Mingxiao Ma

Yuting Huang, Leilei Ding, Zhipeng Tang et al.

Large Language Models (LLMs) exhibit substantial promise in enhancing task-planning capabilities within embodied agents due to their advanced reasoning and comprehension. However, the systemic safety of these agents remains an underexplored frontier. In this study, we present Safe-BeAl, an integrated framework for the measurement (SafePlan-Bench) and alignment (Safe-Align) of LLM-based embodied agents' behaviors. SafePlan-Bench establishes a comprehensive benchmark for evaluating task-planning safety, encompassing 2,027 daily tasks and corresponding environments distributed across 8 distinct hazard categories (e.g., Fire Hazard). Our empirical analysis reveals that even in the absence of adversarial inputs or malicious intent, LLM-based agents can exhibit unsafe behaviors. To mitigate these hazards, we propose Safe-Align, a method designed to integrate physical-world safety knowledge into LLM-based embodied agents while maintaining task-specific performance. Experiments across a variety of settings demonstrate that Safe-BeAl provides comprehensive safety validation, improving safety by 8.55 - 15.22%, compared to embodied agents based on GPT-4, while ensuring successful task completion.

Mingxiao Ma, Shunyao Zhu, Guoliang Kang

Incremental object detection (IOD) aims to continuously expand the capability of a model to detect novel categories while preserving its performance on previously learned ones. When adopting a transformer-based detection model to perform IOD, catastrophic knowledge forgetting may inevitably occur, meaning the detection performance on previously learned categories may severely degenerate. Previous typical methods mainly rely on knowledge distillation (KD) to mitigate the catastrophic knowledge forgetting of transformer-based detection models. Specifically, they utilize Hungarian Matching to build a correspondence between the queries of the last-phase and current-phase detection models and align the classifier and regressor outputs between matched queries to avoid knowledge forgetting. However, we observe that in IOD task, Hungarian Matching is not a good choice. With Hungarian Matching, the query of the current-phase model may match different queries of the last-phase model at different iterations during KD. As a result, the knowledge encoded in each query may be reshaped towards new categories, leading to the forgetting of previously encoded knowledge of old categories. Based on our observations, we propose a new distillation approach named Index-Aligned Query Distillation (IAQD) for transformer-based IOD. Beyond using Hungarian Matching, IAQD establishes a correspondence between queries of the previous and current phase models that have the same index. Moreover, we perform index-aligned distillation only on partial queries which are critical for the detection of previous categories. In this way, IAQD largely preserves the previous semantic and spatial encoding capabilities without interfering with the learning of new categories. Extensive experiments on representative benchmarks demonstrate that IAQD effectively mitigates knowledge forgetting, achieving new state-of-the-art performance.

Shiqi Zhang, Sha Zhang, Jiajun Deng et al.

Existing open-vocabulary 3D semantic segmentation methods typically supervise 3D segmentation models by merging text-aligned features (e.g., CLIP) extracted from multi-view images onto 3D points. However, such approaches treat multi-view images merely as intermediaries for transferring open-vocabulary information, overlooking their rich semantic content and cross-view correspondences, which limits model effectiveness. To address this, we propose PGOV3D, a novel framework that introduces a Partial-to-Global curriculum for improving open-vocabulary 3D semantic segmentation. The key innovation lies in a two-stage training strategy. In the first stage, we pre-train the model on partial scenes that provide dense semantic information but relatively simple geometry. These partial point clouds are derived from multi-view RGB-D inputs via pixel-wise depth projection. To enable open-vocabulary learning, we leverage a multi-modal large language model (MLLM) and a 2D segmentation foundation model to generate open-vocabulary labels for each viewpoint, offering rich and aligned supervision. An auxiliary inter-frame consistency module is introduced to enforce feature consistency across varying viewpoints and enhance spatial understanding. In the second stage, we fine-tune the model on complete scene-level point clouds, which are sparser and structurally more complex. We aggregate the partial vocabularies associated with each scene and generate pseudo labels using the pre-trained model, effectively bridging the semantic gap between dense partial observations and large-scale 3D environments. Extensive experiments on ScanNet, ScanNet200, and S3DIS benchmarks demonstrate that PGOV3D achieves competitive performance in open-vocabulary 3D semantic segmentation.