While the field of natural language to SQL(NL2SQL) has made significant advancements in translating natural language instructions into executable SQL scripts for data querying and processing, achieving full automation within the broader data science pipeline–encompassing data querying, analysis, visualization, and reporting–remains a complex challenge. This study introduces SageCopilot, an advanced, industry-grade system that automates the data science pipeline by integrating Large Language Models (LLMs), Autonomous Agents (AutoAgents), and Language User Interfaces (LUIs). Designed with a two-phase architecture, SageCopilot uses an offline phase to generate high-quality demonstrations supporting In-Context Learning (ICL), which powers the online phase to transform user inputs into executable scripts for database queries, analysis, and visualization tasks. Leveraging specialized components such as NL2SQL, Text2Analyze, and Text2Viz, as well as chain-of-thought prompting for multi-turn interactions, SageCopilot achieves superior end-to-end automation. Rigorous experimentation with real-world datasets demonstrates the system's ability to minimize human intervention while ensuring correctness and user-friendly operation.

Text-to-motion generation, which translates textual descriptions into human motions, has been challenging in accurately capturing detailed user-imagined motions from simple text inputs. This paper introduces StickMotion, an efficient diffusion-based network designed for multi-condition scenarios, which generates desired motions based on traditional text and our proposed stickman conditions for global and local control of these motions, respectively. We address the challenges introduced by the user-friendly stickman from three perspectives: 1) Data generation. We develop an algorithm to generate hand-drawn stickmen automatically across different dataset formats. 2) Multi-condition fusion. We propose a multi-condition module that integrates into the diffusion process and obtains outputs of all possible condition combinations, reducing computational complexity and enhancing StickMotion’s performance compared to conventional approaches with the self-attention module. 3) Dynamic supervision. We empower StickMotion to make minor adjustments to the stickman’s position within the output sequences, generating more natural movements through our proposed dynamic supervision strategy. Through quantitative experiments and user studies, sketching stickmen saves users about 51.5% of their time generating motions consistent with their imagination. Our codes, demos, and relevant data will be released in https:// github.com/InvertedForest/StickMotion.

Knowledge Distillation (KD) aims to improve a light-weight student network supervised by a large teacher network. The core idea of KD is to explore valuable knowledge from the teacher. Previous works often extract information from a single sample, but ignore relation modeling among multiple samples between student and teacher. Therefore, we propose Cross-Layer Graph Knowledge Distillation (CLGKD) that conducts graph-augmented feature and relation distillation assisted by graph neural networks. We further propose a meta-learning mechanism to optimize cross-layer matching weights for promoting GKD among all student and teacher layers. Experimental results on image classification and object detection demonstrate that CLGKD achieves state-of-the-art performance compared to other KD methods. Our code is available at https://github.com/cynmzzz/ICASSP2025-CLGKD

Removing unwanted concepts from large-scale text-to-image (T2I) diffusion models while maintaining their overall generative quality remains an open challenge. This difficulty is especially pronounced in emerging paradigms, such as Stable Diffusion (SD) v3 and Flux, which incorporate flow matching and transformer-based architectures. These advancements limit the transferability of existing concept-erasure techniques that were originally designed for the previous T2I paradigm (e.g., SD v1.4). In this work, we introduce EraseAnything, the first method specifically developed to address concept erasure within the latest flow-based T2I framework. We formulate concept erasure as a bi-level optimization problem, employing LoRA-based parameter tuning and an attention map regularizer to selectively suppress undesirable activations. Furthermore, we propose a self-contrastive learning strategy to ensure that removing unwanted concepts does not inadvertently harm performance on unrelated ones. Experimental results demonstrate that EraseAnything successfully fills the research gap left by earlier methods in this new T2I paradigm, achieving state-of-the-art performance across a wide range of concept erasure tasks.

Multi-human parsing is an image segmentation task necessitating both instance-level and fine-grained category-level information. However, prior research has typically processed these two types of information through distinct branch types and output formats, leading to inefficient and redundant frameworks. This paper introduces UniParser, which integrates instance-level and category-level representations in three key aspects: 1) we propose a unified correlation representation learning approach, allowing our network to learn instance and category features within the cosine space; 2) we unify the form of outputs of each modules as pixel-level results while supervising instance and category features using a homogeneous label accompanied by an auxiliary loss; and 3) we design a joint optimization procedure to fuse instance and category representations. By unifying instance-level and category-level output, UniParser circumvents manually designed post-processing techniques and surpasses state-of-the-art methods, achieving 49.3% AP on MHPv2.0 and 60.4% AP on CIHP. We have released our source code, pretrained models, and demos to facilitate future studies on https://github.com/cjm-sfw/Uniparser.

This work studies the multi-human parsing problem. Existing methods, either following top-down or bottom-up two-stage paradigms, usually involve expensive computational costs. We instead present a high-performance Single-stage Multi-human Parsing (SMP) deep architecture that decouples the multi-human parsing problem into two fine-grained sub-problems,i.e., locating the human body and parts. SMP leverages the point features in the barycenter positions to obtain their segmentation and then generates a series of offsets from the barycenter of the human body to the barycenters of parts, thus performing human body and parts matching without the grouping process. Within the SMP architecture, we propose a Refined Feature Retain module to extract the global feature of instances through generated mask attention and a Mask of Interest Reclassify module as a trainable plug-in module to refine the classification results with the predicted segmentation. Extensive experiments on the MHPv2.0 dataset demonstrate the best effectiveness and efficiency of the proposed method, surpassing the state-of-the-art method by 2.1% in AP50p, 1.0% in APvolpsup>, and 1.2% in PCP50. Moreover, SMP also achieves superior performance in DensePose-COCO, verifying generalization of the model. In particular, the proposed method requires fewer training epochs and a less complex model architecture. Our codes are released in https://github.com/cjm-sfw/SMP.

In this paper, a low complexity moving average nested generalized memory polynomial model (MAN-GMP) is proposed for digital predistortion (DPD) of broadband power amplifiers (PAs). As the signal bandwidth increases drastically, the strong nonlinear distortions, especially those induced by the memory effect, are generated from the highly efficient PAs. To compensate for the strong memory effect, a moving average nested envelope memory polynomial (MAN-EMP) model is derived from an accuracy-enhanced GMP model, which offers reduced complexity while suffering from degraded modeling accuracy. The MAN-GMP model is further proposed to improve the modeling accuracy by connecting several memory branches of the MAN-EMP model in parallel. An iterative algorithm is designed to extract the model coefficients efficiently through only one or two iterations. Experimental measurements are carried out on two sub-7 GHz broadband GaN Doherty PAs with up to 200 MHz bandwidth OFDM signals to benchmark the proposed MAN-GMP model against the GMP, the parallel-LUT-MP-EMP (PLUME), the augmented complexity-reduced GMP (ACR-GMP), the generalized twin-nonlinear two-box (GTNTB), and the enhanced Wiener models. The experimental results show that the MAN-GMP model can effectively compensate for the nonlinear distortion of broadband PAs with significant complexity reduction.

In this paper, a novel multi-stream spatial digital predistortion (DPD) technique is proposed to model and linearize the fully-connected (FC) hybrid beamforming (HBF) transmitters. The proposed scheme solves the DPD implementation issue in the FC HBF array by estimating and linearizing the beam signals instead of the individual PAs. In FC HBF systems, significant intermodulation (IMD) between different transmit signals will be generated due to the analog beamforming and combining network upstream of the power amplifiers (PAs). The IMD beams will end up being radiated in different directions and some of them might fall in the linear beam directions. Therefore, multi-input DPD blocks using a practical multi-variable model are constructed for each RF chain to eliminate the complicated inner- and cross-channel IMDs of the beam signals. Simulations on a 4-stream 64-element FC HBF array and experimental tests on a 2-stream 4-element system are carried out to benchmark the proposed DPD technique against the conventional techniques. Better than 13 dB adjacent channel power ratio (ACPR) improvement and 12 dB normalized mean square error (NMSE) improvement have been achieved by the proposed DPD technique.