Holmes-VAD Framework Overview

• Holmes-VAD is a dual-framework integrating advanced video anomaly detection and Bangla ASR with diarization, characterized by precise temporal supervision and robust post-processing.
• The video module employs a VAD-Instruct50k benchmark, modular encoder-sampler architecture, and multimodal LLM for efficient anomaly localization and natural language explanation.
• The speech component leverages noise removal, CTC-based alignment, and a three-phase diarization curriculum to achieve low error rates in long-form Bangla recordings.

Holmes-VAD refers to two distinct but highly technical frameworks in recent research literature: one addressing interpretable video anomaly detection via multimodal LLMs (Zhang et al., 2024), and the other describing a holistic Bangla automatic speech recognition (ASR) and speaker diarization pipeline built around advanced Voice Activity Detection (VAD) and Connectionist Temporal Classification (CTC) alignment (Ishmam et al., 26 Feb 2026). Both frameworks are characterized by their emphasis on precise temporal supervision, modular front-end designs, and robust post-processing for real-world untrimmed data. This entry details both systems under the unifying umbrella of “Holmes-VAD”.

1. VAD-Instruct50k Benchmark and Label-Efficient Annotation (Video)

Holmes-VAD for video anomaly detection introduces the first large-scale instruction-tuning benchmark for VAD, termed VAD-Instruct50k. The benchmark leverages a semi-automatic labeling paradigm aimed at efficient and precise temporal annotation.

• 5,547 untrimmed videos were drawn from UCF-Crime and XD-Violence datasets, initially labeled at the coarse video level.
• Annotation proceeds in three steps:
  1. Single-frame annotation: Human annotators click a single frame per abnormal event (average ≈ 2.35 clicks/video), localizing anomaly timing at dramatically reduced cost (~100× cheaper than dense framewise labeling).
  2. Event-clip generation: A preliminary VAD network $\phi_s$ expands each click into a short abnormal clip based on frame-level anomaly scores $S = \{s_1, ..., s_N\}$, pairing with random clips from normal videos to form event sets $\mathcal{E} = \{ (s_i, e_i, y_i) \}$ with $y_i \in \{$Normal, Explosion, ...$\}$.
  3. Event-clip captioning: Each clip is captioned via an off-the-shelf video captioner (e.g., Video-LLaVA), yielding natural-language description $c_i$.

• Multimodal instruction items are constructed by crafting prompts $P_t$ (embedding $y_i$, $c_i$), enabling a large LLM (Llama3-Instruct-70B) to generate “assistant” replies to queries such as “Are there any unexpected events in this clip? Explain why.”

• After manual filtering, the benchmark contains 51,567 items, each comprising a trimmed video clip, anomaly label, caption, and a ~45-word explanation.

This annotation pipeline both reduces human labor and enhances anomaly-focused dialogue data for LLM instruction tuning (Zhang et al., 2024).

2. Holmes-VAD Framework: Model Components and Architectural Design (Video)

The Holmes-VAD video system is structured around three core modules:

• Video Encoder ($\phi_v$): A frozen ViT-L/14 (from LanguageBind) extended with a temporal self-attention layer. For $S = \{s_1, ..., s_N\}$0 input frames of resolution $S = \{s_1, ..., s_N\}$1, each outputting:

$S = \{s_1, ..., s_N\}$2

where $S = \{s_1, ..., s_N\}$3 is the [CLS] token, and $S = \{s_1, ..., s_N\}$4 are patch tokens.

• Temporal Sampler ($S = \{s_1, ..., s_N\}$5): A lightweight, trainable module that maps the sequence of frame [CLS] features to per-frame anomaly scores:

$S = \{s_1, ..., s_N\}$6

At inference, only frames where $S = \{s_1, ..., s_N\}$7 (with $S = \{s_1, ..., s_N\}$8) are retained. The sampler is implemented as a UR-DMU (Unified Representation, Dual Memory Units) with global and local multi-head self-attention and two memory banks representing normal and abnormal prototypes. The loss combines MIL, magnitude, triplet, and KL terms:

$S = \{s_1, ..., s_N\}$9

• Projector and Multimodal LLM: A two-layer MLP $\mathcal{E} = \{ (s_i, e_i, y_i) \}$0 maps the selected tokens to the LLM token space. The LLM backbone (Vicuna, Video-LLaVA initialized), with LoRA adapters, processes the concatenated visual and text embeddings:

$\mathcal{E} = \{ (s_i, e_i, y_i) \}$1

Outputs are both a “Yes/No” anomaly decision and a free-form, natural-language explanation.

This architecture ensures high temporal precision and comprehensive interpretability for open-ended anomaly detection.

3. Training Paradigms and Instruction Tuning (Video)

Temporal Sampler Supervision

Single-frame temporal clicks are used to generate dense soft pseudo-labels via:

$\mathcal{E} = \{ (s_i, e_i, y_i) \}$2

where $\mathcal{E} = \{ (s_i, e_i, y_i) \}$3 are anomalous snippet indices and $\mathcal{E} = \{ (s_i, e_i, y_i) \}$4. The anomaly loss is defined as binary cross-entropy:

$\mathcal{E} = \{ (s_i, e_i, y_i) \}$5

The total sampler loss:

$\mathcal{E} = \{ (s_i, e_i, y_i) \}$6

Frames are sampled with stride 16; Adam optimizer with learning rate $\mathcal{E} = \{ (s_i, e_i, y_i) \}$7.

Instruction Tuning (Projector + LoRA)

• Projector and LoRA adapters on the LLM are fine-tuned: each item presents a trimmed clip’s visual tokens and the corresponding user prompt.
• Objective is token-level negative log-likelihood on ground-truth reply $\mathcal{E} = \{ (s_i, e_i, y_i) \}$8:

$\mathcal{E} = \{ (s_i, e_i, y_i) \}$9

• Hyperparameters: projector LR $y_i \in \{$0, batch 128, LoRA rank 64 ($y_i \in \{$1, LR $y_i \in \{$2), LLM backbone frozen.

This training regime encourages both efficient anomaly localization and high-fidelity explanation, supervised with large-scale, multimodal instruction data (Zhang et al., 2024).

4. Inference Workflow and Output Interpretation (Video)

The Holmes-VAD video inference pipeline is as follows:

1. Accept an untrimmed video and user query.
2. Dense feature extraction of all frames via $y_i \in \{$3.
3. Frame-level anomaly scoring $y_i \in \{$4.
4. Retain only frames $y_i \in \{$5 (typically reducing computation by 90%).
5. Project selected tokens via $y_i \in \{$6 and concatenate with text prompt.
6. Pass to LLM, which emits:
   • “Yes/No” for segment anomaly
   • Explanation for each flagged segment
7. Return a time series of anomaly scores (interpolated to all frames) and an explanatory report e.g., “At $y_i \in \{$7s a person is running against traffic, which is highly unusual…”

This pipeline enables scalable, interpretable anomaly detection and reporting on hour-scale video inputs (Zhang et al., 2024).

5. Empirical Results, Human Study, and Ablation Analyses (Video)

Holmes-VAD is evaluated on UCF-Crime and XD-Violence benchmarks:

Method	XD-Violence AP	UCF-Crime AUC
Holmes-VAD	90.67%	89.51%
VadCLIP (prev SOTA, non-explainable)	84.51%	88.02%
LAVAD (explainable)	62.01%	80.28%

• Single-frame supervision alone increases AP/AUC by at least 4.5%.
• Human evaluation over 86 clips (10 raters):

Setting	Judgement Accuracy (JA)	Content Perception (CP)	Anomaly Explanatory (AE)
Training-free LLM	65.1%	11.6%	15.9%
FT Projector	81.4%	27.2%	32.2%
Projector + LoRA (default)	86.0%	61.2%	51.9%

• Temporal sampler reduces inference latency from 32.8s to 4.24s per video compared to uniform sampling, with much higher accuracy.
• Qualitative outputs demonstrate context-sensitive explanations, e.g., distinguishing sports fouls from normal gameplay.

This demonstrates state-of-the-art accuracy and clear explanation capability compared to prior art (Zhang et al., 2024).

6. Holmes-VAD for Holistic ASR and Speaker Diarization (Speech)

A separate framework, also referenced as Holmes-VAD, targets robust Bangla ASR and speaker diarization in long-form audio (>3,000s) (Ishmam et al., 26 Feb 2026). The architecture features:

• Noise removal (Demucs): Separation of vocals from background to yield clean audio.
• Optimized VAD: Energy, zero-crossing rate, and spectral entropy features, with adaptive thresholds:

$y_i \in \{$8

$y_i \in \{$9

$\}$0

Speech is detected via:

$\}$1

Post-processing includes median filtering and minimum segment enforcement.

• CTC Forced Alignment and Chunking: MMS-300M CTC aligner predicts per-frame posteriors; Viterbi forced alignment yields precise word-level timestamps; segments are kept <30s to match Whisper ASR input limits.
• ASR Model Training: Whisper is fine-tuned (bengaliAI/tugstugi_bengaliai-asr_whisper-medium) on 158 hours of clean, chunked audio (WER ~22.7%).
• Three-phase Diarization Curriculum: pyannote.audio is adapted in sequence: (1) on raw audio, (2) Demucs-cleaned vocals, (3) with dynamic augmentation (random gain, noise). Speaker embeddings are clustered to generate diarized outputs.

Final outputs are diarized transcripts with word-level timestamps and speaker attribution, optimized for long-form, noisy, multi-speaker Bangla speech (Ishmam et al., 26 Feb 2026).

7. Evaluation Metrics and Performance (Speech)

Empirical performance for the Bangla ASR and diarization system:

Model	Public WER	Private WER
tugstugi fine-tuned	21.99%	23.58%
tugstugi zero-shot	36.14%	37.85%
Bangla-ASR fine-tuned	50.05%	54.33%
Mozilla Large (base)	63.17%	69.72%
Whisper zero-shot	86.59%	88.63%

Diarization Strategy	Public DER	Private DER
Normal fine-tuning (base)	23.15%	31.13%
+ Demucs refinement	21.62%	33.45%
+ Data augmentation	21.46%	32.66%

The system sustains low WER and DER over long recordings, with inference throughput of ~2 hours for a 158h test set on two T4 GPUs. Every component—VAD, CTC chunking, fine-tuned ASR, noise removal, augmentation, and curriculum diarization—contributes to domain-robust performance (Ishmam et al., 26 Feb 2026).