models to exploit topic guidance, including implicitly using the topics as input features to generate words related to the topic and explicitly modifying the weights in the decoder with topics to function as an ensemble of topic-aware language decoders. Our comprehensive experimental results on the current largest video caption dataset MSR-VTT prove the effectiveness of our topic-guided model, which significantly surpasses the winning performance in the 2016 MSR video to language challenge.

The recent advances in image captioning stimulate the research in generating natural language description for visual content, which can be widely applied in many applications such as assisting blind people. Video description generation is a more complex task than image caption. Most works of video description generation focus on visual information in the video. However, audio provides rich information for describing video contents as well. In this paper, we propose to generate video descriptions in natural sentences using both audio and visual cues. We use unified deep neural networks with both convolutional and recurrent structure. Experimental results on the Microsoft Research Video Description (MSVD) corpus prove that fusing audio information greatly improves the video description performance.

State-of-the-art methods for product recommendation encounter a significant performance drop in categories where a user has no purchase history. This problem needs to be addressed since current online retailers are moving beyond single category and attempting to be diversified. In this article, we investigate the challenging problem of product recommendation in unexplored categories and discover that the price, a factor comparable across categories, can improve the recommendation performance significantly. We introduce the price utility concept to characterize users’ sense of price and propose three different utility functions. We show that user price preference in a category is a distribution and we mine typical user price preference patterns based on three different types of distance between distributions. We fuse user price preference through regularization and joint factorization to boost recommendation performance in both browsing and buying shopping orientations. Experimental results show that fusing user price preference improves performance in a series of recommendation tasks: unexplored category recommendation, product recommendation under a given unexplored category, and product recommendation under generic unexplored categories.

With the increasing use of audio sensors in user generated content (UGC) collections, semantic concept annotation from video soundtracks has become an important research problem. In this paper, we investigate reducing the semantic gap of the traditional data-driven bag-of-audio-words based audio annotation approach by utilizing the large-amount of wild audio data and their rich user tags, from which we propose a new feature representation based on semantic class model distance. We conduct experiments on the data collection from HUAWEI Accurate and Fast Mobile Video Annotation Grand Challenge 2014. We also fuse the audio-only annotation system with a visual-only system. The experimental results show that our audio-only concept annotation system can detect semantic concepts significantly better than does random guessing. The new feature representation achieves comparable annotation performance with the bag-of-audio-words feature. In addition, it can provide more semantic interpretation in the output. The experimental results also prove that the audio-only system can provide significant complementary information to the visual-only concept annotation system for performance boost and for better interpretation of semantic concepts both visually and acoustically.

Computer systems in the near future are expected to have Non-Volatile Main Memory (NVMM), enabled by a new generation of Non-Volatile Memory (NVM) technologies, such as Phase Change Memory (PCM), STT-MRAM, and Memristor. The non-volatility property has the promise to persist in-memory data structures for instantaneous failure recovery. However, realizing such promise requires a careful design to ensure that in-memory data structures are in known consistent states after failures. This paper studies persistent in-memory B + -Trees as B + -Trees are widely used in database and data-intensive systems. While traditional techniques, such as undo-redo logging and shadowing, support persistent B + -Trees, we find that they incur drastic performance overhead because of extensive NVM writes and CPU cache flush operations. PCM-friendly B + -Trees with unsorted leaf nodes help mediate this issue, but the remaining overhead is still large. In this paper, we propose write atomic B + -Trees (wB + -Trees), a new type of main-memory B + -Trees, that aim to reduce such overhead as much as possible. wB + -Tree nodes employ a small indirect slot array and/or a bitmap so that most insertions and deletions do not require the movement of index entries. In this way, wB + -Trees can achieve node consistency either through atomic writes in the nodes or by redo-only logging. We model fast NVM using DRAM on a real machine and model PCM using a cycle-accurate simulator. Experimental results show that compared with previous persistent B + -Tree solutions, wB + -Trees achieve up to 8.8x speedups on DRAM-like fast NVM and up to 27.1x speedups on PCM for insertions and deletions while maintaining good search performance. Moreover, we replaced Memcached's internal hash index with tree indices. Our real machine Memcached experiments show that wB + -Trees achieve up to 3.8X improvements over previous persistent tree structures with undo-redo logging or shadowing.

State-of-the-art methods for product recommendation encounter significant performance drop in categories where a user has no purchase history. This problem needs to be addressed since current online retailers are moving beyond single category and attempting to be diversified. In this paper, we investigate the challenge problem of product recommendation in unexplored categories and discover that the price, a factor transferrable across categories, can improve the recommendation performance significantly. Through our investigation, we address four research questions progressively: 1) what is the impact of unexplored category on recommendation performance? 2) How to represent the price factor from the recommendation point of view? 3) What does price factor across categories mean to recommendation? 4) How to utilize price factor across categories for recommendation in unexplored categories? Based on a series of experiments and analysis conducted on a dataset collected from a leading E-commerce website, we discover valuable findings for the above four questions: first, unexplored categories cause performance drop by 40% relatively for current recommendation systems; second, the price factor can be represented as either a quantity for a product or a distribution for a user to improve performance; third, consumer behavior with respect to price factor across categories is complicated and needs to be carefully modeled; finally and most importantly, we propose a new method which encodes the two perspectives of the price factor. The proposed method significantly improves the recommendation performance in unexplored categories over the state-of-the-art baseline systems and shortens the performance gap by 43% relatively.