The System Description of CPS Team for Track on Driving with Language of CVPR 2024 Autonomous Grand Challenge

• URL: http://arxiv.org/abs/2509.11071v1
• Date: Sun, 14 Sep 2025 03:37:17 GMT
• Title: The System Description of CPS Team for Track on Driving with Language of CVPR 2024 Autonomous Grand Challenge
• Abstract summary: This report outlines our approach using vision language model systems for the Driving with Language track of the CVPR 2024 Autonomous Grand Challenge.<n>We have exclusively utilized the DriveLM-nuScenes dataset for training our models.<n>We have integrated depth information from open-source depth estimation models to enrich the training and inference processes.
• Score: 10.577222594790333
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This report outlines our approach using vision language model systems for the Driving with Language track of the CVPR 2024 Autonomous Grand Challenge. We have exclusively utilized the DriveLM-nuScenes dataset for training our models. Our systems are built on the LLaVA models, which we enhanced through fine-tuning with the LoRA and DoRA methods. Additionally, we have integrated depth information from open-source depth estimation models to enrich the training and inference processes. For inference, particularly with multiple-choice and yes/no questions, we adopted a Chain-of-Thought reasoning approach to improve the accuracy of the results. This comprehensive methodology enabled us to achieve a top score of 0.7799 on the validation set leaderboard, ranking 1st on the leaderboard.

Related papers

• Rethinking Visual Intelligence: Insights from Video Pretraining [75.32388528274224]
  Large language models (LLMs) have demonstrated that large-scale pretraining enables systems to adapt rapidly to new problems.<n>We investigate Video Diffusion Models (VDMs) as a promising direction for bridging the gap.
  arXiv  Detail & Related papers  (2025-10-28T14:12:11Z)
• Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models [4.917936997225074]
  Seewo's systems for both tracks of the Multilingual Conversational Speech Language Model Challenge (MLC-SLM)<n>We introduce a multi-stage training pipeline that explicitly enhances reasoning and self-correction in speech language models for ASR.
  arXiv  Detail & Related papers  (2025-06-16T09:42:05Z)
• AlphaMaze: Enhancing Large Language Models' Spatial Intelligence via GRPO [0.0]
  Large Language Models (LLMs) have demonstrated impressive capabilities in language processing, yet they often struggle with tasks requiring visual spatial reasoning.<n>We introduce a novel two-stage training framework designed to equip standard LLMs with visual reasoning abilities for maze navigation.
  arXiv  Detail & Related papers  (2025-02-20T16:05:18Z)
• Driving with InternVL: Oustanding Champion in the Track on Driving with Language of the Autonomous Grand Challenge at CVPR 2024 [23.193095382776725]
  This report describes the methods we employed for the Driving with Language track of the CVPR 2024 Autonomous Grand Challenge.<n>We utilized a powerful open-source multimodal model, InternVL-1.5, and conducted a full-fledged fine-tuning on the competition dataset, DriveLM-nuScenes.<n>Our single model achieved a score of 0.6002 on the final leadboard.
  arXiv  Detail & Related papers  (2024-12-10T07:13:39Z)
• Enhancing End-to-End Autonomous Driving with Latent World Model [78.22157677787239]
  We propose a novel self-supervised learning approach using the LAtent World model (LAW) for end-to-end driving.<n> LAW predicts future scene features based on current features and ego trajectories.<n>This self-supervised task can be seamlessly integrated into perception-free and perception-based frameworks.
  arXiv  Detail & Related papers  (2024-06-12T17:59:21Z)
• A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge [33.89616011003973]
  We describe our proposed spoken semantic parsing system for the quality track (Track 1) in Spoken Language Understanding Grand Challenge. Strong automatic speech recognition (ASR) models like Whisper and pretrained Language models (LM) like BART are utilized inside our SLU framework to boost performance. We also investigate the output level combination of various models to get an exact match accuracy of 80.8, which won the 1st place at the challenge.
  arXiv  Detail & Related papers  (2023-05-02T17:25:19Z)
• Guiding Large Language Models via Directional Stimulus Prompting [114.84930073977672]
  We introduce Directional Stimulus Prompting, a novel framework for guiding black-box large language models (LLMs) toward specific desired outputs. Instead of directly adjusting LLMs, our method employs a small tunable policy model to generate an auxiliary directional stimulus prompt for each input instance.
  arXiv  Detail & Related papers  (2023-02-22T17:44:15Z)
• The Wisdom of Hindsight Makes Language Models Better Instruction Followers [84.9120606803906]
  Reinforcement learning has seen wide success in finetuning large language models to better align with instructions via human feedback. In this paper, we consider an alternative approach: converting feedback to instruction by relabeling the original one and training the model for better alignment in a supervised manner. We propose Hindsight Instruction Relabeling (HIR), a novel algorithm for aligning language models with instructions.
  arXiv  Detail & Related papers  (2023-02-10T12:16:38Z)
• longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks [45.451337461348906]
  We describe the approach of the team "longhorns" on Task 1 of the The First Workshop on Dynamic Adversarial Data Collection (DADC) Our team finished first, with a model error rate of 62%.
  arXiv  Detail & Related papers  (2022-06-29T15:52:20Z)
• Unifying Language Learning Paradigms [96.35981503087567]
  We present a unified framework for pre-training models that are universally effective across datasets and setups. We show how different pre-training objectives can be cast as one another and how interpolating between different objectives can be effective. Our model also achieve strong results at in-context learning, outperforming 175B GPT-3 on zero-shot SuperGLUE and tripling the performance of T5-XXL on one-shot summarization.
  arXiv  Detail & Related papers  (2022-05-10T19:32:20Z)
• The USYD-JD Speech Translation System for IWSLT 2021 [85.64797317290349]
  This paper describes the University of Sydney& JD's joint submission of the IWSLT 2021 low resource speech translation task. We trained our models with the officially provided ASR and MT datasets. To achieve better translation performance, we explored the most recent effective strategies, including back translation, knowledge distillation, multi-feature reranking and transductive finetuning.
  arXiv  Detail & Related papers  (2021-07-24T09:53:34Z)
• Two-Stream Consensus Network: Submission to HACS Challenge 2021 Weakly-Supervised Learning Track [78.64815984927425]
  The goal of weakly-supervised temporal action localization is to temporally locate and classify action of interest in untrimmed videos. We adopt the two-stream consensus network (TSCN) as the main framework in this challenge. Our solution ranked 2rd in this challenge, and we hope our method can serve as a baseline for future academic research.
  arXiv  Detail & Related papers  (2021-06-21T03:36:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.