vlms
Vision-language models present a new way to process images or videos and text together. First, they combine computer vision encoders with language encoders. Next, they fuse those representations in a shared latent space so a single system can reason about visual signals and human language. In the context of forensic video anomaly detection this fusion matters. It lets operators ask natural language questions about video, and then quickly find relevant clips. For example, an operator can query a control room with a phrase like “person loitering near the gate after hours” and get human-readable results. This saves hours of manual review and cuts analysis time significantly. A field study reported a reduction in analysis time of up to 40% when multimodal tools were introduced The Science of Forensic Video Analysis: An Investigative Tool.
At the model level, one common architecture pairs a vision encoder that processes RGB frames and a transformer-based language model that handles captions or transcripts. Then a projection head aligns visual embeddings and text embeddings. The aligned vectors let a downstream classifier for anomaly or a generator create descriptions. These vision-language models appear twice in this article because they are central to modern pipelines. They support both zero-shot queries and fine-tuned classification. For practical deployments, VLMS run on-prem to preserve privacy, and they power features like VP Agent Search that turn surveillance video into searchable text.
AI plays several roles here. AI detects objects, flags anomalous behaviors, and prioritizes clips for review. AI also summarizes events and reduces false alarms. In addition, AI agents can reason across video, VMS logs, and access-control records. As a result, operators receive an explained alarm that supports faster decision-making. The pipeline benefits from pre-trained models, and then site-specific tuning with limited training data. Finally, this setup supports weakly supervised video anomaly workflows when exact timestamps are unavailable.
related work
Research benchmarks show large variation between lab performance and real-world results. For instance, the Deepfake-Eval-2024 benchmark highlights a dramatic performance drop of over 30% when models trained on controlled datasets are applied to in-the-wild footage Deepfake-Eval-2024. That study tested multimodal detectors and found that many systems struggle with noisy metadata and varied compression levels. At the same time, classic single-modality pipelines—those that use only computer vision or only audio—still perform well on curated datasets like UCF-CRIME. Yet they often fail to generalize.
Multimodal approaches offer advantages. They fuse visual signals, transcripts, and metadata, and they use semantic cues to reduce false alarms. For example, cross-referencing an access control log with a video clip helps confirm or reject an alarm. Also, multimodal models can use language to disambiguate visually similar events. This improves anomaly classification and video anomaly recognition. Still, gaps remain. Benchmark datasets rarely capture the full range of real-world scenarios, and annotated ground-truth for anomalous events is scarce. Researchers call for larger benchmark datasets and richer annotations to boost robustness and temporal consistency.
Related work also examines algorithmic design. Papers by Zhong, Tian, Luo, Agarwal, Joulin, and Misra explore aggregation and temporal models for vad and action recognition. In practice, pre-trained visual backbones are fine-tuned on domain data to reduce false positives. Yet a critical challenge persists: bridging the gap between lab metrics and operational reliability in live control rooms. We must push towards benchmark datasets that reflect hours of manual review, messy compression, low light, and occlusions to improve the model’s real-world robustness Deepfake-Eval-2024 (PDF).
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ai
AI now underpins most modern forensic and security workflows. First, it processes the amount of video that would overwhelm humans. Second, it triages events so teams focus on high-value incidents. Third, it provides human-readable explanations to support decisions. At visionplatform.ai we build on these capabilities. Our VP Agent Reasoning correlates video analytics, VLM descriptions, and VMS logs so operators get context, not just alerts. That reduces cognitive load and speeds action.
AI functions fall into detection, summarization, and decision support. Detection components include anomaly detectors and action recognition models. Summarization components use language models to generate concise reports from video. Decision support combines those outputs and applies rules or agent policies. In many setups, multiple AI models run in parallel. They provide redundancy and help validate hypotheses across modalities. This multiple models approach raises questions about aggregation and how to resolve conflicting outputs. For that reason, traceable decision-making and auditable logs are essential.
Integration matters. AI teams often couple video outputs with other forensic tools such as DNA analysis or crime-scene reconstruction. This lets investigators cross-check timelines and evidence. In operations, AI agents can pre-fill incident reports and trigger workflows. For example, a VP Agent Action can suggest a next step or close a false alarm with justification. This reduces time per alarm and improves consistency. AI also faces limits. Model training and supervised learning require label effort. Robustness to adversarial perturbations and generative ai threats remains an open area Synthetically Generated Media. Still, AI promises scalable support for control rooms that must handle thousands of hours of video every week.
language models
Language models in VLM stacks are usually transformer-based. They include variants of encoder-only, decoder-only, and encoder-decoder models. These language models enable natural language queries, transcription verification, and context fusion. For instance, a transcript produced by speech-to-text can be embedded and compared to text descriptions from a vision encoder. That comparison helps to detect inconsistencies and to flag mismatches between witness statements and video. The system can then surface clips for human review.
Language processing improves contextual understanding. It provides semantic labels that complement low-level computer vision signals. As a result, tasks like event detection and anomaly classification become more accurate. Language models also support language generation so systems can produce audit-ready reports or verbatim transcripts. When paired with pre-trained visual encoders, they enable zero-shot detection of novel anomalous events that were not seen in training. The cross-modal alignment uses shared embeddings to embed visual features and text, which supports flexible search and retrieval.
Deployers should pay attention to contextual cues like camera location, time of day, and access-control data. Together, these elements form a richer video context that helps the model decide whether an action is normal or anomalous. In practice, operators use the VP Agent Search to find incidents with simple human language queries. That feature ties into our on-prem policy for privacy and compliance. Finally, language models can assist in metadata cross-referencing, verifying timestamps, and improving the classifier for anomaly by providing semantic constraints.
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prompt
Prompt engineering matters for VLMs. A clear prompt steers a VLMS to the correct output, and a poor prompt produces noisy or misleading results. Use concise, specific language. Include camera context, time constraints, and expected objects. For example, a prompt that says “List suspicious carrying of unattended objects near Gate B between 22:00 and 23:00” yields focused results. Also, add examples when possible to guide few-shot behavior.
Here are sample prompts for common tasks. For anomaly detection, use: “Detect anomalous behaviors in this clip. Highlight loitering, sudden running, or leaving items.” For event summarisation, use: “Summarise the clip in three bullet points. Include people count, actions, and contextual cues.” For transcript verification, use: “Compare the transcript to the video. Flag mismatches and provide timestamps.” These prompt patterns help the model reduce false alarms and improve temporal consistency.
Prompt design affects generalisation. Clear prompts help zero-shot and few-shot performance. Conversely, ambiguous prompts can bias the model’s output and worsen anomaly detectors. To improve robustness, iterate with real-world clips, and collect feedback from operators. A prompt loop with human-in-the-loop correction helps refine the prompt and the model’s responses. Finally, remember that prompt templates are part of the deployment pipeline and should be versioned and audited for compliance.
experimental setup & experimental results
We designed experiments with both controlled dataset clips and in-the-wild footage. The controlled dataset included curated RGB frames with annotated anomalous events. The in-the-wild set used hours of surveillance video captured from multiple sites under varied lighting and compression. We also evaluated models on UCF-CRIME clips to benchmark action recognition and video-level labels. The experimental setup measured detection accuracy, false positives, time savings, and other operational metrics.
Evaluation metrics included AUC for detection, precision and recall for anomaly classification, false alarms per hour, and average time saved per incident. Quantitatively, multimodal VLM-based pipelines showed a 25% improvement in event detection and object recognition over single-modality baselines on mixed benchmarks. In addition, teams observed up to 40% reduction in review time when AI summarisation and VP Agent Search were in use time reduction study. However, the Deepfake-Eval-2024 benchmark highlighted a significant performance drop in real-world scenarios, confirming that robustness remains an issue performance drop in in-the-wild tests.
Challenges surfaced in generalisation and false positives. The number of false alarms rose when models saw different camera angles or novel types of anomalies. To address this, teams used pre-training on large image data, then fine-tuned on local training and testing data. They also embedded procedure-driven checks to reduce false positives, for example by cross-referencing access logs. These steps improved robustness and reduced the classifier for anomaly errors. Overall experimental results support multimodal VLMs as a promising approach, while also signalling the need for more realistic benchmark datasets and stronger temporal models Visual and Multimodal Disinformation report.
For readers who want applied examples, see our VP Agent features: forensic search in airports for quick historical queries (forensic search in airports), automated intrusion checks (intrusion detection in airports), and loitering analytics (loitering detection in airports).
FAQ
What are vision-language models and how do they differ from vision models?
Vision-language models combine visual encoders with language models to reason across images or videos and text. In contrast, vision models focus only on visual data and do not natively handle human language.
Can a VLM detect anomalous events in long surveillance feeds?
Yes. VLMs can prioritise clips and flag anomalous events so operators review fewer segments. They can also summarise events to speed investigation.
Are VLMs ready for real-world scenarios?
VLMs perform well on controlled datasets but may suffer a performance drop in realistic, messy conditions. Ongoing work improves robustness and benchmarking against in-the-wild footage.
How do prompts affect model outputs?
Prompts steer the model’s behaviour and scope. Clear, contextual prompts usually improve accuracy, while vague prompts can produce noisy or irrelevant output.
What role does AI play in control rooms?
AI triages alerts, reduces false alarms, and provides decision support. It can also pre-fill reports and automate low-risk workflows while keeping humans in the loop.
How do VLMs handle transcripts and metadata?
They embed transcripts and metadata into the shared latent space and cross-check them against visual signals. This helps verify statements and detect inconsistencies.
Do VLMs require a lot of labelled data?
Pre-trained models reduce the need for extensive labelled data, but fine-tuning on site-specific examples improves performance. Weakly supervised video anomaly methods can help when labels are scarce.
Can VLMs reduce false positives in alarms?
Yes. By adding contextual understanding and cross-referencing other systems, VLMs can lower false alarms and improve decision-making. Human oversight remains important.
How do you evaluate a VLM in practice?
Use metrics like detection accuracy, false positives per hour, precision, recall, and time saved per incident. Also test on both benchmark datasets and real-world scenarios for a full picture.
Where can I see examples of deployed systems?
For practical deployments, check examples such as intrusion detection in airports, loitering detection in airports, and forensic search in airports. These illustrate how VLMs enhance operational workflows.
