arXiv subjects

Kyungsu Kim

Publications and source records attributed to Kyungsu Kim.

At least 19 recordsLinked to original sources

arXiv2026

Edited Endpoints as Partial Goals for Image-to-Video Generation

Edited endpoint images make the target of an image-to-video transition explicit and can improve transition completion, but also preserve context in the source image's layout. Full-frame endpoint conditioning turns this preserved context into requirements on the video's final state, potentially inducing temporal artifacts. We measure discontinuity with Hard-Cut (HC), late background reversal with Background Motion Reversal (BMR), and motion suppression with Dynamic Degree (DD). These artifacts appear across five video models spanning training-free and learned endpoint conditioning, in model-specific forms. We study Partial, a selective conditioning scheme that constrains only an edit-relevant region estimated from image-editor attention and leaves the remaining terminal content to the video model. Across three backbones on TC-Bench-I2V, Partial reduces temporal artifacts relative to full-frame conditioning, with no significant difference in Transition Completion Ratio (TCR); on Wan2.2-Turbo, uniformly weakening conditioning does not reproduce these reductions. A donor-context intervention keeps full-frame conditioning and replaces only the context outside the edit-relevant region with the final frame of a Partial video. It lowers HC and BMR on HunyuanVideo-1.5 and lowers BMR while restoring DD on Wan2.2-Turbo, with no significant difference in TCR, so constraint extent alone does not account for these artifacts. Together, these findings suggest that edited endpoints should serve as partial future goals, although edit relevance does not guarantee future validity. Code and data will be released at https://github.com/wannagetacat/Edited-Endpoints-I2V.

cs.CV↗

arXiv2026

ReState: Noise-Level-Aware State Correction for Training-Free Object Removal in Few-Step Diffusion Models

Training-free object removal commonly redirects self-attention away from the target region, but on few-step distilled diffusion models this often leaves object-shaped residuals. We identify a temporal mismatch: a structured prediction forms inside the mask at the first forward, and the short trajectory offers few later chances to revise it, while attention rerouting acts mainly in later steps. Across four distilled backbones, magnitude-matched single-forward interventions show that background-directed state correction affects the final result more than attention rerouting. We therefore propose ReState, which corrects the masked clean prediction toward background evidence retrieved from the input using a noise-level-aware weight that vanishes at the final forward. ReState retains object-excluding attention as a complementary constraint and adds no denoiser evaluations. Across two paired benchmarks and two few-step backbones, ReState achieves the best masked PSNR, SSIM, and LPIPS among training-free methods, improving PSNR by 3.17 dB on average over the strongest baseline. On OBER-Test, it also outperforms the 50-step attention-forcing reference on all three metrics at 6.7x lower latency. The same configuration transfers to two additional distilled backbones without retuning. Project page: https://tdrlee.github.io/ReState

cs.CV↗

arXiv2026

Value-Informed Schedule Trimming for Accelerated Sequential Monte Carlo Guidance in Discrete Diffusion

Reward-guided discrete diffusion enables test-time alignment with downstream objectives, but applying Sequential Monte Carlo (SMC) guidance at every denoising step is computationally expensive. Active guidance steps require additional reward evaluations and, for gradient-based proposals, value-gradient computations. Sparse guidance reduces this cost by guiding only a subset of timesteps, but commonly used schedules are fixed heuristics whose effectiveness can vary substantially across tasks. We propose VISTA, a training-free method that automatically selects guided timesteps under a fixed budget. VISTA formulates sparse guidance as approximation of the ideal full-guidance probability path and derives an estimable upper bound on $\ell_1$ path deviation. Using value statistics collected from a small number of full-guidance warmup generations, VISTA optimizes the resulting empirical surrogate exactly via dynamic programming. The warmup generations can be retained as model outputs, and after schedule selection VISTA has the same per-generation guidance complexity as other sparse schedules with the same budget. Across text, biological sequence, and image generation tasks, VISTA discovers task-dependent schedules and outperforms hand-designed sparse schedules in most tested settings, while approaching or exceeding full-guidance performance on several task metrics. In toxic-text generation, for example, VISTA attains a 0.99 unique-toxic ratio compared with 1.00 for full guidance while reducing post-selection per-generation latency from 204.23 s to 38.16 s, a 5.35$\times$ speedup.

cs.AI↗

arXiv2026

LiveAnon: Improving Privacy and Utility in Streaming Speaker Anonymization with Codec Language Models

Speaker anonymization (SA) aims to conceal speaker identity while preserving linguistic content and emotion, but existing streaming systems often lose emotion information through discrete content bottlenecks or require emotion-specific modeling. We present LiveAnon, a voice-conversion-based streaming SA system that directly conditions an autoregressive codec language model on hidden features from a pretrained streaming ASR encoder, preserving emotion without emotion-specific supervision. To reduce codec-generation latency, we introduce capped RVQ decoding, which preserves dependencies among coarse codebooks while predicting the remaining codebooks in parallel. Under VPC24, LiveAnon improves intelligibility and emotion preservation over fixed-target Stream-Voice-Anon (SVA), a strong prior streaming SA system, matches emotion-prompted SVA in emotion preservation, and achieves the strongest semi-informed privacy among streaming systems. Under VPC26, it ranks second across all metrics among the evaluated anonymization systems, demonstrating a strong privacy-utility balance.

eess.AS↗

arXiv2026

Sori: Streamable and Controllable Model for Non-musical Audio to Symbolic Music Transformation

We introduce Non-musical Audio to Symbolic Music Transformation (NASMT), a novel task that converts non-musical audio into symbolic music representations. Unlike automatic music transcription, which assumes musical input and a unique correct score, NASMT is inherently one-to-many and requires evaluation along two axes: input correspondence and musicality. We present Sori, an encoder--decoder Transformer that addresses this task through domain adversarial training (DAT), which bridges the gap between musical and non-musical audio in the encoder's feature space, and classifier-free guidance (CFG) with a continuous parameter~$γ$ that controls the trade-off between correspondence and musicality at inference time. Across objective metrics and a listening study, Sori provides effective continuous control via $γ$ and compares favorably with instrument-agnostic transcription models. Sori supports real-time streaming on a consumer CPU. Audio examples are available at https://soridemo0320.github.io/sori-demo/.

cs.SD↗

arXiv2026

LiveSynth: A Streaming Neural Synthesizer for Instrument Cloning and Text-to-Instrument

Recently, generative neural synthesizers have enabled zero-shot instrument cloning from reference recordings and timbre generation from text prompts. However, existing systems generate complete audio segments offline, precluding their use as playable instruments. We present LiveSynth, a streaming neural synthesizer with flexible timbre control and interactive generation capabilities, including zero-shot instrument cloning and text-to-instrument synthesis. A feedback-free causal Transformer conditioned on MIDI events and a CLAP timbre embedding outputs continuous audio latents at 100 Hz, which a causal variational autoencoder decodes into audio. Frame-wise timbre conditioning enables continuous timbre morphing, while MIDI-conditioning dropout enables autonomous continuation from a performed prefix. On the NSynth and Slakh datasets, LiveSynth achieved timbre similarity and spectral reconstruction quality comparable to offline baselines. On an M3 MacBook Air, LiveSynth required 8.8 ms of computation per 10 ms audio frame (an RTF of 0.88) with 10 ms algorithmic latency, enabling real-time interaction with a performer.

cs.SD↗

arXiv2026

Depth-to-RGB: Repurposing a Frozen Depth Estimator for Geometry-Guided Compositing

Reference-based object compositing inserts or replaces an object using a background image, a reference image, and a 2D compositing mask. These inputs guide appearance and placement but leave the completed scene's geometry implicit, which can distort object structure or alter the surroundings. Our Depth-to-RGB (D2R) framework predicts composite depth for a scene not yet observed in the RGB inputs. It learns reference-conditioned corrections to a frozen depth estimator using encoder features of paired completed scenes as targets. The unchanged decoder maps the corrected representation to the intended scene's depth, which a separately trained renderer holds fixed during RGB synthesis. Under matched architecture and training, encoder-feature supervision reduces OOD Stage-1 AbsRel by 31.4% relative to decoded-depth supervision. We also introduce AnyInsertion++ with paired in-distribution and category-disjoint splits to evaluate generalization beyond compositing training categories. The complete D2R system leads 12 open-source and 3 closed-source baselines in estimator-derived geometry and photometric quality on both paired splits. On category-disjoint data, D2R reduces AbsRel by 43.7% and improves PSNR by 2.4 dB over the matched RGB baseline. Across three unpaired benchmarks, D2R leads both identity metrics and reduces mean CLIP reference cosine distance by 55% relative to the strongest baseline. Project page: https://shjo-april.github.io/Depth2RGB/

cs.CV↗

arXiv2026

Risk-Calibrated Proposal Transport for Finite-Particle Diffusion Steering

Inference-time steering combines pretrained diffusion experts or rewards without retraining by changing the dynamics that transport noise to data. Feynman-Kac correction compensates for proposal mismatch through importance-weighted sequential Monte Carlo (SMC), whose finite-particle behavior depends on the proposal. Variance-controlling guidance (VCG) improves that proposal by fitting a linear drift correction to minimize empirical log-weight-rate variance. Although its population optimum cannot worsen residual variance, finite-particle VCG can nearly eliminate its fitting residual while increasing residual risk on new states by orders of magnitude. The resulting update can degrade unweighted generation or accelerate particle collapse. We show that the centered Feynman-Kac rate is the normalized transport residual and that expected out-of-fit benefit is exactly population headroom minus coefficient-estimation penalty. Under regularity assumptions, a Wasserstein analysis bounds the unweighted proposal's terminal error using this residual. These results motivate Risk-Calibrated Proposal Transport (RCPT), which uses deletion leave-one-out residuals to calibrate the retained fraction of the VCG update, adding no model calls and only small linear-algebra overhead. Experiments on 2D checker distributions, scaffold decoration, molecular property optimization, and class-conditional CIFAR-10 generation demonstrate recovery from harmful fitted updates. Across molecular and image domains, RCPT mitigates harmful fitted updates and improves a broad range of terminal metrics relative to uncalibrated VCG.

stat.ML↗

arXiv2026

SurfSpec: Enhancing Off-Target-Agnostic Specificity by Bounding Pocket-Ligand Geometric Mismatch

Lead optimization in structure-based drug design aims to improve target binding while avoiding unintended interactions with off-target pockets. However, existing affinity-driven methods do not explicitly control specificity, whereas current specificity-aware approaches commonly require prior knowledge of off-target structures. We address off-target-agnostic specificity-aware lead optimization by analyzing the geometric mismatch between a ligand and the target pocket. We provide a conservative specificity lower bound for geometrically separated off-targets without requiring access to off-target structures. By metricizing pocket--ligand mismatch, the triangle inequality shows that reducing target--ligand mismatch improves a conservative lower bound on mismatch to a separated off-target class, which can be translated into a specificity lower bound through an empirical geometry--affinity calibration. Motivated by this analysis, we introduce SurfSpec, an off-target-agnostic lead optimization framework that iteratively grows ligands toward under-occupied regions of the target pocket surface. SurfSpec alternates between linker generation toward selected target-surface patches, which provides geometric pseudo-labels, and refinement under a pocket-conditioned ligand prior, which restores these pseudo-labels into valid ligands. On the CrossDocked2020 test set, SurfSpec reduces geometric mismatch and outperforms evaluated off-target-agnostic lead optimization baselines in empirical specificity, while maintaining competitive target-affinity improvement.

q-bio.QM↗

arXiv2026

PatchGate: Narrowing the Verbalization Gap with Intrinsic Object Inventories in Frozen Vision-Language Models

Reliable image captioning in Vision-Language Models (VLMs) requires captions to be both precise and complete, avoiding unsupported object mentions while covering visible objects. Existing training-free methods primarily address the former requirement, suppressing unsupported object words by intervening on model-predicted mentions during generation. Because they operate only on objects the model is already likely to mention, visible objects omitted from the output remain difficult to recover. We propose PatchGate, a training-free framework that extracts prompt-free object evidence intrinsic to a frozen VLM before generation and uses it to narrow the gap between an intrinsic object set and final object mentions. In the first stage, Visual Evidence eXtraction (VEX) reads patch-level lexical evidence from the latter half of LM decoder layers and constructs an image-conditioned object set without any task prompt. In the second stage, Visual-Evidence Inclusion-Exclusion Decoding (VIED) uses this object evidence to calibrate decoding logits, promoting evidence-supported but under-verbalized objects and suppressing weakly supported but over-verbalized objects. On AMBER, PatchGate improves both sides of object-level reliability, increasing visible-object coverage from 49.4 to 56.0 (+13.4%) and reducing object hallucination by lowering CHAIR from 7.5 to 6.6 (-12.0%), without external detectors or fine-tuning and with one extra forward pass.

cs.CV↗

arXiv2026

One Click per Cell Type Suffices: Training-free Group Interaction for Cell Instance Segmentation

Cell instance segmentation models trained on cell-specific datasets suffer severe performance drops on out-of-distribution cell types, while interactive foundation models overcome this through per-instance prompting at a cost that is prohibitively expensive for histopathology images containing hundreds to thousands of densely packed instances. We introduce \textbf{Group Prompting}, a new paradigm that shifts interactive segmentation from per-instance $O(N)$ to per-type $O(T)$, where a single click per cell type suffices to segment all instances of that type. Our key observation is that the frozen image encoder of the Segment Anything Model (SAM) already clusters same-type cells in its feature space before any prompt is given, and that this clustering holds across staining modalities without any training. Exploiting this property, we propose \textbf{Chain-of-Prompts (CoP)}, a training-free framework that recursively expands a single user click by (1) identifying reliable same-type locations through non-parametric gating of multi-scale encoder features, and (2) selecting the most spatially distant reliable point as the next prompt to maximize coverage. On eleven benchmarks, CoP generalizes to both unseen cell types and unseen imaging modalities without any adaptation: with one click per type it retains over 90\% of per-instance performance on three cell-type-annotated datasets while surpassing fully-supervised methods, and with one click per image it retains over 95\% on eight datasets spanning both H\&E and non-H\&E imaging. Project Page: https://shjo-april.github.io/Chain-of-Prompts/

cs.CV↗

arXiv2026

ISAC: Training-Free Instance-to-Semantic Attention Control for Multi-Instance Generation

Recent open-weight text-to-image (T2I) diffusion models still struggle with multi-instance prompts, often omitting or merging instances and mixing semantics among similar objects. We trace these failures to early denoising steps, before instance boundaries are reliably stabilized. Existing training-free guidance is largely driven by cross-attention or other token-conditioned semantic signals. Such guidance can separate concepts at the token level, but largely assumes that distinct instance regions have already emerged. In early denoising steps, it cannot reliably carve out these regions, so count failures and semantic mixing persist. By contrast, self-attention exposes class-agnostic instance layouts during early denoising. To exploit this asymmetry, we propose $\textbf{ISAC}$ ($\textbf{I}$nstance-to-$\textbf{S}$emantic $\textbf{A}$ttention $\textbf{C}$ontrol), a training-free, model-agnostic objective that first stabilizes self-attention layouts and then binds cross-attention semantics within them, without fine-tuning or external vision models. Across T2I-CompBench, HRS-Bench, and our newly curated IntraCompBench, ISAC consistently outperforms prior training-free methods. Furthermore, ISAC enhances layout-to-image controllers by refining coarse, overlapping bounding boxes into dense instance masks. Code and IntraCompBench are available at https://shjo-april.github.io/ISAC.

cs.CV↗

arXiv2026

EraseLoRA: MLLM-Driven Foreground Exclusion and Background Subtype Aggregation for Dataset-Free Object Removal

Object removal must prevent the masked target from reappearing and reconstruct the occluded background with structural and contextual fidelity, rather than merely filling a hole plausibly. Recent dataset-free approaches manipulate the diffusion model's internal self-attention to prevent it from referencing the masked region, yet they fail in two critical ways: (i) they treat the masked region as the sole foreground, misinterpreting non-target objects as background and regenerating them, and (ii) they apply uniform attention constraints without distinguishing diverse background subtypes, leading to textural blurring and structural misalignment. Both failures stem from the absence of explicit background-aware reasoning. We propose EraseLoRA, a dataset-free framework that replaces attention surgery with background-aware reasoning and test-time adaptation. The first stage, Background-aware Foreground Exclusion (BFE), leverages a multimodal large-language model to separate target foreground, non-target foregrounds, and clean background from a single image-mask pair. The second stage, Background-aware Reconstruction with Subtype Aggregation (BRSA), performs test-time optimization that treats inferred background subtypes as complementary pieces, enforcing their consistent integration through reconstruction and alignment objectives without explicit attention intervention. As a model-agnostic plug-in applicable to diverse diffusion backbones, EraseLoRA reconstructs backgrounds at least 23% more faithful to the original scene than previous dataset-free methods while nearly halving unwanted foreground re-generation, and surpasses all dataset-driven approaches in both aspects despite requiring no training data. Code is available at https://shjo-april.github.io/EraseLoRA.

cs.CV↗

arXiv2026

On the Collapse of Generative Paths: A Criterion and Correction for Diffusion Steering

Inference-time steering adapts pretrained diffusion and flow models to new tasks without retraining, often utilizing ratio-of-densities constructions that reweight time-indexed marginals with fixed exponents. We identify Marginal Path Collapse, a failure mode in which the intermediate density defined by such compositions becomes non-normalizable despite valid endpoints. This collapse can arise when composing heterogeneous experts trained with mismatched noise schedules (and/or negative exponents / partial supports). To address this, we provide (i) a sharp sufficient Path Existence Criterion that characterizes when the composed intermediate densities are mathematically well-defined, and (ii) Adaptive Path Correction with Exponents (ACE), which generalizes Feynman-Kac steering to support time-varying exponents. Our analysis reveals that ACE controls the quantile radius of the intermediate distributions, providing a theoretical mechanism for path stabilization observed in experiments. On flexible-pose scaffold decoration, a drug design task composed of de-novo, conformer, and protein-conditioned experts, ACE prevents collapse and significantly outperforms constant-exponent baselines. Furthermore, ACE improves attribute success rates in compositional image generation, establishing it as a general framework for compositional sampling. Project Page: https://ziseoklee.github.io/projects/ACE/

cs.AI↗

arXiv2026

TRACE: Your Diffusion Model is Secretly an Instance Edge Detector

High-quality instance and panoptic segmentation has traditionally relied on dense instance-level annotations such as masks, boxes, or points, which are costly, inconsistent, and difficult to scale. Unsupervised and weakly-supervised approaches reduce this burden but remain constrained by semantic backbone constraints and human bias, often producing merged or fragmented outputs. We present TRACE (TRAnsforming diffusion Cues to instance Edges), showing that text-to-image diffusion models secretly function as instance edge annotators. TRACE identifies the Instance Emergence Point (IEP) where object boundaries first appear in self-attention maps, extracts boundaries through Attention Boundary Divergence (ABDiv), and distills them into a lightweight one-step edge decoder. This design removes the need for per-image diffusion inversion, achieving 81x faster inference while producing sharper and more connected boundaries. On the COCO benchmark, TRACE improves unsupervised instance segmentation by +5.1 AP, and in tag-supervised panoptic segmentation it outperforms point-supervised baselines by +1.7 PQ without using any instance-level labels. These results reveal that diffusion models encode hidden instance boundary priors, and that decoding these signals offers a practical and scalable alternative to costly manual annotation. Project Page: https://shjo-april.github.io/TRACE/

cs.CV↗

arXiv2025

Explainable deep learning framework for cancer therapeutic target prioritization leveraging PPI centrality and node embeddings

We developed an explainable deep learning framework integrating protein-protein interaction (PPI) network centrality metrics with node embeddings for cancer therapeutic target prioritization. A high-confidence PPI network was constructed from STRING database interactions, computing six centrality metrics: degree, strength, betweenness, closeness, eigenvector centrality, and clustering coefficient. Node2Vec embeddings captured latent network topology. Combined features trained XGBoost and neural network classifiers using DepMap CRISPR essentiality scores as ground truth. Model interpretability was assessed through GradientSHAP analysis quantifying feature contributions. We developed a novel blended scoring approach combining model probability predictions with SHAP attribution magnitudes for enhanced gene prioritization. Our framework achieved state-of-the-art performance with AUROC of 0.930 and AUPRC of 0.656 for identifying the top 10\% most essential genes. GradientSHAP analysis revealed centrality measures contributed significantly to predictions, with degree centrality showing strongest correlation ($ρ$ = -0.357) with gene essentiality. The blended scoring approach created robust gene prioritization rankings, successfully identifying known essential genes including ribosomal proteins (RPS27A, RPS17, RPS6) and oncogenes (MYC). This study presents a human-based, combinatorial in silico framework successfully integrating network biology with explainable AI for therapeutic target discovery. The framework provides mechanistic transparency through feature attribution analysis while maintaining state-of-the-art predictive performance. Its reproducible design and reliance on human molecular datasets demonstrate a reduction-to-practice example of next-generation, animal-free modeling for cancer therapeutic target discovery and prioritization.

q-bio.QM↗

arXiv2025

COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentation

Cell instance segmentation (CIS) is crucial for identifying individual cell morphologies in histopathological images, providing valuable insights for biological and medical research. While unsupervised CIS (UCIS) models aim to reduce the heavy reliance on labor-intensive image annotations, they fail to accurately capture cell boundaries, causing missed detections and poor performance. Recognizing the absence of error-free instances as a key limitation, we present COIN (COnfidence score-guided INstance distillation), a novel annotation-free framework with three key steps: (1) Increasing the sensitivity for the presence of error-free instances via unsupervised semantic segmentation with optimal transport, leveraging its ability to discriminate spatially minor instances, (2) Instance-level confidence scoring to measure the consistency between model prediction and refined mask and identify highly confident instances, offering an alternative to ground truth annotations, and (3) Progressive expansion of confidence with recursive self-distillation. Extensive experiments across six datasets show COIN outperforming existing UCIS methods, even surpassing semi- and weakly-supervised approaches across all metrics on the MoNuSeg and TNBC datasets. The code is available at https://github.com/shjo-april/COIN.

cs.CV↗

arXiv2025

Early Timestep Zero-Shot Candidate Selection for Instruction-Guided Image Editing

Despite recent advances in diffusion models, achieving reliable image generation and editing remains challenging due to the inherent diversity induced by stochastic noise in the sampling process. Instruction-guided image editing with diffusion models offers user-friendly capabilities, yet editing failures, such as background distortion, frequently occur. Users often resort to trial and error, adjusting seeds or prompts to achieve satisfactory results, which is inefficient. While seed selection methods exist for Text-to-Image (T2I) generation, they depend on external verifiers, limiting applicability, and evaluating multiple seeds increases computational complexity. To address this, we first establish a multiple-seed-based image editing baseline using background consistency scores, achieving Best-of-N performance without supervision. Building on this, we introduce ELECT (Early-timestep Latent Evaluation for Candidate Selection), a zero-shot framework that selects reliable seeds by estimating background mismatches at early diffusion timesteps, identifying the seed that retains the background while modifying only the foreground. ELECT ranks seed candidates by a background inconsistency score, filtering unsuitable samples early based on background consistency while preserving editability. Beyond standalone seed selection, ELECT integrates into instruction-guided editing pipelines and extends to Multimodal Large-Language Models (MLLMs) for joint seed and prompt selection, further improving results when seed selection alone is insufficient. Experiments show that ELECT reduces computational costs (by 41 percent on average and up to 61 percent) while improving background consistency and instruction adherence, achieving around 40 percent success rates in previously failed cases - without any external supervision or training.

cs.CV↗