Liang Xie

Knowledge distillation (KD) has become a prevalent technique for compressing large language models (LLMs). Existing KD methods are constrained by the need for identical tokenizers (i.e., vocabularies) between teacher and student models, limiting their versatility in handling LLMs of different architecture families. In this paper, we introduce the Multi-Level Optimal Transport (MultiLevelOT), a novel approach that advances the optimal transport for universal cross-tokenizer knowledge distillation. Our method aligns the logit distributions of the teacher and the student at both token and sequence levels using diverse cost matrices, eliminating the need for dimensional or token-by-token correspondence. At the token level, MultiLevelOT integrates both global and local information by jointly optimizing all tokens within a sequence to enhance robustness. At the sequence level, we efficiently capture complex distribution structures of logits via the Sinkhorn distance, which approximates the Wasserstein distance for divergence measures. Extensive experiments on tasks such as extractive QA, generative QA, and summarization demonstrate that the MultiLevelOT outperforms state-of-the-art cross-tokenizer KD methods under various settings. Our approach is robust to different student and teacher models across model families, architectures, and parameter sizes.

VCOD (Video Camouflage Object Detection) is a crucial security technology that identifies camouflaged objects in videos, bolstering security measures across diverse applications. On one hand, appearance-based VCOD methods face challenges because camouflaged appearances cause objects to blend into their surroundings, and current VCOD methods typically utilize optical flow to represent motion information. However, over-reliance on accurate estimation renders the model overly fragile. On the other hand, there is a shortage of effectively annotated camouflaged video datasets, coupled with the time-consuming and labor-intensive annotation process, severely constraining the development of this field. To address this, we propose a novel weakly-supervised framework for VCOD based on cross-domain querying of preceding and succeeding frames. Specifically, we propose a time-efficient and labor-saving manual annotation approach based on large visual models to rapidly generate pseudo-labels. Furthermore, we design a network based on Spatio-Temporal Memory (STM) that performs cross-modal feature querying with the current frame against preceding and succeeding frames to acquire useful information, thereby enhancing the focus on temporal information. Extensive experiments conducted on two common VCOD datasets have proven the effectiveness of our method, achieving state-of-the-art performance on the challenging camouflaged video data.

3D object detection algorithms for autonomous driving reason about 3D obstacles either from 3D birds-eye view or perspective view or both. Recent works attempt to improve the detection performance via mining and fusing from multiple egocentric views. Although the egocentric perspective view alleviates some weaknesses of the birds-eye view, the sectored grid partition becomes so coarse in the distance that the targets and surrounding context mix together, which makes the features less discriminative. In this paper, we generalize the research on 3D multi-view learning and propose a novel multi-view-based 3D detection method, named X-view, to overcome the drawbacks of the multi-view methods. Specifically, X-view breaks through the traditional limitation about the perspective view whose original point must be consistent with the 3D Cartesian coordinate. X-view is designed as a general paradigm that can be applied on almost any 3D detectors based on LiDAR with only little increment of running time, no matter it is voxel/grid-based or raw-point-based. We conduct experiments on KITTI [1] and NuScenes [2] datasets to demonstrate the robustness and effectiveness of our proposed X-view. The results show that X-view obtains consistent improvements when combined with mainstream state-of-the-art 3D methods.

Traffic forecasting is the task of predicting future traffic based on historical traffic data. It is challenging due to the complex spatial-temporal correlation on road networks. Most existing research works use sequential Graph Neural Networks (GNN) to model traffic inference. However, they only focus on nodes (intersections) or edges (road segments) traffic forecasting alone. As a result, they could hardly provide a complete description of future traffic on road networks. Actually, nodes and edges traffic are interrelated. Both of them are important for traffic safety and efficiency, and neither one is negligible. In this paper, we exploit nodes and edges information together and make traffic forecasting on nodes and edges simultaneously. We propose a novel dual graph framework, called DualGraph, to model the propagation behavior of traffic on road networks. Inside our framework, we develop a DualMap block to simulate the recursive interactions between nodes and edges. The interaction process is realized by a message passing mechanism of nearby information flow. We employ the Simulation of Urban MObility (SUMO) software to generate real-world traffic data to illustrate the effectiveness of our method. We also empirically evaluate our model on public traffic datasets. The results show that even for node or edge traffic forecasting alone, our model still outperforms compared ones, especially for long term (one hour) prediction.