Generating realistic and coordinated 3D human motion for multiple individuals within complex environments remains a significant challenge. Existing text-to-motion methods are often ``blind'' to the physical scene, leading to implausible motions, while scene-conditioned (HSI) approaches demand cumbersome full 3D data and largely neglect multi-person dynamics. To address these limitations, we introduce the VL2Motion paradigm and its embodiment, MMG-VL, a hierarchical framework that generates coordinated multi-person motions from the most accessible inputs: a single 2D image and natural language. MMG-VL first employs a Scene-Aware Intent Planner (SAIP) to interpret the visual context and decompose the user's command into a set of spatially-grounded, multi-person action blueprints. Subsequently, a Coordinated Motion Synthesizer (CMS) translates these blueprints into high-fidelity 3D motion sequences. The synergy between these stages is driven by two novel loss functions: a Spatial-Semantic Grounding Loss to ensure the planner's output is grounded in visual reality, and a Coordinated Environmental Realism Loss that enforces physical constraints and coherent group dynamics during synthesis. To facilitate this research, we introduce HumanVL, the first large-scale dataset featuring multi-person activities in multi-room scenes, providing aligned images, text, blueprints, 3D motions, and scene geometry. Extensive experiments demonstrate that MMG-VL significantly outperforms existing methods in generating spatially coherent, physically realistic, and coordinated multi-person motions, paving the way for more scalable and intuitive creation of dynamic virtual worlds.

Parsing Natural Language to SQL (NL2SQL) helps users who are not proficient in databases to efficiently query desired data through natural language. Although existing NL2SQL parsers demonstrate good capabilities in processing clear queries, ambiguity still remains an unresolved issue which makes parsers produce unstable outputs that deviate from the user's actual intent. To bridge the gap, this paper introduces the CLEAR framework, a systematic study of disambiguation for NL2SQL, including ambiguity detection, clarification, and reformulation, which benefits any NL2SQL parsers. Firstly, CLEAR employs a pipeline using Large Language Models (LLMs) and a series of rules to detect ambiguities, thus obtaining the “candidate mapping” for ambiguity representation. Secondly, an interactive selection module is employed to collect the clarification information from users through multiple-choice questions, thus obtaining the “selection mapping”. Finally, rewriting rules are employed to reformulate the question and schema, thus obtaining a clear input for parsers to generate clear SQLs. Furthermore, we construct CLAMBSQL, a novel benchmark for systematic evaluation for NL2SQL disambiguation, which contains fine-grained ambiguity and clarification annotations. Experiments on various datasets and baselines demonstrate that CLEAR can successfully address seven types of ambiguity. When parsers are integrated with CLEAR, the performance of ambiguous SQLs detection achieves a significant improvement of 30.5 % on AMBROSIA in the AllFound metric and 21.1 % on AmbiQT in the BothInTop-5 metric, the performance of ambiguity clarification achieves a remarkable improvement of 16.2 % on CLAMBSQL in the CEX metric, and the performance of the general prediction achieves an increase of 1.6 % in the EX metric and 7.7 % in the CSR metric on BIRD. The CLEAR code and CLAMBSQL dataset are available at https://github.com/mengzhang18/CLEAR.

The rise of pre-trained large models (PLMs) has sparked interest in vision tasks like object tracking. However, as PLMs scale, fully fine-tuning all parameters becomes impractical, highlighting the need for parameter-efficient fine-tuning (PEFT). While adapter tuning, which adds tunable parameters to Multi-Head Attention (MHA) or Feed-Forward Networks (FFN), is common, critical parameters like Layer Normalization (LN), vital for stability and convergence, are often overlooked. Furthermore, traditional fine-tuning strategies fail to differentiate module importance, limiting performance improvements. To solve these issues, we propose a new PEFT method for unlocking large model potential in object tracking: Adaptive Game-Based Fine-tuning Tracker (AGFT-Tracker). AGFT-Tracker combines adapter tuning with direct LN fine-tuning and adaptively allocates parameter budgets based on tracking attention losses. Important sensitive modules use higher-rank LoRA and frozen LN, while stable modules undergo lower-rank LoRA and LN adjustments. This approach improves effectiveness and efficiency, achieving state-of-the-art results on challenging benchmarks.

Adapting to changes in data distribution is a major challenge in visual object tracking. In Transformer-based tracking, Layer Normalization (LN) is often applied uniformly to both template and search features, limiting feature diversity. Additionally, models tend to converge to trivial solutions, and tracking samples are sensitive to distribution shifts, affecting robustness. To address these issues, we propose the Adaptive Distribution-Aware Transformer Tracker (ADAT), incorporating three key components: the Target-Aware Module (TAM), the Region-Aware Module (RAM), and the Self-Feedback-Aware Module (SFAM). TAM normalizes template and search features separately, preserving flexibility and enhancing target learning. RAM refines target perception by distinguishing between near and far target regions. SFAM filters out noisy samples and fine-tunes normalization parameters through self-feedback. While TAM and RAM regulate feature-level distribution, SFAM adjusts at the sample level. Extensive experiments show that ADAT outperforms existing methods, achieving superior performance on challenging benchmarks.

Abstract In recent years, precision electronic warfare (PREW), an energy‐focussed delivery technology, has been used to solve a series of problems that currently limit traditional electronic warfare. A super‐sparse array of transmitters is used to concentrate the jamming energy as much as possible in the target area and reduce the jamming energy as much as possible in other specific protected areas. However, determining how to design jamming waveforms with jamming characteristics, such as covering the target work bandwidth, while ensuring that the energy transmission meets the requirements, is still an urgent problem. For the first time, this paper considers the task characteristics of PREW applications as constraint conditions. Through a first‐order Taylor expansion, the optimisation problem is approximately transformed into a convex sequential cone programming problem, and jamming waveforms are obtained through an iterative solution method. Experiments showed that the jamming waveforms designed by this algorithm meet the energy distribution requirements of PREW scenarios, and at the same time, the jamming waveforms emitted by each transmitter are superimposed in the jamming area to produce a signal that can cover the target work bandwidth. This method can achieve energy focussing on the space and frequency domains.