Computer Vision
Wenzhen Yuan, Jiacheng Ruan, Wutao Xiong et al. (+3 more)
We conduct a systematic study of 18 widely used
vision-
language benchmarks and identify three major issues: 1) many items do not rely on visual cues and therefore fail to effectively measure
multimodal understanding; 2) many items are already close to performance saturation for current LVLMs, which limits their discriminative power; 3) a small number of anomalous items affect the reliability of evaluation results. To this end, we propose MMGist, a curated
benchmark that covers seven capability dimensions and contains 7,262 items. MMGist is constructed through a three-stage pipeline, which sequentially combines text-ablation filtering, cross-model saturation filtering, and anomaly detection filtering. We conduct extensive experiments on 27 leading LVLMs and compare MMGist with the raw pool of 23,250 items. The results show that MMGist preserves model rankings with high fidelity, with Spearman $\rho = 0.98$, while reducing evaluation items by 69% and improving cross-model discrimination by 78%. Further results indicate that Visual Logic remains a systematic weakness of current LVLMs, while knowledge-intensive dimensions such as Expert Knowledge dimensions remain important factors for distinguishing closed-source models from open-source models. These findings suggest that high-quality evaluation should prioritize visual dependency, discriminative power, and reliability, rather than simply pursuing
benchmark scale.
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All authors Wenzhen Yuan, Jiacheng Ruan, Wutao Xiong, Chengping Zhao, Ting Liu, Yuzhuo Fu
Computer Vision
FlowDec: Temporal Conditional Flow Decorruptor for Robust Continuous Vision-Language Navigation
Yufei Zhang and Changhao Chen
Vision-and-
Language Navigation in Continuous Environments (VLN-CE) requires
agents to follow natural-
language instructions in unseen scenes. While Large Models (LMs) have advanced VLN-CE, their performance remains severely degraded by real-world visual corruptions, a critical yet underexplored domain constraint. We introduce Temporal Conditional Flow Decorruptor (FlowDec), a novel image restoration framework tailored for LM-based VLN-CE. FlowDec integrates a hybrid temporal conditioning strategy to align the generative flow path with historical context and employs action-centroid guided filtering to dynamically assess and integrate outputs. Extensive experiments demonstrate that FlowDec outperforms state-of-the-art decorruption methods in both
navigation accuracy and generation latency. Our approach establishes a robust, efficient paradigm for resilient
embodied navigation in unpredictable real-world conditions.
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Computer Vision
Gen2Balance: Generative Balancing for Long-Tailed Video Action Recognition
Prajwal Gatti, Simon Jenni, Fabian Caba Heilbron et al. (+1 more)
We address the problem of training on long-tailed data for
video action recognition. We propose to augment the training set using a text-to-
video generative model, conditioned on diverse text
prompts grounded in action profiles and training exemplars. Our approach, called Gen2Balance, converts an imbalanced training set into a balanced combination of real and generated
video clips. To effectively learn from such data, we employ a two-stage training strategy that mitigates domain shift and yields significant improvements. We evaluate on long-tailed versions of standard
benchmarks: UCF-101 (UCF-LT) and a 100-class subset of Kinetics (K100-LT) selected to prioritise temporally challenging actions. Gen2Balance improves accuracy over the strongest baselines for long-tailed learning by 5.1% and 7.0% on the respective
datasets. On rare actions from the RareAct
dataset (e.g., cut keyboard), Gen2Balance improves accuracy by 31.9%, demonstrating effectiveness for scarce actions. By varying the amount of synthetic data added, we show that partial balancing already achieves 79% of the performance gains at 27% of the compute cost on K100-LT, highlighting the practical scalability of Gen2Balance.
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All authors Prajwal Gatti, Simon Jenni, Fabian Caba Heilbron, Dima Damen
Computer Vision
Gold Points Sniper: Self-guided Visual Reasoning in VLM for Fine-grained Action Understanding
Haodi Liu, Xinhang Yang, Kunda Yan et al. (+3 more)
Robots operating in everyday environments must understand fine-grained human actions, intentions, and contextual cues from broad views where people occupy only small regions, a capability unmet by current systems. While open-vocabulary action recognition methods remain limited to assigning predefined labels, and
vision-
language models (VLMs) face an inherent trade-off between informational richness and factual fidelity in their outputs, neither approach achieves the deep semantic interpretation required for reliable human-
robot interaction. We propose Gold Points Sniper (GPS), a novel framework that empowers lightweight VLMs with self-guided
multimodal reasoning capabilities for fine-grained human action understanding. Our approach comprises three key modules: Gold Points Extractor trains VLMs to identify critical action-relevant details, Selective Socratic Questioner validates and refines these details through selective self-questioning, and Semantic Entailment Evaluator quantitatively assesses factual consistency using semantic entailment classification. Extensive experiments on our curated instruction-tuning
dataset based on the CAP
benchmark demonstrate that GPS-enhanced lightweight VLMs achieve substantial performance improvements, with some models reaching performance comparable to proprietary GPT-4o while maintaining superior factual accuracy. Our work establishes a reliable foundation for fine-grained action understanding in domestic robotics, enabling
robots to safely interpret human behavior through information-dense yet factually grounded descriptions. Source code, training configurations, annotation
prompts, and
dataset details are released at https://github.com/Haodi-Liu/GPS-Gold-Point-Sniper.
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All authors Haodi Liu, Xinhang Yang, Kunda Yan, Sen Cui, Zeyu Zhang, Changshui Zhang
Computer Vision
Multi-cancer detection using a computationally efficient CNN with transfer learning
Vasileios E. Papageorgiou, Georgios Petmezas, Dimitrios-Panagiotis Papageorgiou et al. (+2 more)
This study introduces a computationally efficient convolutional neural network (CNN) architecture enhanced with transfer learning for multi-cancer detection using biomedical images. The proposed lightweight CNN model is designed to reduce computational complexity while maintaining high classification performance, making it suitable for deployment in resource-constrained environments. We evaluate this approach on three distinct tumor
datasets comprising
brain magnetic resonance imaging (MRI) and lung and kidney computed
tomography (CT) scans. The model achieves test accuracy of 90.85 +- 2.22%, 98.64 +- 2.43% and 99.92 +- 0.08% for
brain, lung, and kidney cancer classification, respectively, using 5-fold stratified cross-validation (CV). Transfer learning is employed by pretraining the model on one cancer type and fine-tuning it on the others, requiring only 20 additional epochs to achieve performance comparable to models trained from scratch. The fine-tuning process involves updating the classification part of the CNN and requires approximately 0.014 seconds per image per epoch using an NVIDIA GeForce GTX 960. Comparative evaluations show that the proposed model outperforms several state-of-the-art pretrained architectures, such as Xception, VGG16, VGG19, MobileNetV2 and DenseNet121. Overall, the model's effectiveness is evaluated across three types of cancer with distinct morphological characteristics, assessing its performance on both MRI and CT imaging modalities and demonstrating robust performance across diverse tasks and data types. These findings underscore the potential of streamlined deep learning (DL) frameworks in accelerating cancer diagnosis without sacrificing accuracy, especially in settings with limited computational resources.
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All authors Vasileios E. Papageorgiou, Georgios Petmezas, Dimitrios-Panagiotis Papageorgiou, Leandros Stefanopoulos, Nicos Maglaveras
Computer Vision
Curvature-Adaptive Consistency Flow Matching: Autonomous Trajectory Optimization via Reinforcement Learning
Songtao Tian, Guhan Chen, Bohan Li et al. (+2 more)
Consistency
distillation has significantly accelerated
diffusion-model inference, but its sampling dynamics remain underexplored. We reveal an asymmetry: although Logit-Normal sampling priors work well for standard iterative generation, consistency
distillation exhibits a different difficulty profile (e.g., U-shaped), with optimization bottlenecks concentrated at the boundary stages rather than intermediate steps. To address the limitations of static sampling under evolving learning demands, we propose
Curvature-Adaptive Consistency Flow Matching (CACFM). By formulating
distillation as a dynamic decision process, CACFM uses a lightweight reinforcement learning
agent to probe Probability Flow ODE trajectories and construct an efficiency-oriented curriculum that prioritizes critical regions without manual scheduling. Combined with Flow-adapted DMD and
adversarial consistency objectives, our RL-based scheduler achieves state-of-the-art results on large-scale models such as FLUX and SDXL, mitigating structural deformities and preserving high-frequency details in extreme few-step regimes.
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All authors Songtao Tian, Guhan Chen, Bohan Li, Jingyi Ma, Zixiong Yu
Computer Vision
Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers
Edwin Kwadwo Tenagyei, Lei Wang, Ugochukwu Ejike Akpudo et al. (+2 more)
Parameter-efficient fine-tuning (PEFT) has become a practical solution for adapting large pretrained
vision transformers (ViTs) to downstream tasks while updating only a small subset of parameters. However, existing adapter-based methods perform adaptation independently for each
token, implicitly assuming that
token refinements should be learned in isolation. This
token-wise formulation overlooks the structured relationships among
tokens that naturally arise in visual scenes, potentially leading to redundant updates and spatially inconsistent feature refinement. In this work, we revisit the design of parameter-efficient adapters and propose to perform adaptation in hyperedge space rather than
token space. We introduce HyperAdapter, a hypergraph-based adapter architecture that enables structured, group-aware adaptation through soft
token routing. HyperAdapter constructs a soft hypergraph over ViT
tokens using prototype-based assignments, aggregates
token features into latent hyperedge representations, applies lightweight bottleneck adaptation at the hyperedge level, and diffuses the resulting updates back to
tokens via the hypergraph incidence structure. This design injects an explicit structural inductive bias into PEFT while preserving the modularity and efficiency of standard adapters. Extensive experiments across diverse visual
benchmarks demonstrate that structured hyperedge adaptation consistently outperforms strong PEFT baselines under comparable parameter budgets, with particularly pronounced gains on tasks requiring structured
reasoning. Our results suggest that the choice of adaptation space is a critical yet underexplored dimension in parameter-efficient transfer for ViTs.
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All authors Edwin Kwadwo Tenagyei, Lei Wang, Ugochukwu Ejike Akpudo, Jun Zhou, Yongsheng Gao
Computer Vision · Cross-listed from eess.IV
Large Language Model-Assisted Cleaning of Report-Derived Labels in a Large-Scale Chest CT Dataset
Yosuke Yamagishi, Atsushi Takamatsu, Mototsugu Sato et al. (+4 more)
Purpose: To evaluate whether large
language model (LLM)-assisted label cleaning can identify label-report discordance in CT-RATE, a large-scale public chest CT
dataset. Materials and Methods: After report-level deduplication, 24,446 unique radiology reports were identified. Twelve reports were excluded from the primary GPT-5.4 analysis because of Microsoft Azure AI Foundry content-safety filtering, leaving 24,434 reports and 439,812 label instances across 18 abnormality categories. GPT-5.4-derived binary labels were generated from report text using structured JSON output and compared with existing CT-RATE labels. Discordant instances were adjudicated by radiologists. In addition, 100 randomly sampled reports were manually annotated to compare CT-RATE labels, individual LLM-derived labels, and multi-LLM majority-vote labels against radiologist-annotated reference labels. Results: Overall agreement between GPT-5.4-derived and CT-RATE labels was 96.4%, with Cohen's kappa of 0.884. Lymphadenopathy showed the lowest agreement and kappa. In discordance review, radiologist adjudication supported GPT-5.4-derived labels in 72 of 97 (74.2%) general discordant instances and 91 of 99 (91.9%) targeted lymphadenopathy discordant instances. Against radiologist-annotated reference labels, multi-LLM majority-vote labels achieved the highest label-macro-averaged F1 score and Cohen's kappa. Conclusion: LLM-assisted label cleaning identified clinically meaningful label-report discordance in CT-RATE and may support scalable quality improvement of public imaging
datasets. The cleaned
dataset will be made publicly available to support future research.
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All authors Yosuke Yamagishi, Atsushi Takamatsu, Mototsugu Sato, Tomohiro Kikuchi, Shouhei Hanaoka, Takeharu Yoshikawa, Osamu Abe
Computer Vision · Cross-listed from cs.ET
Enhancing Road Safety: An IoT-Based Accident Detection and Prevention Mechanism
Prabhu Pugalenthi and Pramod Krishnaa Dhanbalan
Road traffic accidents remain a critical global crisis, consistently serving as a primary driver of preventable mortality and severe injury. These incidents are frequently precipitated by human error, including overspeeding, driving under the influence of alcohol, and cognitive fatigue. To address this urgent public safety challenge, this paper presents an intelligent, Internet of Things (IoT)-based Accident Prevention and Detection System (APDS) designed to systematically mitigate driver risk and optimize post-collision emergency responses. The proposed framework features a multi-tiered architecture capable of executing continuous real-time telemetry monitoring, proactive local alarm triggering, and automated situational intervention. Furthermore, the system integrates automated emergency communication protocols that aggregate immediate spatial coordinates via GPS and dispatch targeted alerts to medical facilities in close proximity, thereby optimizing response times and reducing accident-related fatalities.
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Computer Vision
Following the Flow: Advection-Consistent Modeling for Event-based Small Object Detection
Wen Guo, Fulong Cai and Wuzhou Quan
Event cameras enable high-frequency visual perception with microsecond latency, offering advantages for dynamic scenes. However, event-based small object detection remains challenging due to sparse asynchronous measurements and weak object responses that are easily disrupted by noise. Limited spatial support causes small-object signals to lose temporal continuity, resulting in fragmented and unstable predictions. To address this issue, we propose a physics-guided advection-consistent modeling framework, termed PACT, which formulates event
evolution as a motion-driven feature transport process. Instead of relying solely on local spatio-temporal aggregation, PACT propagates features along estimated velocity fields and enforces trajectory-level consistency through advection constraints. This design preserves weak event responses over time and prevents their degradation under complex background interference. Technically, PACT integrates motion-aware feature extraction with a differentiable advection-based transport operator, enabling coherent motion representation and effective noise suppression during temporal
evolution. Extensive experiments on
benchmark event-based
datasets demonstrate that PACT consistently outperforms state-of-the-art methods, achieving improvements of 20.72% in IoU and 15.03% in accuracy while maintaining comparable computational efficiency. The code is publicly available at https://github.com/fulongcai/PACT.
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Computer Vision · Cross-listed from eess.IV
ZeroGVC: Zero-Shot Generative Video Compression with Autoregressive Diffusion Priors
Yixin Gao, Xiaohan Pan, Lin Liu et al. (+3 more)
Recent generative
video compression methods leverage powerful generative priors to achieve perceptually pleasing reconstructions. However, most existing approaches require additional training to adapt generative models to produce realistic reconstructions from compact representations. In this paper, we propose ZeroGVC, a zero-shot generative
video compression framework that leverages pretrained autoregressive
diffusion priors for low-delay
video reconstruction. ZeroGVC encodes the first frame of each group of pictures (GOP) with an image codec and represents subsequent P-frames through Codebook-Guided Autoregressive Latent Compression. This design is motivated by our observation that the compression scheme of denoising
diffusion codebook models is effective in few-step consistency sampling. By selecting compact combinations of reproducible codebook noise vectors, ZeroGVC steers the latent denoising trajectory toward the target P-frame while allowing the decoder to reproduce the same trajectory in only a few denoising steps. In addition, we design an optional bidirectional reference mode that mitigates error propagation by leveraging the next I-frame context without introducing any additional bitrate overhead. Extensive experiments on standard
video compression
benchmarks demonstrate that ZeroGVC achieves superior perceptual reconstruction quality at ultra-low bitrates without any additional training.
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All authors Yixin Gao, Xiaohan Pan, Lin Liu, Xin Li, Zhibo Chen, Qi Tian
Computer Vision
Towards Error-Free Long Video Generation
Shuning Chang, Weihua Chen, Jiasheng Tang et al. (+8 more)
Recent advances in
video generation have made minute-level synthesis possible; however, generating long
videos remains challenging due to error accumulation, attribute drift, and the limited availability of long
video data. In this paper, we introduce an infinite-length
video generation framework that focusing on addressing these issues and produces high-quality, dynamic, and identity-consistent single-shot long
videos. We first finetune a
diffusion model as a
video extension model on large-scale short
video data to autoregressively generate temporally coherent clips. Inspired by the success of large
language models (LLMs), we adopt causal
attention computation between clips to further finetune this model on long
video data. In this way, the
tokens in one clip (short
video) are computed by bidirectional
attention while
tokens among clips are computed by unidirectional
attention. This design leverages the strengths of modern
diffusion models while preserving long-term context information, effectively mitigating error accumulation and attribute drift. To achieve
memory efficiency during inference, we adopt a key-value (KV) caching mechanism to maintain a constant KV
memory. Furthermore, we introduce truncation-rectified flow (T-RFlow) technique to further suppress error accumulation. Experimental results demonstrate the effectiveness of our method. Our framework establishes a new
benchmark for realistic and coherent minute-level
video synthesis.
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All authors Shuning Chang, Weihua Chen, Jiasheng Tang, Hao Xu, Zeyu Zhang, Hangjie Yuan, Yu Lu, Ruigang Niu, Fan Wang, Bohan Zhuang, Yi Yang
Computer Vision · Cross-listed from cs.CL
ORBIT: Training-Free Multi-Attribute Behavioral Steering via Orthogonal Subspace Rotation
Narges Ghasemi, Amir Ziashahabi, Salman Avestimehr et al. (+1 more)
Language models are widely used in assistant settings, where controlling behavioral attributes is often essential. Activation steering modifies hidden-state representations at inference time, providing a lightweight, training-free mechanism that can be toggled at runtime. Existing methods, however, have focused primarily on steering a single attribute at a time. When multiple attributes must be controlled simultaneously, naive summation of per-attribute steering vectors suffers from norm imbalance and directional cancellation, while classifier-based approaches require retraining whenever the attribute set changes. We introduce ORBIT (Orthogonal Rotation-Based Intervention Technique), a training-free extension of rotation-based steering to the multi-attribute setting. Our method constructs a joint subspace from per-attribute steering planes via singular value decomposition and applies a single norm-preserving rotation within that subspace toward a combined target direction. Adaptive per-
token gating identifies which attributes need correction at each position, and an optional additive boost strengthens attributes with weak initial projection. We also introduce TraitFactory, a new multi-attribute
benchmark that focuses on behavioral tendencies rather than surface-level style. We evaluate ORBIT on TraitFactory and ToneBank across three models (Llama-3.2-3B, Qwen-2.5-7B, Llama-3.1-8B) while steering multiple attributes simultaneously, showing that it achieves stronger and more balanced multi-attribute steering than existing training-free baselines while better preserving output coherence.
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All authors Narges Ghasemi, Amir Ziashahabi, Salman Avestimehr, Jonathan May
Computer Vision
Interest Entanglement: The Hidden Barrier to Blind Super-Resolution Optimization
Junxiong Lin, Xinji Mai, Qianyu Guo et al. (+5 more)
Fidelity and perceptual quality are two inherently competing and conflicting objectives in the image super-resolution (SR) task. Different loss functions focus on these objectives to varying extents. Regression losses enhance the model's fidelity but lack sufficient
attention to high-frequency details, resulting in a loss of fine details. In contrast, perception losses improve the model's visual quality but may introduce undesirable artifacts. Balancing these two optimization goals can be viewed as a Multi-Objective Optimization problem. Existing methods are limited to cautiously adjusting weight parameters between these losses, overlooking the underlying Interest
Entanglement problem. To address this problem, we explore the inherent frequency-domain conflict between the regression objective and the perceptual objective, and analyze the causes of Interest
Entanglement in SR tasks. According to our findings, we propose the Shared-Feature-Representation based Super-Resolution framework (SFR), which decouples the learning process of different optimization objectives, allowing the model to explore a common optimization direction for both goals and achieve an effective balance between them. To better leverage shared features, we also proposed the InfoSqueeze module, which filters redundant information through a dimensionality reduction and expansion process, effectively transforming features into a consistent space. Quantitative and qualitative experiments across five representative
datasets affirm the superiority of SFR.
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All authors Junxiong Lin, Xinji Mai, Qianyu Guo, Haoran Wang, Zeng Tao, Xuan Tong, Ivy Pan, Wenqiang Zhang
Computer Vision · Cross-listed from cs.LG
Reliability-Guided Adaptive Ensembling for Robust Test-Time Adaptation
Adam Koziak and Yuhong Guo
Test-time adaptation (TTA) can mitigate domain shift without source data, but it is highly brittle under adversarially contaminated test streams, where corrupted inputs also destabilize online updates. We study robust test-time adaptation (RTTA) in the
adversarial-stream setting, which remains comparatively underexplored relative to standard TTA, and propose SAFER (
Stochastic Augmentation Framework for Enhanced Robustness), a training-free reliability-guided augmentation wrapper for RTTA. SAFER preserves the wrapped TTA objective while replacing brittle single-view predictions with a reliability-guided pooled predictor. For each test sample, SAFER generates
stochastic augmentations and aggregates their predictions through correlation-weighted pooling with outlier detection. We further study an adaptive-mixing extension that improves clean-performance retention by adjusting original-versus-augmentation weighting using feature disagreement signals. We evaluate on PACS, VLCS, and OfficeHome under PGD attacks at various attack rates. Across
benchmarks, SAFER improves resilience of TTA methods to
adversarial attacks while maintaining competitive clean performance.
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Computer Vision
Customizing Video Portraits via Identity-ActionDecoupling
Junxiong Lin, Haoran Wang, Xinji Mai et al. (+4 more)
Identity-Preserving Text-to-
Video Generation (IPT2V) seeks to synthesize a temporally coherent
video from a reference image and a textual description, while simultaneously preserving the subject's identity and allowing fine-grained control over facial dynamics. Although recent methods such as ID-Animator and ConsisID inject identity features only at inference time, they ignored the ID-irrelevant information contained in Facial
embedding, leading to monotonous or inaccurate facial movements that poorly follow the
prompt. We introduce Identity-Action Decoupling (IaD) framework as well as two loss function Identity Decoupling Loss and Text
Alignment Loss to solve this problem. Without any subject-specific fine-tuning, IaD yields
videos that (1) maintain cross-temporal identity consistency and (2) exhibit rich, controllable expressions and scene variations that closely match the input text.
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All authors Junxiong Lin, Haoran Wang, Xinji Mai, Zeng Tao, Xuan Tong, Ivy Pan, Wenqiang Zhang
Computer Vision
T-IMPACT: A Severity-Aware Benchmark for Contextual Image-Text Manipulation
Gagandeep Singh, Aaditya Yadav and Priyanka Singh
Recent advances in
vision-
language models and generative editing systems have made it increasingly easy to produce persuasive
multimodal misinformation by altering images, text, or both jointly. However, existing
datasets focus mainly on authenticity, out-of-context mismatch, or manipulation type, and rarely capture how strongly an edit changes the likely interpretation of a post. We introduce T-IMPACT, a first-release severity-aware
benchmark for manipulated news-style image-text pairs. T-IMPACT contains 98,786 examples spanning pristine, image-only, text-only, and joint manipulations, with a calibrated continuous severity signal, coarse low/medium/high labels, and supporting grounding metadata. Starting from a news image-text pair, the pipeline extracts semantic anchors, grounds them spatially, performs localized image edits and constrained caption rewrites, and calibrates contextual-impact scores using limited human ratings. In this release, the calibrated continuous score is the primary severity target, while the low/medium/high bands should be interpreted as coarse operating buckets rather than balanced classes. Experiments show that current models recover some authenticity signal, but severity prediction remains substantially harder and only weakly aligned with human judgment. T-IMPACT provides an initial
benchmark for studying
multimodal manipulation beyond binary real/fake classification toward graded contextual impact.
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Computer Vision · Cross-listed from cs.RO
EmbodiedUS-FS: Fast Slow Intelligence for Ultrasound Robotics
Fangzhuo Zhang, Xinyu Wang, Xiao Yang et al. (+1 more)
Robotic ultrasound scanning in real clinical environments requires both high-level clinical workflow
reasoning and low-level closed-loop execution. Physicians natural-
language instructions often contain implicit anatomical targets, procedural logic, image-quality requirements, and safety constraints, while execution is affected by patient motion, contact variations, and target drift. We propose a fast and slow hierarchical
embodied ultrasound system for safe and interpretable robotic ultrasound assistance. The Slow
Brain performs intent parsing and stage-wise task planning with knowledge augmentation from an API and handbook corpus, and generates executable plans through task-graph construction and structured plan verification. The Fast
Brain fuses
multimodal feedback, including ultrasound images,
robot pose and force states, and patient-motion information, to refine local actions and perform image-quality-guided recovery behaviors. The system further integrates a Safety Shield and a hierarchical escalation policy to constrain risky actions and trigger replanning or human confirmation under persistent failures or safety-bound violations. Experiments on planning evaluation, closed-loop execution under dynamic perturbations, and safety-mechanism validation demonstrate that the proposed hierarchical design improves task success rates while reducing safety violations.
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All authors Fangzhuo Zhang, Xinyu Wang, Xiao Yang, Jinchang Zhang
Computer Vision · Cross-listed from cs.LG
Diffusion Integrated Gradients: Controllable Path Generation for Flexible Feature Attribution
Soyeon Kim, Kyowoon Lee and Jaesik Choi
Path-based attribution methods such as Integrated Gradients (IG) are widely adopted for their strong axiomatic properties and effectiveness in attributing model predictions to input features by integrating gradients along a path from a baseline to the input. However, the choice of the attribution path largely affects the quality of explanations, and existing approaches rely on fixed or hand-crafted paths that often produce noisy or distorted attributions. To address this limitation, we propose
Diffusion Integrated Gradients (DiffIG), a novel method that reformulates path generation as a conditional generative modeling problem. DiffIG first trains a
diffusion model to learn a distribution over paths generated from a Stick-Breaking Process, then employs guided sampling to embed user guidance during the sampling procedure. We demonstrate that DiffIG quantitatively matches or outperforms existing path-based methods, achieving perceptually aligned explanations. This work introduces a new generative perspective for flexible, inference-time controllable Explainable Artificial Intelligence (XAI) methods.
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Computer Vision · Cross-listed from eess.IV
Specificity- and Calibration-Aware Breast Ultrasound Segmentation via Entropy-Guided Boundary Supervision
Manar Alsaid, Mandip Shrestha and Mohammad Abbas
Lesion
segmentation in breast ultrasound involves two related challenges. In images with lesions, speckle noise, low tissue contrast, and posterior acoustic shadowing cause boundary leakage and incomplete contour delineation. In images without lesions, those same artifacts generate false-positive activations in regions resembling solid lesion tissue. This study addresses both failure modes through a single modification to the training objective. Rather than weighting every boundary pixel equally, the proposed loss scales contour penalties by per-pixel predictive
entropy and the ground-truth boundary map, concentrating gradient emphasis on lesion margin locations where the network remains uncertain. The loss was evaluated on the BUSI
dataset through a controlled ablation against two baselines: a model without boundary supervision and a model with uniformly weighted boundary binary cross-
entropy. Across 97 lesion-containing test images, mean Dice scores were statistically indistinguishable between the proposed method and the no-boundary baseline (0.7624 versus 0.7616, paired Wilcoxon p = 0.27), confirming that lesion
segmentation quality is preserved. The primary effect appears in specificity. False-positive activations on 20 no-lesion test images fell from 14 of 20 and 19 of 20 for the two baselines to 5 of 20 with the proposed approach (McNemar p = 0.012 and 0.0005). Non-overlapping Wilson 95% confidence intervals confirm the difference is both statistically significant and practically substantial. A post-hoc spatial temperature scaling step further reduced expected calibration error from 0.0201 to 0.0095 without altering
segmentation masks.
Entropy-guided boundary supervision and spatial calibration thus function as complementary training-level and inference-level refinements that improve specificity and probability reliability within a U-Net framework.
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Computer Vision
Towards Accurate and Robust Surveillance Roadside IVD via Trackletized Audio-Visual Reasoning
Xiwen Li, Xiaoya Tang, Bodong Zhang et al. (+1 more)
Idling Vehicle Detection (IVD) seeks to determine, at the final frame of a
video clip, whether any vehicle is idling, meaning the vehicle is stationary with its engine running, using synchronized
video from a remote surveillance camera and multichannel audio captured by spatially distributed wireless microphones along the roadside. Prior full-image, clip-level fusion approaches tend to overfit scene background and full-frame context, produce unstable temporal decisions, and lack an explicit spatial prior to align vehicles with microphones, which makes them brittle under domain shift and data inefficient. Instead, we introduce TAVR-IVD, an audio-visual framework guided by multi-object tracking. Our method detects vehicles, links detections into tracklets, and classifies each vehicle by operating on its tracklet. This design raises the effective signal-to-noise ratio, stabilizes temporal decisions through tracklets, enforces an explicit spatial prior to align vehicles with microphones, and adapts across domains with limited calibration annotations while remaining detector agnostic and efficient. To evaluate deployment robustness, we further curate two evaluation extensions, AVIVD-LT and AVIVD-M, covering inter-day and cross-site shifts.
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All authors Xiwen Li, Xiaoya Tang, Bodong Zhang, Tolga Tasdizen
Computer Vision
Efficient Document Tampering Localization with Multi-Level Discrepancy Features and Unified DCT-Quantization Embedding
Mohamed Dhouib, Ye Zhu, Sonia Vanier et al. (+1 more)
Localizing document tampering is extremely challenging, as manipulations are crafted to appear visually consistent and often leave only subtle traces that are nearly invisible to the human eye. In prior work, evaluation has been largely dominated by synthetic
benchmarks that closely match the training distribution, and methods have shown steady progress under this setting. However, these gains often translate poorly to human-made forgeries and to cross-domain evaluation, where both the source documents and the tampering pipeline can change, leading to a distribution shift. In addition, since the introduction of the Frequency Perception Head for the discrete cosine transform (DCT) modality, it has become a standard choice, and subsequent work has largely focused on downstream modules and fusion strategies rather than revisiting the backbone itself. To help close this gap in cross-domain performance and improve the DCT backbone design, we propose DiffNet, a relatively simple yet effective RGB–DCT early-fusion architecture driven by two key design choices. First, to ensure that the decoder aggregates multi-scale inconsistency evidence rather than operating on raw, content-heavy activations, we apply a lightweight multi-level discrepancy transformation at the output of each backbone stage, replacing features with magnitude-only responses to learned zero-sum filters. Second, we design an efficient DCT-domain backbone that relies on a lightweight frequency-index-aware DCT–quantization joint
embedding. Our approach achieves state-of-the-art performance on cross-domain and human-made document tampering localization, outperforming prior methods by around 30%, with up to $7\times$ higher throughput than the previous best model.
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All authors Mohamed Dhouib, Ye Zhu, Sonia Vanier, Aymen Shabou