CV-Bench: Vision Benchmarking Suites

• CV-Bench is a suite of open-source benchmarks that evaluate visual defect detection, convolution primitives, and multimodal video reasoning using standardized data generation and evaluation protocols.
• It leverages both synthetic and real-world data along with precise instrumentation to fairly compare model performance and identify critical algorithmic bottlenecks.
• The benchmark outputs actionable insights by quantifying metrics such as mAP, latency, and reasoning accuracy, driving architectural improvements across diverse computer vision tasks.

CV-Bench denotes a class of open-source computer vision benchmarking suites targeting fundamentally distinct yet critically important domains: visual defect detection in infrastructure, primitive-level performance evaluation for convolution algorithms, and cross-video multimodal reasoning. The term is used in at least three prominent research efforts: CERBERUS (synthetic crack detection and recognition) (Reinman et al., 27 Jun 2025), ConvBench (2D convolution primitive evaluation) (Alvarenga et al., 2024), and CVBench (multimodal relational reasoning across multiple videos) (Zhu et al., 27 Aug 2025). Each implementation enforces rigor through formalized data generation, standardized protocols, and comprehensive diagnostic outputs, thus enabling unbiased evaluation and targeted architectural insight.

1. Formal Definitions and Scope

CERBERUS (“CV-Bench”) for Infrastructure Inspection

CERBERUS is a unified benchmark for crack and defect detection in built infrastructure, providing synthetic image pipelines, realistic 3D inspection scenarios rendered in Unity, and standard evaluation across varying geometries, lighting, and distractor conditions (Reinman et al., 27 Jun 2025).

ConvBench (“CV-Bench”) for Convolution Algorithm Analysis

ConvBench is a primitive-level benchmark enabling fair, exhaustive comparison across convolution implementation strategies (Direct, Im2col+GEMM, Winograd) via instrumented pre-, in-, and post-convolution routines over 9,243 deduplicated configurations extracted from 1,097 real-world Vision Transformer and CNN models (Alvarenga et al., 2024).

CVBench for Cross-Video Multimodal Reasoning

CVBench introduces the first large-scale diagnostic protocol for evaluating multimodal LLMs (MLLMs) in multi-video relational reasoning. It encompasses 1,000 multi-tiered question–answer instances derived from curated video clusters spanning five domains (sports, life records, artistic performances, etc.), rigorously testing models’ capacity for object, event, and complex commonsense synthesis across video streams (Zhu et al., 27 Aug 2025).

2. Dataset Construction and Generation Methodologies

CERBERUS

• Crack Image Generation: Each crack modeled as a stochastic parametric curve: $x(t)=x_0+\int_0^t \cos \theta(s) ds$, $y(t)=y_0+\int_0^t \sin \theta(s) ds$, with angular randomness $$\theta(s+\Delta s)=\theta(s)+\mathcal{N}(0, \sigma_\theta^2)+b\cdot\mathrm{Bern}(p_{\text{branch}})$$, controlled thickness $w\in\{2,3,4\}$ px, intensity profile $I(r)=I_0\exp(-r^2/(2\sigma_r^2))$, and Perlin noise background.
• Automatic Labeling: Crack pixels exported in YOLO normalized bounding-box format.
• 3D Scenario Construction: Unity 2022.3 HDRP, fly-by and underpass camera trajectories, physically based lighting models, randomized distractor placement.

ConvBench

• convSet Extraction: One random input tensor per model, capturing every unique 2D convolution configuration (batch size, channels, spatial, kernel, stride, padding, grouping, dilation).
• Deduplication: Yields 9,243 ops, with stratification by type (pointwise, grouped, dilated, rectangular).

CVBench

• Video Curation: 1,315 videos, five clusters, strict quality (≥720p@24 fps), annotated for intrinsic inter-video relationships.
• Question/Answer Synthesis: Adaptive segment-level captions (GPT-4o prompts), relational summary extraction (entity/event graphs), adversarial distractor crafting, multi-stage human quality control.

3. Evaluation Protocols and Metrics

CERBERUS

• Training Regimes: Synthetic-only, real-data-only, and combined sets, all models trained for 200 epochs (YOLOv11).
• Loss Function: Weighted sum of bounding-box, objectness, and class confidence terms.
• Metrics: Precision $P(\theta)$, recall $R(\theta)$, mean average precision (mAP) at $0.5$ IoU threshold.

ConvBench

• Instrumentation: Seven-stage timing breakdown via TM-Tool (preconv packing/transform, conv tile/pack/GEMM/unpack, postconv reorder).
• Metrics: Latency, speedup ($T_\text{baseline}/T_\text{main}$), throughput (GFLOPS/s), routine-specific timing distributions.
• Outputs: Automated CSV dumps, barplots, stacked timing breakdowns.

CVBench

• Model Evaluation: Closed-source (GPT-4o, Gemini-2.0-flash), open-source (Qwen2.5-VL-7B, etc.), reinforcement-trained (Video-R1-7B).
• Input Formats: Tokenized video segments, standardized frame sampling/resolution.
• Primary Metric: Accuracy $$=\frac{1}{N} \sum_{i=1}^N \mathbb{1}[\hat{y}_i=y_i]$$, precision, recall, and $F1$ at instance level.

4. Key Experimental Results

CERBERUS

Training Regime	[email protected] IoU (%)
Synthetic Only	48.7
Real Only	53.2
Real Only (More Data)	57.9
Synthetic + Real	61.5
Synthetic + Real (More Data)	67.4

• Mixing synthetic and real data achieves a $+8.3\%$ mAP improvement over single-source baselines; adding further training samples boosts mAP up to $67.4\%$.
• Fly-by scenarios achieve higher mAP ($\approx 72.1\%$) than underpass ($\approx 58.3\%$), quantifying lighting/geometry complexity impacts (Reinman et al., 27 Jun 2025).

ConvBench

Conv Type	# Ops	SConv Faster	SConv Slower
Regular	1,213	93.6%	6.4%
Pointwise	5,622	63.1%	36.9%

• Sliced Convolution (SConv) is $93.6\%$ faster than Im2col+GEMM in regular convolutions; slowdowns (6.4%) are traced to fallback scalar packing routines, averaging $+79.5\%$ time penalty.
• Fine-grained timing reveals pre-/post-conv overheads can dominate when core compute is highly optimized (Alvarenga et al., 2024).

CVBench

Model	Object Assoc.	Event Assoc.	Complex Reasoning
Human	88.9%	92.7%	91.3%
GPT-4o	66.9%	70.8%	69.1%
Gemini 2.0	64.5%	67.0%	69.4%
Qwen2.5-VL	53.2%	54.0%	51.3%
Video-R1-7B	54.4%	61.6%	49.2%
Random	27.4%	33.8%	26.2%

• Top-performing MLLMs trail human accuracy by $\sim 22$ points overall, and more than $30$ points on multi-video temporal and counterfactual reasonings (Zhu et al., 27 Aug 2025).

5. Diagnostic Analysis and Architectural Insights

• CERBERUS: Ablation studies indicate crack geometry noise controls and realistic background texture significantly affect detection robustness; underpass scenes introduce systematic performance loss due to lighting/shadow complexity.
• ConvBench: Bottlenecks isolated to packing kernel fallback in SConv for stride$>$1 or low channel counts; resolving these can eliminate nearly all observed slowdowns. Pre- and post-processing steps are nontrivial contributors to total latency in high-performance kernels.
• CVBench: Three principal MLLM failure points are identified:
  1. Insufficient inter-video context retention: entity/event states are non-coherent across streams.
  2. Poor entity disambiguation: visually similar objects fail to be reliably matched.
  3. Suboptimal temporal-causal modeling: inability to capture long-range dependencies impacts multi-event and counterfactual inference.

6. Recommendations and Future Directions

• CERBERUS: Extensions toward multi-class defect detection, sophisticated lighting models (HDRI skies), and optimized UAV flight-paths for 3D scene reconstruction are suggested.

• ConvBench: Kernel designers should implement specialized microkernels for edge cases (low channel count, non-unit stride) and closely instrument routine overheads beyond core compute.
• CVBench: Suggested strategies include explicit cross-video memory modules, cross-stream attention architectures, synthetic curriculum pretraining, and hybrid symbolic–neural reasoning layers to improve causal integration and multi-hop inference. Curriculum escalation from object association to complex reasoning is advocated to enable robust generalization.

7. Resources and Reproducibility

• CERBERUS (CV-Bench): Publicly released at https://github.com/justinreinman/Cerberus-Defect-Generator (Reinman et al., 27 Jun 2025).
• ConvBench: C++ header-only framework and evaluation kit at https://github.com/LucasFernando-aes/ConvBench (Alvarenga et al., 2024).
• CVBench: Complete QA dataset, annotation workflow, and evaluation code at https://github.com/Hokhim2/CVBench (Zhu et al., 27 Aug 2025).

CV-Bench, in its various incarnations, sets demanding standards for dataset diversity, methodological clarity, and diagnostic feedback, serving both as reference protocol for algorithmic comparison and as driver for targeted architectural enhancement across multiple computer vision subfields.