Choose the route closest to your main contribution, then adapt it. Architecture, objective, theory, analysis, resource, system, application and position arguments carry different evidence obligations. Only types present in this inspected corpus appear below. These editorial routes do not replace the conference's official track definitions.
For a new design, prepare a readable computation or representation overview, a comparison with matched conditions, and controls that test the changed component. Add pseudocode when iteration, state, sampling or gradient routing would otherwise be ambiguous. Add a theorem only when making a formal claim. A position paper instead needs a defensible thesis, substantive alternatives and implications; it is not required to pretend to be a leaderboard method.
Observed medians describe source versions. Suggested display packages are conditional editorial starting points, not official requirements or evidence of a necessary number of figures. Small groups provide examples, not robust estimates of a conference-wide standard.
The central deliverable changes the model's representation, component arrangement, information flow or reusable inference pipeline. Data, objectives and efficiency can still be substantial supporting contributions.
Corpus context · 23 papers: median 6 main figures and 4 main tables; body span ≈ 8.1 pages; attached appendix span ≈ Not resolved pages (0 resolved boundaries). Separate source files: median Not resolved whole-file pages among 0 papers; these can repeat main text and are not appendix-only lengths. Extraction-assisted observations—not drafting targets.
Explain the failure that motivates a component, show the relevant data path, then test the component in the conditions where it should matter. Distinguish training from inference and learned modules from external pretrained dependencies. Use an architecture or train/inference comparison only when ownership, dependencies or different paths are central to the claim. Use component controls with a shared protocol; retain task-specific non-wins and indicate resource-sensitive settings. A complex diagram or a sequence of cumulative ablations is not by itself evidence that every component is necessary or independently causal.
Editorial starting package: choose artifacts that test your actual claims. Quantities below are planning suggestions, not counts inferred from all category members or conference rules.
Start with one overview; add insets only when they resolve different questions
Label inputs, outputs, inherited versus new components, frozen/trainable blocks and training-only paths.
When: Core when the novelty is computational organization; use a simple representation cartoon before the full diagram if the data object is unfamiliar.
One compact example or aligned qualitative grid
Make the claimed representational problem visible and reuse the same example through the mechanism.
When: Especially useful for 3D geometry, multimodal tokens, articulated objects or structured outputs; do not add a gallery that does not test the claim.
One focused table per incompatible task or information regime
Keep metric direction, data, backbone, reference inputs and refinement policy next to the results.
When: Separate single-model inference from per-instance optimization and privileged-input baselines.
A compact switch table or matched diagnostic panels
Test the named reason for each component rather than removing modules without preserving compute or capacity.
When: Use matched controls when the mechanism claim depends on architecture rather than more parameters or more supervision.
Only when the procedure or guarantee requires it
Expose iteration, state, branching or assumptions not recoverable from the diagram.
When: A feed-forward module does not require pseudocode or a theorem simply to look like an accepted paper.
If implementation variants or resolution sweeps cannot fit, supply exact settings and dependency versions in a clearly named supplement; do not substitute a pointer for accessible evidence.
CorrCLIP gives scope and value reconstruction distinct jobs, exposes the external mask/feature models, and retains branch non-wins and mask-resolution costs.
ForestFormer3D distinguishes random training cylinders from overlapping inference cylinders and whole-scene assembly; separate instance and semantic metrics reveal trade-offs.
Boundary: This route contains 23 primary editorial assignments in the 64-paper inspected subset (64 inventory records). It is not an official track, a universal writing standard or evidence that these choices caused acceptance.
See inline examples of these artifact styles · Compare every member in the measurement ledger
The primary deliverable adds distinct motion extraction and memory-based detail-recovery components to an inherited animation pipeline.
The central method is a reusable post-processing pipeline for token-level visual explanations, supported by a specifically defined plausibility evaluation.
The fused 3D feature representation and directed kNN tracking update are the primary changed computation.
A dual-stream conditional diffusion representation and its reference-injection blocks organize the contribution; hybrid data construction supports that pipeline.
The central contribution reorganizes a tracking front end, motion-aware bundle adjustment and dense refinement into a coupled reconstruction pipeline.
A modified heat-conduction visual encoder and spatial correction embeddings are the primary changed representation, supported by tailored pretraining losses.
A coordinate-based cross-attention upsampler is the reusable module, with separately analyzed high-resolution supervision enabling it.
Location-aware grouping, intra-group masking and cross-group aggregation change the MIL computation; efficiency is a supporting outcome.
A controllable masked-motion pipeline combines differentiable token sampling, an added conditioning branch and inference-time logits guidance.
Separate face/hair identity-conditioned representations and a shared scalp anchor are the central reusable model design, enabled by synthetic supervision.
A sparse surface representation and self-pruning VAE are the central modeling contribution, enabled by sectional rendering training.
A frame-dimension conditioning mechanism and target-pose injection adapt a video generator to synchronized camera-controlled re-rendering.
A geometry-aware control-path construction and second-order neural controlled differential equation form the forecasting model.
Local ordering-guided updates and global registration/confidence fusion redesign the interaction of monocular priors with iterative stereo matching.
A non-parametric latent-compositing replacement for cross-attention supplies the new occlusion-control mechanism without parameter training.
A shared latent guidance design and motion-adaptive normalization combine local character and global scene motion.
A pyramid wavelet tokenizer and interacting low/high-frequency transformer decoders form the central super-resolution model.
Pair-centric encoding and iterative diffusion guidance are the reusable mechanism for avoiding cross-garment attribute confusion.
The central model predicts superquadric parameters and soft point assignments, followed by geometric refinement, to form a reusable compact representation.
The primary contribution composes pretrained encoders and masks into a new correlation-reconstruction and feature-refinement inference architecture.
The primary contribution is a modular perception/planning/reasoning architecture with query-specific scene-graph state and streaming execution.
The core contribution is a sparse voxel encoder and lightweight decoder combining implicit click fusion with learned negative embeddings.
The main method introduces forest-adapted query selection, one-to-many association and score-based merging, supported by an expanded evaluation dataset.
The argument primarily changes a loss, supervision signal, optimization/update rule, distillation recipe or adaptation procedure. Some assigned methods update at inference time; the label refers to the objective-driven argument, not only offline training.
Corpus context · 9 papers: median 6 main figures and 6 main tables; body span ≈ 8.06 pages; attached appendix span ≈ Not resolved pages (0 resolved boundaries). Separate source files: median Not resolved whole-file pages among 0 papers; these can repeat main text and are not appendix-only lengths. Extraction-assisted observations—not drafting targets.
Define what the objective is intended to preserve, where its supervision comes from and how the signal changes across stages before reporting downstream scores. Show supervision, frozen/trainable boundaries and information flow when these distinguish the objective from an architectural change. Compare the proposed signal against simpler or removed-signal controls under matched input and training access; report downstream metrics separately from the objective’s own proxy. Improvement in a proxy or attribution-selection criterion is not automatic evidence of downstream validity, faithfulness or embodiment-independent control.
Editorial starting package: choose artifacts that test your actual claims. Quantities below are planning suggestions, not counts inferred from all category members or conference rules.
One diagram when multiple paths or update stages matter
Mark teachers, students, pseudo-labels, frozen weights, detached gradients and inference-only operations.
When: Use gradient arrows or phase panels when an ordinary module diagram would hide the actual intervention.
An explicit objective; an algorithm box if iterative or stateful
Define variables, positive/negative samples, weights, updates and stopping rather than listing an unexplained weighted sum.
When: A short closed-form loss may need no pseudocode; a multistage update usually does.
One task-specific table or paired baseline/new-rule rows
Hold backbone, supervision and budget fixed where possible so a training-rule claim is not merely a larger-model claim.
When: Show unmatched leaderboard context separately from causal design comparisons.
One appropriate panel per central explanatory claim
Measure the suspected mismatch: gradient behavior, confusing classes, reliability, alignment or temporal persistence.
When: Use the quantity the paper actually claims to improve, not an arbitrary feature visualization.
A compact control table and consequential parameter curves
Distinguish new supervision, loss terms, schedules and extra compute; retain collapse or negative runs.
When: Do not invent large sweeps for inconsequential parameters or hide tuning costs.
Optional
State assumptions and whether the implemented approximation satisfies them.
When: A proof is needed for a formal claim, not as decoration for an empirical improvement.
Provide exact loss weights, schedules, negative/positive construction and selection rules when these affect reproducibility.
SLOC motivates a relaxed local-completeness objective with a counterexample, separates cached black-box responses from attribution optimization, and distinguishes explanation metrics from segmentation accuracy.
Moto makes frozen modules and action-query adaptation visible, then separates tokenizer quality, pretraining priors and policy utility; comparison rows disclose observation spaces.
GT-Loc motivates cyclic time structure separately from geographic alignment; loss, data-fraction and label-noise comparisons keep the supervision argument tied to observable outcomes.
Boundary: This route contains 9 primary editorial assignments in the 64-paper inspected subset (64 inventory records). It is not an official track, a universal writing standard or evidence that these choices caused acceptance.
See inline examples of these artifact styles · Compare every member in the measurement ledger
The paper motivates and tests a cyclic temporal metric-learning objective within a shared retrieval representation.
The central argument is a self-supervised view-synthesis objective with a controlled information split that jointly learns pose and scene representations.
Uncertainty-guided perturbation and cross-model consistency change the scene optimization objective without changing the deployed rendering pipeline.
The core transferable lesson is the joint visual-language training objective across global, temporal and spatial tasks, enabled by a causal adapted backbone.
A pairwise bidirectional entailment objective structures existing image encoders, with a new evaluation view tailored to that objective.
A diffusion-prior regularizer and masked mixed-data objective make one pose prior usable across heterogeneous inverse problems.
Localized semantic supervision and staged curve optimization are the principal mechanism for a geometry-aware abstraction representation.
Motion-token autoregression and joint motion/action fine-tuning supply the transferable training interface between video and control.
The central contribution is a normalized completeness-gap objective and its attribution-map optimization, supported by empirical explainability evaluations.
A formal characterization, algebraic result, guarantee or theoretically derived explanation organizes the main argument, with algorithms and experiments testing consequences or practical approximations.
Corpus context · 4 papers: median 3.5 main figures and 3.5 main tables; body span ≈ 8.05 pages; attached appendix span ≈ Not resolved pages (0 resolved boundaries). Separate source files: median Not resolved whole-file pages among 0 papers; these can repeat main text and are not appendix-only lengths. Extraction-assisted observations—not drafting targets.
State the mathematical obstacle and assumptions, expose a failure or counterexample when useful, then distinguish the guarantee from approximate computation and empirical accuracy. Use a geometric counterexample, calibration/propagation diagram or failure illustration when it explains an assumption better than notation alone. Where a computational guarantee is claimed, report certificate/tightness or coverage with accuracy, compactness and cost; do not let one metric stand for the others. These are writing observations. The studies did not independently validate proofs, certificates or guaranteed coverage after approximation.
Editorial starting package: choose artifacts that test your actual claims. Quantities below are planning suggestions, not counts inferred from all category members or conference rules.
One when it makes unfamiliar definitions operational
Connect algebraic symbols to camera geometry, representations or update behavior.
When: Prefer a reusable symbolic example over an unrelated network schematic.
One short map or comparison table when several regimes are involved
Keep quantifiers, non-degeneracy conditions, approximation regimes and guarantee types visible.
When: Especially important when exact theory motivates an approximate practical solver.
As many as the actual logical claims need
State what is proved and identify the central argument before full technical details.
When: No target theorem count; empirical observations must not be promoted to universal guarantees.
Only when there is an empirical consequence to test
Test the predicted difficult regime, scaling, geometry or optimizer behavior under stated assumptions.
When: A finite experiment illustrates or challenges a consequence; it does not prove an asymptotic statement.
Include where needed to prevent a tempting overgeneralization
Explain the role of assumptions and distinguish sufficient from necessary conditions.
When: A theoretical paper need not manufacture a broad benchmark leaderboard if it is irrelevant to the result.
Route derivations and proof details to precise theorem-specific locations, retaining assumptions and claim boundaries in the main paper.
Anisotropic rotation averaging connects uncertainty to a determinant-constraint failure and replacement relaxation; solution rank is reported alongside reconstruction error and runtime.
Pose-region estimation separates calibration from nonlinear propagation and reports coverage alongside region volume, while acknowledging Gaussian/nonlinear coverage limitations.
Boundary: This route contains 4 primary editorial assignments in the 64-paper inspected subset (64 inventory records). It is not an official track, a universal writing standard or evidence that these choices caused acceptance.
See inline examples of these artifact styles · Compare every member in the measurement ledger
Geometric parameterization and multi-view self-calibration propositions organize the algorithm and evaluation.
The central contribution analyzes anisotropic relaxation failure and derives a stronger certifiable optimization formulation.
The contribution centers on calibrated keypoint regions and deterministic propagation to pose uncertainty, evaluated jointly for coverage and volume.
New relative-pose/focal solvers are derived for explicit depth scale/shift assumptions, with a comparative empirical decision framework.
The main new knowledge is a controlled measurement, diagnostic, comparison, failure analysis or audit. A proposed remedy, model or tool can follow from that evidence.
Corpus context · 5 papers: median 8 main figures and 5 main tables; body span ≈ 8.09 pages; attached appendix span ≈ Not resolved pages (0 resolved boundaries). Separate source files: median Not resolved whole-file pages among 0 papers; these can repeat main text and are not appendix-only lengths. Extraction-assisted observations—not drafting targets.
Make the question, controlled factors, evaluation unit and uncertainty construction explicit before interpreting a trend. Separate measured behavior from scaling extrapolations and mechanism hypotheses. Use controlled sweeps or paired contrasts when trends are the contribution; show observed ranges, baselines, uncertainty units and extrapolation boundaries. Specify splits, sample units, repeated-run conditions and exceptions; distinguish oracle from usable selection rules. Repeated frames are not necessarily independent samples, correlation is not a causal mechanism, and a fitted scaling forecast is not an observed result.
Editorial starting package: choose artifacts that test your actual claims. Quantities below are planning suggestions, not counts inferred from all category members or conference rules.
One compact view
Expose information access, sampling, intervention and matched controls.
When: Essential when teacher labels, dataset splits, task direction or model access determine the interpretation.
One clear entry point
Display the relationship, denominator and variability before a proposed explanation.
When: Show representative ranges rather than selected successes alone.
One destination per central explanatory claim
Distinguish the proposed mechanism from compute, label quality, dataset composition or evaluation artifacts.
When: Correlation and a fitted trend require different language from a controlled causal intervention.
A small aligned set with selection criteria
Make measured failures inspectable and include ambiguous/negative cases where relevant.
When: Qualitative cases illustrate a study; they do not replace prevalence estimates.
As needed by generalization or practicality claims
Repeat the protocol on different tasks/models/settings and report the actual cost.
When: Keep missing, unavailable or diverged results distinct from zero.
Supply robustness sweeps and uncertainty details that allow the trend to be challenged, not merely more favorable examples.
Radar scaling accounts for temporal dependence through effective sample size, uses geographic test separation and paired comparisons, and calls the100M-sample forecast a projection.
Native multimodal scaling distinguishes pretraining-loss trends from downstream task measurements and preserves a task-specific non-win instead of making the scaling claim universal.
DIIP separates observable stopping behavior from oracle diagnostics; the study records the source’s inconsistent oracle water-image values rather than silently harmonizing them.
Boundary: This route contains 5 primary editorial assignments in the 64-paper inspected subset (64 inventory records). It is not an official track, a universal writing standard or evidence that these choices caused acceptance.
See inline examples of these artifact styles · Compare every member in the measurement ledger
Controlled scaling and routing comparisons organize the contribution; architectures are instruments of the empirical investigation.
The argument centers on reinterpreting inversion and validating a heuristic velocity-averaged editing path with controlled transport and fidelity comparisons.
The contribution develops and validates output-derived predictors of detector performance and a corruption-based evaluation protocol.
The main argument studies a pretrained diffusion reconstruction prior and converts the observed optimization dynamics into a blind-restoration procedure.
A large raw-radar dataset and deliberately simple transformer support an empirical study of representation, scaling and transfer choices.
A new corpus, acquisition/annotation process, evaluation suite or measurement protocol is the primary reusable deliverable. A companion model demonstrates utility without replacing the need to validate the resource.
Corpus context · 4 papers: median 5.5 main figures and 6.5 main tables; body span ≈ 8.14 pages; attached appendix span ≈ Not resolved pages (0 resolved boundaries). Separate source files: median Not resolved whole-file pages among 0 papers; these can repeat main text and are not appendix-only lengths. Extraction-assisted observations—not drafting targets.
Explain the coverage gap in acquisition/task terms, then connect curation choices, splits, targets and scoring to that gap before showing benchmark results. Use an acquisition or curation schematic when it explains what existing benchmarks omit. Include dataset units, coverage, split logic, target provenance and task-specific metrics; distinguish native ground truth from constructed or pseudo targets. Dataset size alone does not establish diverse independent samples, and a benchmark’s approximate target should not be described as authoritative physical ground truth.
Editorial starting package: choose artifacts that test your actual claims. Quantities below are planning suggestions, not counts inferred from all category members or conference rules.
One compact resource table
Compare capabilities, annotation units, acquisition conditions and evaluation tasks, not only total size.
When: Use explicit partial or unavailable coverage rather than forcing binary superiority.
One process diagram with examples
Show raw inputs, transformations, human/machine verification and final annotations.
When: Distinguish generated labels, human corrections and measured ground truth.
A distribution, hierarchy or protocol matrix suited to the labels
Show subjects/scenes/classes, long tails, acquisition diversity and what is held out.
When: Avoid conflating frames, tracks, objects, rendered variants and independent subjects.
Focused tables with the same taxonomy used in the protocol
Demonstrate what the resource reveals and expose non-dominating models or hard subgroups.
When: A high average score alone does not validate label quality or benchmark relevance.
One verification analysis when annotation or metric validity is a claim
Document agreement, quality checks, adversarial controls, contamination checks or calibration.
When: Select the check that addresses this resource's actual failure risks; do not imply a single check proves complete validity.
Provide annotation/quality-control procedures, licenses and curation details when applicable, with accessible files and explicit gaps.
MIORe distinguishes motion types, acquisition and effective frame rates, temporal spans and pseudo-ground-truth flow; its optical-flow evaluation is labeled preliminary.
Copernicus separates data, model and benchmark contributions, preserves task-specific metrics and absent modalities, and states sensor/time-coverage limits.
Boundary: This route contains 4 primary editorial assignments in the 64-paper inspected subset (64 inventory records). It is not an official track, a universal writing standard or evidence that these choices caused acceptance.
See inline examples of these artifact styles · Compare every member in the measurement ledger
The curated scene-scale Gaussian corpus is a central reusable deliverable and underpins both separately evaluated pretraining paths.
The aligned multi-sensor corpus and hierarchical evaluation suite are substantial reusable contributions organizing the model and evidence.
Three explicitly defined gesture-understanding tasks and manually curated benchmarks organize the representation-learning contribution and its evaluation.
The primary contribution is a high-motion multi-task dataset construction and benchmark protocol rather than a new restoration model.
The central contribution changes execution, inference, memory, acquisition cost or resource scaling while preserving a stated quality or correctness contract.
Corpus context · 8 papers: median 7.5 main figures and 4.5 main tables; body span ≈ 8.09 pages; attached appendix span ≈ Not resolved pages (0 resolved boundaries). Separate source files: median Not resolved whole-file pages among 0 papers; these can repeat main text and are not appendix-only lengths. Extraction-assisted observations—not drafting targets.
Locate the expensive operation, explain the replacement or removal, and report quality together with resource use under a clearly bounded execution protocol. Show the removed bottleneck or execution/capture sequence when it is the mechanism of savings. Pair quality with latency, memory/storage, hardware, resolution and workload; use the resource actually claimed rather than a convenient proxy. Mixed-hardware inherited timings, disk-size labels, training-only costs and capture-count differences must remain visible; do not turn them into an unconditional speedup.
Editorial starting package: choose artifacts that test your actual claims. Quantities below are planning suggestions, not counts inferred from all category members or conference rules.
One focused breakdown or controlled comparison
Explain which stage is costly and why the proposed decision variable could help.
When: Use matched budgets when an oracle analysis motivates an approximate implementation.
One overview with needed stage or memory insets
Mark preprocessing, cache/reuse, training versus inference, hardware calls and synchronization.
When: The figure should expose added overhead as well as removed work.
One panel per materially different workload
Pair quality with latency, throughput, memory or acquisition effort at comparable operating points.
When: State hardware, precision, batch/sequence/image size and what the timer includes.
A table or stacked breakdown
Show whether an isolated saving survives the entire pipeline.
When: Do not substitute FLOPs, token count or number of steps for wall-clock measurements without qualification.
Curves only for the dimensions the system claims to scale
Measure view/frame count, problem size, cache refresh or device constraints and retain adverse regimes.
When: Changing hardware or input capacity is a different comparison from identical-workload acceleration.
As needed for the execution contract
Make reuse, selection, fallback, stopping and approximation explicit.
When: A claimed exact or distribution-preserving procedure needs a precise argument beyond similar output scores.
Disclose timing protocol, implementation details, breakdowns and failure/OOM conditions needed to reproduce the trade-off.
ReCoVEr separates whether, when and how to remove the cost volume, then pairs endpoint error with runtime/memory and records failure modes.
EVER explains exact integration for its chosen primitives, separates derivation from implementation, and defines memory as disk size while disclosing GPU/resolution differences.
Spatially-varying autofocus reports capture counts, quality/MTF and light/aperture costs; no post-capture reconstruction does not mean no computation.
Boundary: This route contains 8 primary editorial assignments in the 64-paper inspected subset (64 inventory records). It is not an official track, a universal writing standard or evidence that these choices caused acceptance.
See inline examples of these artifact styles · Compare every member in the measurement ledger
Removing a costly inference component while retaining optical-flow quality is the central resource contract.
The argument changes rendering execution and representation to satisfy a 3D-consistent blending contract at usable frame rates.
The contribution is a structured post-training compression procedure evaluated under an explicit short-finetuning resource budget.
The method changes inference token allocation to trade computational cost against generation fidelity without retraining.
The central deliverable reduces codec resources through modular distillation and a teacher-aligned compact student, evaluated against rate-distortion performance.
The primary contract is fewer diffusion sampling steps at maintained LiDAR completion quality, supported by structural distillation losses.
The central intervention replaces repeated stored soft labels with a compact reusable projector and measures downstream utility under that storage contract.
The contribution is an integrated programmable optical system and feedback-control workflow for spatially varying autofocus.
The primary argument is a task-specific capability, physical measurement/reconstruction system or domain-grounded method whose validity depends on real acquisition, task assumptions and domain-appropriate evidence.
Corpus context · 11 papers: median 7 main figures and 3 main tables; body span ≈ 8.05 pages; attached appendix span ≈ Not resolved pages (0 resolved boundaries). Separate source files: median Not resolved whole-file pages among 0 papers; these can repeat main text and are not appendix-only lengths. Extraction-assisted observations—not drafting targets.
Translate the domain constraint into a measurable task, show how intermediate estimates connect to the final output, and evaluate against a reference whose provenance is explicit. Show a domain workflow or intermediate measurement chain when application constraints are part of the contribution. Report task-specific errors, reference provenance and operating conditions; include intermediate diagnostics when a final score hides distinct failure sources. A convincing demonstration is not automatically clinical, safety or broad real-world validation; preserve the studied population, equipment and reference-standard limits.
Editorial starting package: choose artifacts that test your actual claims. Quantities below are planning suggestions, not counts inferred from all category members or conference rules.
One setup or input/output view
Show sensors/modalities, calibration, measured versus generated quantities and what is available at use time.
When: Essential when hardware, multimodal cohorts or nonstandard labels determine the claim.
One overview with necessary physical or anatomical labels
Connect variables, modules and losses to the domain problem rather than only showing generic network boxes.
When: Keep measured inputs, latent physical quantities and predicted outputs visually distinct.
A compact grid or profile plot
Compare matched scenes/patients/objects with relevant maps, spectra, overlays or error views.
When: Label simulation, real data, pseudo-ground-truth and unavailable measurements; an appealing image is not independent validation.
One table per incompatible endpoint
Report the relevant physical/clinical/behavioral metric, units, uncertainty and evaluation split.
When: Separate image quality from geometry, survival discrimination, calibration or downstream behavior.
Controls chosen for the proposed mechanism
Test key physical assumptions, modality use or pipeline components; include an independent reference when feasible.
When: Do not label a model-derived pseudo-label as independent truth or infer clinical benefit from a technical benchmark.
As appropriate to the deployment claim
State calibration, capture, annotation, compute and failure requirements.
When: A successful laboratory demonstration does not establish unrestricted field or clinical reliability.
Provide acquisition, calibration, annotation and participant/scene scope where relevant; retain material deployment limitations in the main paper.
Event vibrometry connects coarse motion to learned refinement and displacement, then states material/illumination conditions and identifies manufacturer moduli as its reference.
Stacked-object counting separates total-volume and average-instance-volume estimation, allowing branch errors to be distinguished from the final count.
Teeth reconstruction evaluates acquisition and tracking constraints in its limited participant setting rather than supplying clinical validation.
Boundary: This route contains 11 primary editorial assignments in the 64-paper inspected subset (64 inventory records). It is not an official track, a universal writing standard or evidence that these choices caused acceptance.
See inline examples of these artifact styles · Compare every member in the measurement ledger
A physically grounded acoustic reconstruction pipeline depends on measured room responses, calibrated visual inputs and domain-specific acoustic evaluation.
The main contribution converts an exposure-formation cue into physical camera velocity using a domain-specific differentiable solver and acquisition protocol.
A proactive identity-protection pipeline is evaluated under a task-specific source/target and model-access contract.
The contribution integrates domain-specific dental geometry, jaw attachment, visibility constraints and a practical phone acquisition protocol.
The core contribution adapts a frozen video prior to a vector-typography optimization workflow with task-specific readability constraints.
A task-specific text-to-brick pipeline combines sequence fine-tuning, physical validity checks and assembly-oriented generation.
The contribution integrates dataset sequencing, diffusion adaptation and conventional tracing into a layered SVG design workflow.
The pipeline combines learned restoration and latent optimization specifically to improve extraction from existing generative-watermark schemes.
A geometric-volume and learned-occupancy pipeline solves a distinct stacked-object counting setting with dedicated synthetic/real evaluation.
A hybrid event/RGB estimator is developed and tested specifically for visual vibration sensing and downstream physical measurements.
The contribution reorganizes automatic SAM prompting and mask post-processing specifically for class-agnostic entity segmentation.
