Machine learning (ML) has become a cornerstone of key applications at ByteDance. As model complexity and data volumes surge, data management for large-scale ML workloads faces substantial challenges, particularly with recent advances in large recommendation models (LRMs) and large multimodal models (LMMs). Traditional approaches exhibit limitations in storage efficiency, metadata scalability, update mechanisms, and integration with ML frameworks. To address these challenges, we propose Magnus, a holistic data management system built upon Apache Iceberg. Magnus integrates innovative optimizations across resource-efficient storage formats optimized for large wide tables and multimodal data, built-in support for vector and inverted indexes to accelerate data retrieval, scalable metadata planning with Git-like branching and tagging capabilities, and high-performance update/upsert based on lightweight merge-on-read (MOR) strategies. Additionally, Magnus provides native support and specialized enhancement for LRM and LMM training workloads. Experimental results demonstrate significant performance gains in real-world ML scenarios. Magnus has been deployed at ByteDance for over five years, enabling robust and efficient data infrastructure for large-scale ML workloads.

We study the multi-round response generation in visual dialog, where a response is generated according to a visually grounded conversational history. Given a triplet: an image, Q&A history, and current question, all the prevailing methods follow a codec (i.e., encoder-decoder) fashion in a supervised learning paradigm: a multimodal encoder encodes the triplet into a feature vector, which is then fed into the decoder for the current answer generation, supervised by the ground-truth. However, this conventional supervised learning does NOT take into account the impact of imperfect history, violating the conversational nature of visual dialog and thus making the codec more inclined to learn history bias but not contextual reasoning. To this end, inspired by the actor-critic policy gradient in reinforcement learning, we propose a novel training paradigm called History Advantage Sequence Training (HAST). Specifically, we intentionally impose wrong answers in the history, obtaining an adverse critic, and see how the historic error impacts the codec's future behavior by History Advantage - a quantity obtained by subtracting the adverse critic from the gold reward of ground-truth history. Moreover, to make the codec more sensitive to the history, we propose a novel attention network called History-Aware Co-Attention Network (HACAN) which can be effectively trained by using HAST. Experimental results on three benchmarks: VisDial v0.9&v1.0 and GuessWhat?!, show that the proposed HAST strategy consistently outperforms the state-of-the-art supervised counterparts.

Video Question & Answering (VideoQA), a task to answer questions in videos, involves rich spatio-temporal content (e.g., appearance and motion) and requires multi-hop reasoning process. However, existing methods usually deal with appearance and motion separately and fail to synchronize the attentions on appearance and motion features, neglecting two key properties of video QA: (1) appearance and motion features are usually concomitant and complementary to each other at time slice level. Some questions rely on joint representations of both kinds of features at some point in the video; (2) appearance and motion have different importance in multi-step reasoning. In this paper, we propose a novel Question- Aware Tube-Switch Network (TSN) for video question answering which contains (1) a Mix module to synchronously combine the appearance and motion representation at time slice level, achieving fine-grained temporal alignment and correspondence between appearance and motion at every time slice and (2) a Switch mod- ule to adaptively choose appearance or motion tube as primary at each reasoning step, guiding the multi-hop reasoning process. To end-to-end train TSN, we utilize the Gumbel-Softmax strategy to account for the discrete tube-switch process. Extensive experimental results on two benchmarks: MSVD-QA and MSRVTT-QA, have demonstrated that the proposed TSN consistently outperforms state-of-the-art on all metrics.

Visual Question Answering (VQA) is to provide a natural language answer for a pair of an image or video and a natural language question. Despite recent progress on VQA, existing works primarily focus on image question answering and are suboptimal for video question answering. This article presents a novel Spatiotemporal-Textual Co-Attention Network (STCA-Net) for video question answering. The STCA-Net jointly learns spatially and temporally visual attention on videos as well as textual attention on questions. It concentrates on the essential cues in both visual and textual spaces for answering question, leading to effective question-video representation. In particular, a question-guided attention network is designed to learn question-aware video representation with a spatial-temporal attention module. It concentrates the network on regions of interest within the frames of interest across the entire video. A video-guided attention network is proposed to learn video-aware question representation with a textual attention module, leading to fine-grained understanding of question. The learned video and question representations are used by an answer predictor to generate answers. Extensive experiments on two challenging datasets of video question answering, i.e., MSVD-QA and MSRVTT-QA, have shown the effectiveness of the proposed approach.