Transforming Visual Scene Graphs to Image Captions

We propose to Transform Scene Graphs (TSG) into more descriptive captions. In TSG, we apply multi-head attention (MHA) to design the Graph Neural Network (GNN) for embedding scene graphs. After embedding, different graph embeddings contain diverse specific knowledge for generating the words with different part-of-speech, e.g., object/attribute embedding is good for generating nouns/adjectives. Motivated by this, we design a Mixture-of-Expert (MOE)-based decoder, where each expert is built on MHA, for discriminating the graph embeddings to generate different kinds of words. Since both the encoder and decoder are built based on the MHA, as a result, we construct a homogeneous encoder-decoder unlike the previous heterogeneous ones which usually apply Fully-Connected-based GNN and LSTM-based decoder. The homogeneous architecture enables us to unify the training configuration of the whole model instead of specifying different training strategies for diverse sub-networks as in the heterogeneous pipeline, which releases the training difficulty. Extensive experiments on the MS-COCO captioning benchmark validate the effectiveness of our TSG. The code is in: https://anonymous.4open.science/r/ACL23_TSG.

Learning Trajectory-Word Alignments for Video-Language Tasks

Aligning objects with words plays a critical role in Image-Language BERT (IL-BERT) and Video-Language BERT (VDL-BERT). Different from the image case where an object covers some spatial patches, an object in a video usually appears as an object trajectory, i.e., it spans over a few spatial but longer temporal patches and thus contains abundant spatiotemporal contexts. However, modern VDL-BERTs neglect this trajectory characteristic that they usually follow IL-BERTs to deploy the patch-to-word (P2W) attention while such attention may over-exploit trivial spatial contexts and neglect significant temporal contexts. To amend this, we propose a novel TW-BERT to learn Trajectory-Word alignment for solving video-language tasks. Such alignment is learned by a newly designed trajectory-to-word (T2W) attention. Besides T2W attention, we also follow previous VDL-BERTs to set a word-to-patch (W2P) attention in the cross-modal encoder. Since T2W and W2P attentions have diverse structures, our cross-modal encoder is asymmetric. To further help this asymmetric cross-modal encoder build robust vision-language associations, we propose a fine-grained ``align-before-fuse'' strategy to pull close the embedding spaces calculated by the video and text encoders. By the proposed strategy and T2W attention, our TW-BERT achieves SOTA performances on text-to-video retrieval tasks, and comparable performances on video question answering tasks with some VDL-BERTs trained on much more data. The code will be available in the supplementary material.