Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect

Despite being impactful on a variety of problems and applications, the generative adversarial nets (GANs) are remarkably difficult to train. This issue is formally analyzed by \cite{arjovsky2017towards}, who also propose an alternative direction to avoid the caveats in the minmax two-player training of GANs. The corresponding algorithm, called Wasserstein GAN (WGAN), hinges on the 1-Lipschitz continuity of the discriminator. In this paper, we propose a novel approach to enforcing the Lipschitz continuity in the training procedure of WGANs. Our approach seamlessly connects WGAN with one of the recent semi-supervised learning methods. As a result, it gives rise to not only better photo-realistic samples than the previous methods but also state-of-the-art semi-supervised learning results. In particular, our approach gives rise to the inception score of more than 5.0 with only 1,000 CIFAR-10 images and is the first that exceeds the accuracy of 90% on the CIFAR-10 dataset using only 4,000 labeled images, to the best of our knowledge.Comment: Accepted as a conference paper in International Conference on Learning Representation(ICLR). Xiang Wei and Boqing Gong contributed equally in this wor

Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect

Despite being impactful on a variety of problems and applications, the generative adversarial nets (GANs) are remarkably difficult to train. This issue is formally analyzed by \cite{arjovsky2017towards}, who also propose an alternative direction to avoid the caveats in the minmax two-player training of GANs. The corresponding algorithm, called Wasserstein GAN (WGAN), hinges on the 1-Lipschitz continuity of the discriminator. In this paper, we propose a novel approach to enforcing the Lipschitz continuity in the training procedure of WGANs. Our approach seamlessly connects WGAN with one of the recent semi-supervised learning methods. As a result, it gives rise to not only better photo-realistic samples than the previous methods but also state-of-the-art semi-supervised learning results. In particular, our approach gives rise to the inception score of more than 5.0 with only 1,000 CIFAR-10 images and is the first that exceeds the accuracy of 90% on the CIFAR-10 dataset using only 4,000 labeled images, to the best of our knowledge.Comment: Accepted as a conference paper in International Conference on Learning Representation(ICLR). Xiang Wei and Boqing Gong contributed equally in this wor

Video Timeline Modeling For News Story Understanding

In this paper, we present a novel problem, namely video timeline modeling. Our objective is to create a video-associated timeline from a set of videos related to a specific topic, thereby facilitating the content and structure understanding of the story being told. This problem has significant potential in various real-world applications, such as news story summarization. To bootstrap research in this area, we curate a realistic benchmark dataset, YouTube-News-Timeline, consisting of over $12$k timelines and $300$k YouTube news videos. Additionally, we propose a set of quantitative metrics as the protocol to comprehensively evaluate and compare methodologies. With such a testbed, we further develop and benchmark exploratory deep learning approaches to tackle this problem. We anticipate that this exploratory work will pave the way for further research in video timeline modeling. The assets are available via https://github.com/google-research/google-research/tree/master/video_timeline_modeling.Comment: Accepted as a spotlight by NeurIPS 2023, Track on Datasets and Benchmark

Unified Visual Relationship Detection with Vision and Language Models

This work focuses on training a single visual relationship detector predicting over the union of label spaces from multiple datasets. Merging labels spanning different datasets could be challenging due to inconsistent taxonomies. The issue is exacerbated in visual relationship detection when second-order visual semantics are introduced between pairs of objects. To address this challenge, we propose UniVRD, a novel bottom-up method for Unified Visual Relationship Detection by leveraging vision and language models (VLMs). VLMs provide well-aligned image and text embeddings, where similar relationships are optimized to be close to each other for semantic unification. Our bottom-up design enables the model to enjoy the benefit of training with both object detection and visual relationship datasets. Empirical results on both human-object interaction detection and scene-graph generation demonstrate the competitive performance of our model. UniVRD achieves 38.07 mAP on HICO-DET, outperforming the current best bottom-up HOI detector by 14.26 mAP. More importantly, we show that our unified detector performs as well as dataset-specific models in mAP, and achieves further improvements when we scale up the model. Our code will be made publicly available on GitHub.Comment: Accepted to ICCV 2023. Code is available at https://github.com/google-research/scenic/tree/main/scenic/projects/univr

Spatiotemporally Discriminative Video-Language Pre-Training with Text Grounding

Most of existing video-language pre-training methods focus on instance-level alignment between video clips and captions via global contrastive learning but neglect rich fine-grained local information, which is of importance to downstream tasks requiring temporal localization and semantic reasoning. In this work, we propose a simple yet effective video-language pre-training framework, namely G-ViLM, to learn discriminative spatiotemporal features. Two novel designs involving spatiotemporal grounding and temporal grouping promote learning local region-noun alignment and temporal-aware features simultaneously. Specifically, spatiotemporal grounding aggregates semantically similar video tokens and aligns them with noun phrases extracted from the caption to promote local region-noun correspondences. Moreover, temporal grouping leverages cut-and-paste to manually create temporal scene changes and then learns distinguishable features from different scenes. Comprehensive evaluations demonstrate that G-ViLM performs favorably against existing approaches on four representative downstream tasks, covering text-video retrieval, video question answering, video action recognition and temporal action localization. G-ViLM performs competitively on all evaluated tasks and in particular achieves R@10 of 65.1 on zero-shot MSR-VTT retrieval, over 9% higher than the state-of-the-art method