Multi-agent collaboration is the latest trending method to build conversational recommender systems (CRS), especially with the widespread use of Large Language Models (LLMs) recently. Typically, these systems employ several LLM agents, each serving distinct roles to meet user needs. In an industrial setting, it’s essential for a CRS to exhibit low first token latency (i.e., the time taken from a user’s input until the system outputs its first response token.) and high scalability—for instance, minimizing the number of LLM inferences per user request—to enhance user experience and boost platform profit. For example, JD.com’s baseline CRS features two LLM agents and a search API but suffers from high first token latency and requires two LLM inferences per request (LIPR), hindering its performance. To address these issues, we introduce a Hybrid Multi-Agent Collaborative Recommender System (Hybrid-MACRS). It includes a central agent powered by a fine-tuned proprietary LLM and a search agent combining a related search module with a search engine. This hybrid system notably reduces first token latency by about 70% and cuts the LIPR from 2 to 1. We conducted thorough online A/B testing to confirm this approach’s efficiency.

Aiming to develop an annotation-efficient algorithm by selecting the most informative samples to be labeled for human pose estimation, we propose a novel active learning approach that generalized the standard core-set approach by dynamically incorporating the uncertainty and representativeness cues. Based on our designed assignment cost that consists of fast spatial consistency and domain-shift scaled distance, our method proposed an adaptive information measurement as data selection criterion. Extensive experiments conducted on two network architectures and various datasets demonstrate the effectiveness and superiority of our proposed approach in comparison with other state-of-the-art methods.

In this paper, we present a data augmentation framework for pedestrian detection using a pose-guided person image synthesis model. The proposed framework can boost the performance of state-of-the-art pedestrian detectors by generating new and unseen pedestrian training samples with controllable appearances and poses. This is achieved by a new latent-consistent adversarial variational auto-encoder (LAVAE) model, leveraging the advantages of conditional variational auto-encoders and conditional generative adversarial networks to disengage and reconstruct person images conditioned on target poses. An additional latent regression path is introduced to preserve appearance information and to guarantee a spatial alignment during transfer. LAVAE goes beyond existing works in restoring structural information and perceptual details with limited annotations and can further benefit the pedestrian detection task in automated driving scenarios. Extensive pedestrian detection and person image synthesis experiments are performed on the EuroCity Person dataset. We show that data augmentation using LAVAE improves the accuracy of state-of-the-art pedestrian detectors significantly. Furthermore, a competitive performance can be observed when we compare LAVAE with other generative models for person image synthesis.

Gestures from traffic police give the authorized information, especially in some urgent situation. Thus, understanding of traffic police instruction accurately and promptly is particularly crucial for the automated driving system. However, this task is a great challenge not only because of the dynamic and diversity characteristics of the human gesture, but also the high requirement for real-time performance in each frame. We propose an online activity recognition method based on pose estimation and Graph Convolutional Networks (GCN) to recognize the traffic police gesture in frame level. The main contribution in this work is the development of an online framework based on graph convolutional networks for traffic police recognition. Our approach obtained the state-of-the-art results on Traffic Police Gesture Recognition (TPGR) dataset.

Big data leads to a great success of deep learning in computer vision. Unfortunately, big datasets are often not balanced in all dimensions and rare cases are often underrepresented. On-board data collection by a moving vehicle can capture thousands of normal pedestrians and vehicles, but what about special persons like police officers, road workers, and school guards? Not only that those types of classes are hard to get, they are crucial to be recognized and classified as such for the task of automated driving. Future self-driving cars need to interact with their environment and need to also understand and follow the signals and instructions of those special persons. In this paper, we show how to classify special person types using Convolutional Neural Networks. The big data imbalance is handled by data augmentation using Generative Models, showing a clear advantage over classical data augmentation. The classification performance of special persons can be significantly improved using our Generative Model based Data Augmentation.