GPT-4o, an omni-modal model that enables vocal conversations with diverse emotions and tones, marks a milestone for omni-modal foundation models. However, empowering Large Language Models to perceive and generate images, texts, and speeches end-to-end with publicly available data remains challenging for the open-source community. Existing vision-language models rely on external tools for speech processing, while speech-language models still suffer from limited or totally without vision-understanding capabilities. To address this gap, we propose the EMOVA (EMotionally Omni-present Voice Assistant), to enable Large Language Models with end-to-end speech abilities while maintaining the leading vision-language performance. With a semantic-acoustic disentangled speech tokenizer, we surprisingly notice that omni-modal alignment can further enhance vision-language and speech abilities compared with the bi-modal aligned counterparts. Moreover, a lightweight style module is introduced for the flexible speech style controls including emotions and pitches. For the first time, EMOVA achieves state-of-the-art performance on both the vision-language and speech benchmarks, and meanwhile, supporting omni-modal spoken dialogue with vivid emotions.

Most sign language translation (SLT) methods to date require the use of gloss annotations to provide additional supervision information, however, the acquisition of gloss is not easy. To solve this problem, we first perform an analysis of existing models to confirm how gloss annotations make SLT easier. We find that it can provide two aspects of information for the model, 1) it can help the model implicitly learn the location of semantic boundaries in continuous sign language videos, 2) it can help the model understand the sign language video globally. We then propose gloss attention, which enables the model to keep its attention within video segments that have the same semantics locally, just as gloss helps existing models do. Furthermore, we transfer the knowledge of sentence-to-sentence similarity from the natural language model to our gloss attention SLT network (GASLT) to help it understand sign language videos at the sentence level. Experimental results on multiple large-scale sign language datasets show that our proposed GASLT model significantly outperforms existing methods. Our code is provided in https://github.com/YinAoXiong/GASLT.

Most of the research to date focuses on bilingual sign language translation (BSLT). However, such models are in-efficient in building multilingual sign language translation systems. To solve this problem, we introduce the multilin-gual sign language translation (MSLT) task. It aims to use a single model to complete the translation between multiple sign languages and spoken languages. Then, we propose MLSLT, the first MSLT model, which contains two novel dy-namic routing mechanisms for controlling the degree ofpa-rameter sharing between different languages. Intra-layer language-specific routing controls the proportion of data flowing through shared parameters and language-specific parameters from the token level through a soft gate within the layer, and inter-layer language-specific routing controls and learns the data flow path of different languages at the language level through a soft gate between layers. In order to evaluate the performance of MLSLT, we collect the first publicly available multilingual sign language understanding dataset, Spreadthesign-Ten (SP-10), which contains up to 100 language pairs, e.g., CSL→en, GSG→zh. Experi-mental results show that the average performance of ML-SLT outperforms the baseline MSLT model and the com-bination of multiple BSLT models in many cases. In ad-dition, we also explore zero-shot translation in sign language and find that our model can achieve comparable performance to the supervised BSLT model on some language pairs. Dataset and more details are at https://mlslt.github.io/.

In e-commerce, product related video is important content to introduce product characteristics and attract consumers. Especially in the recommendation system of e-commerce platform, video highlight detection methods are usually adopted to capture the most attractive clips for showing to consumers, so as to improve the click through rate of products. However, the effect of the current research methods applied to the actual scene is not satisfactory. Compared with other video understanding tasks, video highlight detection is relatively abstract and subjective, and it is difficult to make accurate judgment only by using visual information. Consequently, we put forward multi-modal video highlight detection task, which introduces video related linguistic information as supervised information. And we propose a graph-based commodity-aware model to solve multi-modal video highlight detection in e-commerce scene. Our model consists of multi-modal highlight detection stage and graph-based fine-tuning stage, in which we adopt graph aggregation method to fuse multi-source natural language information and introduce effective visual feature composition method for graph convolution network based highlight detection. Besides, we release the largest e-commerce video highlight detection dataset, TaoHighlight, in which the videos and related data are collected from Taobao e-commerce platform. Our model achieves state-of-art in all separate categories and overall dataset of TaoHighlight, which shows the superiority of our model.

For face presentation attack detection (PAD), most of the spoofing cues are subtle, local image patterns (e.g., local image distortion, 3D mask edge and cut photo edges). The representations of existing PAD works with simple global pooling method, however, lose the local feature discriminability. In this paper, the VLAD aggregation method is adopted to quantize local features with visual vocabulary locally partitioning the feature space, and hence preserve the local discriminability. We further propose the vocabulary separation and adaptation method to modify VLAD for cross-domain PAD task. The proposed vocabulary separation method divides vocabulary into domain-shared and domain-specific visual words to cope with the diversity of live and attack faces under the cross-domain scenario.The proposed vocabulary adaptation method imitates the maximization step of the k-means algorithm in the end-to-end training, which guarantees the visual words be close to the center of assigned local features and thus brings robust similarity measurement. We give illustrations and extensive experiments to demonstrate the effectiveness of VLAD with the proposed vocabulary separation and adaptation method on standard cross-domain PAD benchmarks. The codes are available at https://github.com/Liubinggunzu/VLAD-VSA.

Sign language translation as a kind of technology with profound social significance has attracted growing researchers' interest in recent years. However, the existing sign language translation methods need to read all the videos before starting the translation, which leads to a high inference latency and also limits their application in real-life scenarios. To solve this problem, we propose SimulSLT, the first end-to-end simultaneous sign language translation model, which can translate sign language videos into target text concurrently. SimulSLT is composed of a text decoder, a boundary predictor, and a masked encoder. We 1) use the wait-k strategy for simultaneous translation. 2) design a novel boundary predictor based on the integrate-and-fire module to output the gloss boundary, which is used to model the correspondence between the sign language video and the gloss. 3) propose an innovative re-encode method to help the model obtain more abundant contextual information, which allows the existing video features to interact fully. The experimental results conducted on the RWTH-PHOENIX-Weather 2014T dataset show that SimulSLT achieves BLEU scores that exceed the latest end-to-end non-simultaneous sign language translation model while maintaining low latency, which proves the effectiveness of our method.

Due to the popularity of video contents on the Internet, the information retrieval between videos and texts has attracted broad interest from researchers, which is a challenging cross-modal retrieval task. A common solution is to learn a joint embedding space to measure the cross-modal similarity. However, many existing approaches either pay more attention to textual information, video information, or cross-modal matching methods, but less to all three. We believe that a good video-text retrieval system should take into account all three points, fully exploiting the semantic information of both modalities and considering a comprehensive match. In this paper, we propose a Hierarchical Cross-Modal Graph Consistency Learning Network (HCGC) for video-text retrieval task, which considers multi-level graph consistency for video-text matching. Specifically, we first construct a hierarchical graph representation for the video, which includes three levels from global to local: video, clips and objects. Similarly, the corresponding text graph is constructed according to the semantic relationships among sentence, actions and entities. Then, in order to learn a better match between the video and text graph, we design three types of graph consistency (both direct and indirect): inter-graph parallel consistency, inter-graph cross consistency and intra-graph cross consistency. Extensive experimental results on different video-text datasets demonstrate the effectiveness of our approach on both text-to-video and video-to-text retrieval.

Video question generation is a challenging task in visual information retrieval, which generates questions given a sequence of video frames. The existing methods mainly tackle the problem of single-turn video question generation, but single-turn conversation usually can't meet the needs of video information acquisition. In this paper, we propose a new framework for single-turn VQG, which introduces attention mechanism to process inference of dialog history. And we introduce selection mechanism to choose from the candidate questions generated by each round of dialog history. In the framework, we leverage a recent video question answering model to predict the answer to the generated question and adopt the answer quality as rewards to fine-tune our model based on a reinforced learning mechanism. We also introduce a new task of multi-turn video question generation (M-VQG), which is generating multiple questions based on dialog history and video information to build conversation step by step. Our method achieves the state-of-the-art performance of the single-turn VQG task on two large-scale datasets, YouTube-Clips and TACoS-MultiLevel, and provides a baseline approach for M-VQG task.

Video question answering is an important task combining both Natural Language Processing and Computer Vision, which requires a machine to obtain a thorough understanding of the video. Most existing approaches simply capture spatio-temporal information in videos by using a combination of recurrent and convolutional neural networks. Nonetheless, most previous work focus on only salient frames or regions, which normally lacks some significant details, such as potential location and action relations. In this paper, we propose a new method called Graph-based Multi-interaction Network for video question answering. In our model, a new attention mechanism named multi-interaction is designed to capture both element-wise and segment-wise sequence interactions simultaneously, which can be found between and inside the multi-modal inputs. Moreover, we propose a graph-based relation-aware neural network to explore a more fine-grained visual representation, which could explore the relationships and dependencies between objects spatially and temporally. We evaluate our method on TGIF-QA and other two video QA datasets. The qualitative and quantitative experimental results show the effectiveness of our model, which achieves state-of-the-art performance.

Vision-language research has become very popular, which focuses on understanding of visual contents, language semantics and relationships between them. Video question answering (Video QA) is one of the typical tasks. Recently, several BERT style pre-training methods have been proposed and shown effectiveness on various vision-language tasks. In this work, we leverage the successful vision-language transformer structure to solve the Video QA problem. However, we do not pre-train it with any video data, because video pre-training requires massive computing resources and is hard to perform with only a few GPUs. Instead, our work aims to leverage image-language pre-training to help with video-language modeling, by sharing a common module design. We further introduce an adaptive spatio-temporal graph to enhance the vision-language representation learning. That is, we adaptively refine the spatio-temporal tubes of salient objects according to their spatio-temporal relations learned through a hierarchical graph convolution process. Finally, we can obtain a number of fine-grained tube-level video object representations, as the visual inputs of the vision-language transformer module. Experiments on three widely used Video QA datasets show that our model achieves the new state-of-the-art results.

Video dialog is a new and challenging task, which requires an AI agent to maintain a meaningful dialog with humans in natural language about video contents. Specifically, given a video, a dialog history and a new question about the video, the agent has to combine video information with dialog history to infer the answer. However, the existing methods of image dialog and video question answering, which fail to process the complexity of video information and establish the logical dependency of history contexts, are inappropriate to be applied directly to video dialog. In this paper, we propose a novel approach for video dialog called multi-grained convolutional self-attention context network, which combines video information with dialog history. Instead of using RNN to encode the sequence information, we design a multi-grained convolutional self-attention mechanism to capture both element and segment level interactions that contain multi-grained sequence information. Moreover, a hierarchical dialog history encoder is designed to learn the context-aware question representation. Finally, we establish two decoders in multiple-choice and open-ended forms respectively, which utilize different strategies to get the multi-model context-aware video representation and to generate human-like answers. We evaluate our method on two large-scale datasets. Due to the flexibility and parallelism of the new attention mechanism, our method can achieve higher time efficiency, and the extensive experiments also show the effectiveness of our method.

Weike Jin, Zhou Zhao, Mao Gu, Jun Xiao, Furu Wei, Yueting Zhuang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Video question answering is an important task for testing machine's ability of video understanding. The existing methods normally focus on the combination of recurrent and convolutional neural networks to capture spatial and temporal information of the video. Recently, some work has also shown that using attention mechanism can achieve better performance. In this paper, we propose a new model called Multi-interaction network for video question answering. There are two types of interactions in our model. The first type is the multi-modal interaction between the visual and textual information. The second type is the multi-level interaction inside the multi-modal interaction. Specifically, instead of using original self-attention, we propose a new attention mechanism called multi-interaction, which can capture both element-wise and segment-wise sequence interactions, simultaneously. And in addition to the normal frame-level interaction, we also take the object relations into consideration, in order to obtain more fine-grained information, such as motions and other potential relations among these objects. We evaluate our method on TGIF-QA and other two video QA datasets. The qualitative and quantitative experimental results show the effectiveness of our model, which achieves the new state-of-the-art performance.

Video dialog is a new and challenging task, which requires an AI agent to maintain a meaningful dialog with humans in natural language about video contents. Specifically, given a video, a dialog history and a new question about the video, the agent has to combine video information with dialog history to infer the answer. And due to the complexity of video information, the methods of image dialog might be ineffectively applied directly to video dialog. In this paper, we propose a novel approach for video dialog called multi-grained convolutional self-attention context network, which combines video information with dialog history. Instead of using RNN to encode the sequence information, we design a multi-grained convolutional self-attention mechanism to capture both element and segment level interactions which contain multi-grained sequence information. Then, we design a hierarchical dialog history encoder to learn the context-aware question representation and a two-stream video encoder to learn the context-aware video representation. We evaluate our method on two large-scale datasets. Due to the flexibility and parallelism of the new attention mechanism, our method can achieve higher time efficiency, and the extensive experiments also show the effectiveness of our method.

Visual Question Answering (VQA) is a challenging task that has gained increasing attention from both the computer vision and the natural language processing communities in recent years. Given a question in natural language, a VQA system is designed to automatically generate the answer according to the referenced visual content. Though there recently has been much intereset in this topic, the existing work of visual question answering mainly focuses on a single static image, which is only a small part of the dynamic and sequential visual data in the real world. As a natural extension, video question answering (VideoQA) is less explored. Because of the inherent temporal structure in the video, the approaches of ImageQA may be ineffectively applied to video question answering. In this article, we not only take the spatial and temporal dimension of video content into account but also employ an external knowledge base to improve the answering ability of the network. More specifically, we propose a knowledge-based progressive spatial-temporal attention network to tackle this problem. We obtain both objects and region features of the video frames from a region proposal network. The knowledge representation is generated by a word-level attention mechanism using the comment information of each object that is extracted from DBpedia. Then, we develop a question-knowledge-guided progressive spatial-temporal attention network to learn the joint video representation for video question answering task. We construct a large-scale video question answering dataset. The extensive experiments based on two different datasets validate the effectiveness of our method.