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Object Detection with Multimodal Large Vision-Language Models: An In-depth Review

Ranjan Sapkota, Manoj Karkee

arXiv:2508.19294v2cs.CVcs.AIcs.CL

TL;DR

Traditional detectors identify objects but have limited semantic reasoning for contextual queries, despite object detection’s role in practical applications. This review examines LVLM architectures and compares them with conventional systems; it finds complementary strengths, with LVLMs excelling in contextual understanding and traditional detectors remaining important for speed and precision.

  • Problem

    Traditional detectors can identify objects but have limited semantic reasoning for contextual relationships and queries, although detection supports practical tasks such as safe navigation and diagnostics.

    Source: §1.1 · p. 1, §2.1 · p. 7

  • Method

    The review examines multimodal LVLM architectural innovations and methodologies and compares them with conventional object-detection frameworks.

    Source: §6 · p. 26

  • Results

    LVLMs excel in contextual understanding and multimodal interactions, while traditional frameworks remain important for high-speed, precise, real-time processing on edge devices.

    Source: §6 · p. 26

  • Takeaways & Limitations

    The review concludes that LVLMs and traditional deep-learning models will likely coexist, with integration proposed to expand object detection capabilities across scenarios.

    Source: §6 · p. 27

    Few LVLM-based methods offer mitigation mechanisms for hallucination, semantic misalignment, and spatial mislocalization.

    Source: §5.1 · p. 23

Abstract

from arXiv · show

The fusion of language and vision in large vision-language models (LVLMs) has revolutionized deep learning-based object detection by enhancing adaptability, contextual reasoning, and generalization beyond traditional architectures. This in-depth review presents a structured exploration of the state-of-the-art in LVLMs, systematically organized through a three-step research review process. First, we discuss the functioning of vision language models (VLMs) for object detection, describing how these models harness natural language processing (NLP) and computer vision (CV) techniques to revolutionize object detection and localization. We then explain the architectural innovations, training paradigms, and output flexibility of recent LVLMs for object detection, highlighting how they achieve advanced contextual understanding for object detection. The review thoroughly examines the approaches used in integration of visual and textual information, demonstrating the progress made in object detection using VLMs that facilitate more sophisticated object detection and localization strategies. This review presents comprehensive visualizations demonstrating LVLMs' effectiveness in diverse scenarios including localization and segmentation, and then compares their real-time performance, adaptability, and complexity to traditional deep learning systems. Based on the review, its is expected that LVLMs will soon meet or surpass the performance of conventional methods in object detection. The review also identifies a few major limitations of the current LVLM modes, proposes solutions to address those challenges, and presents a clear roadmap for the future advancement in this field. We conclude, based on this study, that the recent advancement in LVLMs have made and will continue to make a transformative impact on object detection and robotic applications in the future.

1. Introduction

Object detection supports machine interaction across practical settings, but conventional deep-learning detectors face limitations in data demands, zero-shot detection, contextual understanding, and adaptability. This review synthesizes multimodal LVLM advances and compares their capabilities and trade-offs with conventional methods.

1.1. Background

  • Conventional detectors can require extensive labeled data and struggle with unseen classes, complex scenes, contextual interpretation, and detailed object queries; LVLMs integrate vision and language for context-aware recognition.Object detection also supports applications including autonomous navigation and healthcare diagnostics.Source: §1.1 · pp. 1, 3
  • The review examines multimodal LVLM advances from 2022 to 2025, compares them with conventional detectors, and identifies spatial localization as a challenge motivating hybrid frameworks.The proposed direction combines LVLM contextual intelligence with conventional detectors’ spatial precision.Source: §1.1 · p. 3

1.2.1. Review motivation and structure

  • The review analyzes architectures, training foundations, performance, and practical applicability across diverse detection settings, guided by three research questions.The reviewed corpus grew from six qualifying studies in 2022 to fifty in early 2025.Source: §1.2.1 · p. 3

1.2.2. Literature discovery and filtering strategy

  • The literature search used broad and task-specific terms, then included technically transparent, rigorous, relevant vision-language detection studies and excluded works without sufficient depth or reproducible methods.The search covered academic databases, preprint servers, and community-driven hubs.Source: §1.2.2 · pp. 3–4

1.2.3. Review design and research questions

  • The review asks what capabilities LVLMs add, how their architectures and training work, and how they compare with traditional detectors in performance, generalization, efficiency, and deployment readiness.Its analysis also considers datasets, object scales, environmental variability, inference conditions, and robotic applications.Source: §1.2.3 · p. 4

2. Foundational functioning and evolution of multimodal LVLMs in object detection:

This section traces the evolution of multimodal LVLMs toward integrated visual-language processing and surveys their detection strategies, contextual reasoning, and applications. It covers detection, localization, and segmentation across challenging settings, including open-set objects, 3D scenes, and remote sensing.

2. Foundational functioning and evolution of multimodal LVLMs in object detection:

  • LVLM training has shifted from scratch-trained models requiring massive datasets toward pretrained LLM backbones with visual modules; some systems also generate code to turn prompts into tool-mediated actions.Attention maps can be converted into pixel coordinates for bounding boxes, while generated snippets may support visualization or robotic control.Source: §2 · pp. 4–5
  • Zero-shot prediction uses pretrained LVLMs without task-specific tuning, visual fine-tuning adapts the vision encoder to labeled domains, and text prompting changes descriptive input while keeping both encoders frozen.These approaches trade off labeled-data needs, domain adaptation, and lightweight deployment.Source: §2 · p. 6, Figure 5 · p. 6

2.1. Advancements in multimodal LVLMs for object detection

  • LVLM architectures combine visual and textual streams through transformers, attention, and shared embeddings, allowing language to guide attention to image regions, including in zero-shot detection.Dual streams can adjust the relative weights of visual and textual features, while transformer designs process images as patches and descriptions as tokens.Source: §2.1 · p. 7
  • Compared with fixed-class detectors, LVLMs can use natural-language queries for contextual relationships and produce text-aligned descriptions or spatial outputs; language integration also aids recognition and segmentation in crowded scenes.The review contrasts contextual questions handled by Qwen-VL with the fixed-class limits of YOLO and SSD, and notes Ferret’s free-form descriptions and bounding-box coordinates.Source: §2.1 · p. 7

2.2. Visual analysis of object detection with multimodal LVLMs

  • SpatialLM processes point clouds into semantically rich 3D scenes with oriented boxes, while remote-sensing open-set detection uses threshold proposals and LVLM semantic labeling to find unknown categories without manual labels.The remote-sensing approach is described as generalizing beyond YOLO and Mask R-CNN.Source: §2.2 · pp. 8, 10, Figure 7 · p. 9, Figure 8 · p. 10
  • TaskCLIP [129] surpasses DETR-based TOIST in accuracy and efficiency, with a reported 6.2% accuracy increase from separating general detection from task-specific reasoning.Its second stage uses a transformer-based aligner to match object images with visual attributes described by adjective phrases.Source: §2.2 · pp. 8–9, Figure 7 · p. 9
  • ContextDET uses generated textual context and conditional object queries to detect context-dependent objects, outperforming traditional and open-vocabulary models in detailed contextual scenarios.Its tasks include completing masked object names, predicting captions with boxes, and answering questions about object locations and names.Source: §2.2 · p. 9, Figure 7 · p. 9
  • Clip2Safety combines scene recognition with fine-grained verification for workplace PPE detection, while LLMDet [132] co-trains caption generation and grounding to improve open-vocabulary detection.LLMDet uses GroundingCap-1M, which provides grounding labels and detailed image captions.Source: §2.2 · pp. 9–10, Figure 7 · p. 9
  • GeoChat supports zero-shot remote-sensing detection, relevant conversations, and scene classification, while SkyEyeGPT outperforms GPT4 and LLaMa on referring-expression generation and scene classification.SkyEyeGPT aligns remote-sensing visual features with language-domain instructions to support instruction following and dialogue.Source: §2.2 · pp. 10–11
  • zPROD outperforms standard methods on RoadAnomaly and RoadObstacle and achieves comparable results on Fishyscapes subsets.It combines detection and segmentation to identify previously unrecognized objects in complex driving environments, evaluated on SMIYC and Fishyscapes.Source: §2.2 · p. 11
  • zPROD uses frozen LVLM inference to predict instance classifications and segment OOD objects in automated-driving scenes.The approach leverages visual grounding and contextual interpretation to handle known and novel object types.Source: §2.2 · p. 11
  • APE more accurately classifies and grounds in-domain objects than MSP-based detectors that can misclassify them as out-of-distribution.The examples involve varied OOD-object locations in RA and RoadObstacle21, including FS Static images.Source: §2.2 · p. 11
  • LLM-Seg outperforms GRES, LISA, and LLaVA+Grounding SAM in visual comparisons, particularly for multiple-instance segmentation.Its user-intention interpretation supports target-object segmentation, and LLM-Seg40K is presented as a reasoning-segmentation benchmark.Source: Figure 9 · p. 10, §2.2 · p. 11

3. Architectural innovations and application of multimodal LVLMs for object detection

This section addresses RQ2 by surveying how multimodal LVLMs are designed and implemented for object detection. It reviews unified architectures, domain- and task-oriented systems, and applications across fields.

3.1. Unified architectures and enhancement mechanisms in LVLM-based object detection

  • Unified detection designs range from DetGPT’s instruction-to-detector pipeline and CoTDet/TaskCLIP’s task-aligned embeddings to GenerateU’s open-ended labels and LLM–diffusion negative-sample generation.TaskCLIP uses a transformer-based aligner; Zhao et al. use CLIP filtering to retain semantically valid negatives.Source: §3.1 · p. 11, Figure 10 · p. 12
  • Other unified frameworks transfer LVLM hidden states through cross-attention adapters, serialize aerial detections as text, or use language-generated context to guide open-vocabulary localization.LMMRotate’s evaluation excludes confidence scores and introduces mAPnc for comparison with conventional detectors.Source: §3.1 · pp. 11–13, §3.2 · p. 13

3.2. Domain-specific and task-oriented multimodal detection innovations

  • VED-SR uses LLM-guided symbolic regression for training-free, interpretable anomaly detection and achieves or surpasses state-of-the-art performance with less than 1% of supervised methods’ annotated data.Its output is an auditable symbolic expression representing high-level semantic patterns.Source: §3.2 · p. 13
  • Segmentation approaches include InfoScore-ranked attention maps for zero-shot or one-shot use and LLM-Seg’s <SEG>-guided mask selection with frozen core models.LLM-Seg also introduces the LLM-Seg40K dataset for reasoning-aware segmentation.Source: §3.2 · pp. 13, 15
  • VLTNet combines scene mapping and Tree-of-Thought planning for zero-shot navigation, while FireAgent integrates satellite, social-media, and environmental data for fire-impact assessment.VLTNet evaluations on PASTURE and RoboTHOR confirm its superior performance in complex language-grounding and real-time scene-understanding scenarios.Source: §3.2 · pp. 13–15
  • OpenVidVRD combines region captions, role-aware spatiotemporal refinement, and hybrid prompting, achieving state-of-the-art results on VidVRD and VidOR.Its classification compares refined visual features with text embeddings, while training uses object and relation contrastive losses plus an interaction loss.Source: §3.2 · p. 15
  • VLM-R1’s RL-trained models outperform supervised fine-tuned counterparts; on OVDEval, VLM-R1 achieves 31.01 mAP, surpassing SFT models and OmDet in semantic-reasoning categories.Its task-specific rewards use IoU for referring-expression comprehension and mAP with a redundancy penalty for open-vocabulary detection.Source: §3.2 · pp. 15–16

3.3. Major applications of multimodal LVLMs for object detection

  • Reported applications span autonomous driving, surveillance, robotics, remote sensing, and low-resource detection, using multimodal systems for hazards, scene interaction, and unknown objects.The review describes examples including VOLTRON for driving hazards, DetGPT for robotic interaction, and latent semantic transfer for low-resource settings.Source: §3.3 · p. 16

4. Comparison between multimodal LVLMs and traditional deep learning: Capabilities and limitations

This section examines how multimodal LVLMs extend object detection through open-vocabulary recognition, contextual reasoning, and task adaptation, while considering computational costs, latency, and implementation complexity. It also covers text-described object detection, emerging approaches to localization, task unification, and annotation-free alignment, along with evaluation criteria for spatial precision, generalization, semantic alignment, robustness, and deployment practicality.

4.1. Performance metrics and real-time capabilities of LVLMs for object

  • LVLMs offer more adaptable, context-aware open-vocabulary detection than fixed-vocabulary traditional models, while traditional detectors are substantially faster and more resource-efficient.LVLMs also require more diverse training data and computing resources, though language integration supports more complex reasoning.Source: §4.1 · p. 16
  • Griffon models use prompt-conditioned token-level alignment for localization, while Griffon-G unifies detection, segmentation, and visual grounding [165].Vision-R1 [165] uses reward-based curriculum learning to acquire vision-language mappings without manual annotations and outperforms fine-tuned counterparts on VQAv2, RefCOCOg, and LVIS detection while retaining strong zero-shot compositional generalization [166] [167].Source: §4.1 · p. 16

4.2. Adaptability and open-vocabulary capabilities of LVLMs for object

  • LVLMs detect objects from textual descriptions beyond fixed training vocabularies; TaskCLIP [129] aligns detection with complex task requirements and outperforms traditional methods on COCO-Tasks.SkyEyeGPT, GenerateU, and DetGPT illustrate domain adaptation, open-ended detection, and reasoning-based detection, respectively.Source: §4.2 · pp. 19–20
  • Despite adaptive performance, LVLMs’ resource demands and slower inference than traditional models such as YOLO-NAS challenge their use in real-time applications.The passage suggests future iterations may reduce these limitations.Source: §4.2 · p. 20

4.3. System complexity and implementation challenges of LVLMs for object

  • LVLM deployment is complicated by multimodal alignment, large-scale data and GPU-hour requirements, prompt engineering, and fine-tuning sensitivity, alongside high compute demands that limit edge deployment [111] [132].Prompt design must avoid context misinterpretation or hallucinations, and small changes to parameters or training data can produce substantially different outcomes.Source: §4.3 · p. 20

4.4. Architectural trade-offs and computational considerations

  • Multimodal encoders, cross-attention, and large LLM backbones increase LVLM memory use, latency, and integration complexity, limiting real-time edge deployment without quantization or pruning.Autoregressive decoding and dense visual tokenization add costs in large models such as Flamingo-80B and GPT-4V [170], while even BLIP-2 (13B) and DetGPT (7B) incur multi-stage bottlenecks.Source: §4.4 · p. 20
  • LVLM gains may be marginal against efficient detectors: GLIP scores 49.8 mAP on COCO Zero-Shot and OV-DETR 38.2 mAP on COCO novel classes, while requiring 4× −10× more resources.Grounding DINO typically runs at 30–45 FPS on high-end GPUs, compared with 110–161 FPS for optimized YOLO derivatives on similar hardware, a concern for real-time applications.Source: §4.4 · p. 20
  • Hybrid designs such as OV-DINO [172], ContextDET, and DetGPT separate LLM-based reasoning from localization, but staged integration can require fine-tuned encoders or handcrafted alignment.The review calls for systematic FLOPs, latency, memory, and annotation-cost evaluation, alongside cost-effective scaling, distillation, and architecture-aware benchmarking.Source: §4.4 · pp. 20–21

4.5. Comparative insights into Open-Vocabulary Object Detectors (OVOD)

  • OVODs detect arbitrary text-prompted objects without retraining; ViLD [178] distills CLIP-style embeddings into region-proposal pipelines, while YOLO-World and YOLOE [118] add textual conditioning to efficient YOLO architectures.OWL-ViT and GLIP combine joint vision-language pretraining with end-to-end detection fine-tuning.Source: §4.5 · p. 21
  • 48.3 AP@50:95 on RoadObstacle21 is reported for Grounding DINO, significantly outpacing standard YOLO on out-of-distribution objects.The passage also says OVODs consistently outperform traditional methods in zero-shot and domain-transfer settings.Source: §4.5 · p. 21
  • Dynamic prompt updates in YOLO-World and lightweight modular customization in LP-OVOD and CCKT-Det support adaptability, while OV-DETR and OV-DINO [172] use cross-attention or selective fusion for modality alignment.GLIP and YOLOE are also described as achieving high open-vocabulary recall while maintaining real-time inference speeds.Source: §4.5 · p. 21
  • OVODs remain inconsistent in adversarial robustness and limited in abstract reasoning and negation understanding, and prompt-conditioned scenes can produce boxes for implausible object relationships.The passage notes perturbation tolerance in OWL-ViT and promising corrupted-input generalization in CCKT-Det and Open Corpus OVD [185].Source: §4.5 · p. 21

4.6. Evaluation metrics in LVLM-based object detection

  • Evaluation combines mAP@0.5:0.95 with zero-shot mAP, Hallucination Error Rate, OVA, CLIPScore, Compositional Error Rate, Corruption mAP Drop, FPS, human alignment, and task-specific MAE, RMSE, and soft-accuracy.Traditional precision–recall metrics alone do not capture zero-shot performance or hallucination from vision-language misalignment; the measures address generalization, prompt understanding, semantic alignment, attribute binding, robustness, and deployment.Source: §4.6 · p. 21

5. Discussion

The discussion outlines practical and architectural barriers to LVLM-based detection, alongside proposed strategies for improving efficiency, robustness, and localized reasoning. It considers benchmark trade-offs and the potential role of LVLMs in robotics.

5.1. Discussion on current challenges and potential solutions

  • LVLM deployment is constrained by compute, data and inference demands, modality alignment, region-level precision, background handling, noisy pseudo-labels, and base–novel conflicts.The review also identifies prompt dependency, hallucinated outputs, and loss of context when analyzing localized regions.Source: §5.1 · pp. 21–23
  • Proposed responses combine region-aware pretraining and adversarial prompt tuning with spatiotemporal encoding, decoupled prediction heads, compression, and multimodal fusion.The roadmap also includes reinforcement learning and integrated solutions intended to support lightweight, robust, interpretable detection.Source: §5.1 · p. 21, Figure 13 · p. 23
  • Few methods mitigate hallucination, semantic misalignment, or mislocalization, and proposed approaches lack systematic evaluation under real-world perturbations.The review notes that some methods rely on curated benchmarks, while hybrid approaches may depend on handcrafted thresholds or extra modules.Source: §5.1 · p. 23

5.2. Strategic directions and research outlook for LVLM-based object detec-

  • Future directions include compression for edge deployment, localized multimodal reasoning, reinforcement learning, robustness methods, and scale-aware training on semantically rich data.Suggested techniques include quantization, pruning, regional tokenization, adversarial prompt training, and decoupled heads.Source: §5.2 · pp. 23, 25

5.3. Quantitative benchmark comparison of LVLMs

  • The benchmark comparison considers five LVLMs across mAP@0.5, mAP@0.5:0.95, FPS, and generalization, framing trade-offs between performance and deployment feasibility.The passage provides no individual model scores.Source: §5.3 · p. 26

5.4. Impact of LVLM-based object detection on future of robotics

  • LVLMs are presented as a potential fit for robotics tasks that combine visual reasoning with language instructions, including home assistance, warehouse automation, and human–robot interaction.The review describes GR00T N1 as combining vision-language processing with a Diffusion Transformer for motor actions, while noting future needs for greater speed and efficiency.Source: §5.4 · p. 26

6. Conclusion

The conclusion presents LVLMs as adding contextual, language-aware capabilities to object detection, while conventional models remain important for fast, precise real-time localization. It identifies challenges and future directions, and points to robotics as an application area.

6. Conclusion

  • The review finds LVLMs strong in contextual understanding and multimodal interaction, while conventional models remain important for high-speed, precise real-time localization on edge devices.It characterizes the two approaches as complementary, including for scene understanding and preliminary labeling.Source: §6 · p. 26
  • Fig. 14 illustrates a robotic system that processes image observations and language instructions alongside robot-state and action encodings to generate motor actions.The figure also outlines a four-stage progression from multimodal inputs through feedback and learning.Source: Figure 14 · p. 27
  • The review identifies computational cost, prompt dependency, noisy pseudo-labels, and the pretraining-to-detection domain gap, and proposes region-aware pretraining, compression, reinforcement learning, and improved fusion.It anticipates continued coexistence between LVLMs and traditional deep learning models.Source: §6 · p. 27

CRediT authorship contribution statement

The contribution statement assigns writing, visualization, methodology, investigation, and formal analysis roles to Ranjan Sapkota and Manoj Karkee.

CRediT authorship contribution statement

  • Ranjan Sapkota and Manoj Karkee are both credited with writing, visualization, methodology, investigation, and formal analysis.Karkee is additionally credited with resources, project administration, and funding acquisition.Source: CRediT authorship contribution statement · p. 27

Statement on AI Writing Assistance

The authors report using ChatGPT and Perplexity for grammatical accuracy and sentence refinement, and ChatGPT-4o to generate visualizations.

Statement on AI Writing Assistance

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