YesTrack: Referring Multi-Object Tracking via MLLM-based Yes/No Verification

• URL: http://arxiv.org/abs/2609.02318v1
• Date: Wed, 02 Sep 2026 09:04:43 GMT
• Title: YesTrack: Referring Multi-Object Tracking via MLLM-based Yes/No Verification
• Abstract summary: Referring multi-object tracking (RMOT) aims to track every instance in a video that matches a given language expression.<n>We propose YesTrack, a novel two-stage RMOT method that reformulates referring as a discriminative task.<n>YesTrack significantly outperforms existing state-of-the-art methods while maintaining high efficiency.
• Score: 8.314594040375072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring multi-object tracking (RMOT) aims to track every instance in a video that matches a given language expression. Despite the recent integration of multimodal large language models (MLLMs) to enhance generalization, existing methods predominantly relegate them to the role of caption generators, necessitating external modules for final decision-making. This paradigm not only introduces extra latency but also severely underutilizes the inherent vision-language alignment capabilities of MLLMs. To address these limitations, we propose YesTrack, a novel two-stage RMOT method that reformulates referring as a discriminative task, directly leveraging MLLMs for Yes/No verification without explicit text generation. To further enhance the reliability and efficiency of this MLLM-based verification, we introduce two lightweight temporal consistency constraints: Temporal Confidence Prior (TCP) and Temporal Reference Propagation (TRP). We further validate the generality of this discriminative paradigm by proposing YesTrack-MOT, a straightforward yet highly effective instantiation for generic multi-object tracking (MOT). Experiments on Refer-KITTI and Refer-KITTI-V2 show that YesTrack significantly outperforms existing state-of-the-art methods while maintaining high efficiency, even when implemented with the smallest variant of Qwen3-VL. Code is released at https://github.com/ggbondrighthere24/YesTrack.

Related papers

• LLMTrack: Semantic Multi-Object Tracking with Multi-modal Large Language Models [7.6967194010564235]
  We propose textbfLLMTrack, a novel end-to-end framework for Semantic Multi-Object Tracking (SMOT)<n>We adopt a bionic design philosophy that decouples strong localization from deep understanding, utilizing Grounding DINO as the eyes and the LLaVA-OneVision multimodal large model as the brain.
  arXiv  Detail & Related papers  (2026-01-10T12:18:12Z)
• A Survey on Video Temporal Grounding with Multimodal Large Language Model [107.24431595873808]
  Recent advancement in temporal grounding (VTG) has significantly enhanced fine-grained video understanding.<n>With superior multimodal comprehension and reasoning abilities, VTG approaches based on MLLMs (VTG-MLLMs) are gradually surpassing traditional fine-tuned methods.<n>Despite extensive surveys on general video-language understanding, comprehensive reviews specifically addressing VTG-MLLMs remain scarce.
  arXiv  Detail & Related papers  (2025-08-07T08:52:11Z)
• Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model [63.14883657299359]
  Multi-modal Large Language Models (MLLMs) integrate visual and linguistic reasoning to address complex tasks such as image captioning and visual question answering.<n> tuning MLLMs for downstream tasks encounters two key challenges: Task-Expert, where distribution shifts between pre-training and target datasets constrain target performance, and OpenWorld Stabilization, where catastrophic forgetting erases the model general knowledge.
  arXiv  Detail & Related papers  (2025-03-06T15:29:13Z)
• Accelerating Multimodal Large Language Models via Dynamic Visual-Token Exit and the Empirical Findings [66.04061083611863]
  Excessive use of visual tokens in existing Multimoal Large Language Models (MLLMs) often exhibits obvious redundancy and brings in prohibitively expensive computation.<n>We propose a simple yet effective method to improve the efficiency of MLLMs, termed dynamic visual-token exit (DyVTE)<n>DyVTE uses lightweight hyper-networks to perceive the text token status and decide the removal of all visual tokens after a certain layer.
  arXiv  Detail & Related papers  (2024-11-29T11:24:23Z)
• ChatTracker: Enhancing Visual Tracking Performance via Chatting with Multimodal Large Language Model [29.702895846058265]
  Vision-Language(VL) trackers have proposed to utilize additional natural language descriptions to enhance versatility in various applications.<n>VL trackers are still inferior to State-of-The-Art (SoTA) visual trackers in terms of tracking performance.<n>We propose ChatTracker to leverage the wealth of world knowledge in the Multimodal Large Language Model (MLLM) to generate high-quality language descriptions.
  arXiv  Detail & Related papers  (2024-11-04T02:43:55Z)
• Bootstrapping Referring Multi-Object Tracking [27.77514740607812]
  We introduce a new and general referring understanding task, termed referring multi-object tracking (RMOT)<n>Its core idea is to employ a language expression as a semantic cue to guide the prediction of multi-object tracking.<n>To efficiently generate high-quality annotations, we propose a semi-automatic labeling pipeline that formulates a total of 9,758 language prompts.
  arXiv  Detail & Related papers  (2024-06-07T16:02:10Z)
• NoteLLM-2: Multimodal Large Representation Models for Recommendation [71.87790090964734]
  Large Language Models (LLMs) have demonstrated exceptional proficiency in text understanding and embedding tasks.<n>Their potential in multimodal representation, particularly for item-to-item (I2I) recommendations, remains underexplored.<n>We propose an end-to-end fine-tuning method that customizes the integration of any existing LLMs and vision encoders for efficient multimodal representation.
  arXiv  Detail & Related papers  (2024-05-27T03:24:01Z)
• SEED-Bench-2: Benchmarking Multimodal Large Language Models [67.28089415198338]
  Multimodal large language models (MLLMs) have recently demonstrated exceptional capabilities in generating not only texts but also images given interleaved multimodal inputs. SEED-Bench-2 comprises 24K multiple-choice questions with accurate human annotations, which spans 27 dimensions. We evaluate the performance of 23 prominent open-source MLLMs and summarize valuable observations.
  arXiv  Detail & Related papers  (2023-11-28T05:53:55Z)
• End-to-end Tracking with a Multi-query Transformer [96.13468602635082]
  Multiple-object tracking (MOT) is a challenging task that requires simultaneous reasoning about location, appearance, and identity of the objects in the scene over time. Our aim in this paper is to move beyond tracking-by-detection approaches, to class-agnostic tracking that performs well also for unknown object classes.
  arXiv  Detail & Related papers  (2022-10-26T10:19:37Z)

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.