Computer Vision
Certified vs. Empirical Adversarial Robust-ness via Hybrid Convolutions with Attention Stochasticity
Joy Dhar, Song Xia, Manish Kumar Pandey et al. (+5 more)
We introduce Hybrid Convolutions with
Attention Stochasticity (HyCAS), an
adversarial defense that narrows the long-standing gap between provable robustness under L2 certificates and empirical robustness against strong L attacks, while preserving strong generalization across diverse imaging
benchmarks. HyCAS unifies deterministic and randomized principles by coupling 1-Lipschitz, spectrally normalized convolutions with two
stochastic components, spectral normalized random, projection filters and a randomized
attention-noise mechanism, to realize a randomized defense. Injecting smoothing randomness inside the architecture yields an overall <= 2-Lipschitz network with formal certificates. Exten-sive experiments on diverse imaging
benchmarks, including CIFAR-10/100, ImageNet-1k, NIH Chest X-ray, HAM10000, show that HyCAS surpasses prior leading certified and empirical defenses, boosting certified accuracy by up to 7.3% (on NIH Chest X-ray) and empirical robustness by up to 3.1% (on HAM10000), without sacrificing clean accuracy. These results show that a randomized Lipschitz constrained architecture can simultaneously improve both certified L2 and empirical L
adversarial robustness, thereby supporting safer deployment of deep models in high-stakes applications. Code: https://github.com/misti1203/HyCAS
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All authors Joy Dhar, Song Xia, Manish Kumar Pandey, Maryam Haghighat, Azadeh Alavi, Ferdous Sohel, Wenyu Zhang, Nayyar Zaidi
Computer Vision
VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animation
Guotao Liang, Zhangcheng Wang, Chuang Wang et al. (+6 more)
Scalable Vector Graphics (SVG) animation generation is pivotal for professional design due to their structural editability and resolution independence. However, this task remains challenging as it requires bridging discrete code representations with continuous visual dynamics. Existing optimization-based methods often destroy topological consistency, while general-purpose LLMs rely on rigid CSS/SMIL transformations, failing to model geometry-level non-rigid deformations. To address these limitations, we present VAnim, the first LLM-based framework for open-domain text-to-SVG animation. We reconceptualize animation not as sequence generation, but as Sparse State Updates (SSU) on a persistent SVG DOM tree. This paradigm compresses sequence length by over 9.8x while preserving the SVG DOM structure and non-participating elements by construction. To enable precise control, we propose an Identification-First Motion Planning mechanism that grounds textual instructions in explicit visual entities. Furthermore, to overcome the non-differentiable nature of SVG rendering, we employ Rendering-Aware Reinforcement Learning via Group Relative Policy Optimization (GRPO). By leveraging a hybrid reward from a state-of-the-art
video perception encoder, we align discrete code updates with high-fidelity visual feedback. We also introduce SVGAnim-134k, the first
benchmark for vector animation. Extensive experiments demonstrate that VAnim significantly outperforms state-of-the-art baselines in semantic
alignment and structural validity, with additional appendix metrics further validating motion quality and identity preservation.
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All authors Guotao Liang, Zhangcheng Wang, Chuang Wang, Juncheng Hu, Haitao Zhou, Junhua Liu, Jing Zhang, Dong Xu, Qian Yu
Computer Vision
Two-Pass Zero-Shot Temporal-Spatial Grounding of Rare Traffic Events in Surveillance Video
Jiantang Huang
Grounding traffic accidents in real CCTV footage is a rare-event problem where training on labeled accident
video is often prohibited, yet accurate joint localization in time, space, and collision type is required. We present a no-fine-tuning pipeline that elicits this joint output from frozen
vision-
language models through two ideas. First, a coarse-to-fine two-pass decomposition: a full-
video pass at 1 fps produces a coarse (t, x, y, c) tuple, then a second pass at 5 fps within a +/- 3 s window refines time and location, with two deterministic confidence gates that revert to the coarse estimate on boundary hedges or edge-clamped coordinates. Second, a specialist role assignment: Qwen3-VL-Plus handles grounding, Gemini 3.1 Flash-Lite handles typing on a centered
video clip. On the ACCIDENT@CVPR 2026
benchmark (2,027 real CCTV
videos) we reach ACC^S = 0.539 (95% CI [0.525, 0.553]): +0.127 over the
benchmark paper's best-of-baselines oracle (0.412), +0.143 over the strongest single-VLM baseline (Molmo-7B, 0.396), and +0.250 over the naive baseline (0.289). The VLM path uses up to three API calls per
video (17% fall back to physics on API failures); the full run costs ~$20.
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Computer Vision
SwiftPie: Lightning-fast Subject-driven Image Personalization via One step Diffusion
Huy Duong, Trong-Tung Nguyen, Cuong Pham et al. (+3 more)
Diffusion models have achieved remarkable success in high-quality image synthesis, sparking interest in image-guided generation tasks such as subject-driven image personalization. Despite their impressive personalization results, existing methods typically rely on computationally intensive fine-tuning, iterative optimization, or multi-step denoising processes, which significantly hinder their deployment and interactive capability in real-time applications. In this work, we present SwiftPie, the first one-step
diffusion image personalization tool that enables lightning-fast generation of personalized images. SwiftPie introduces a novel dual-branch identity injection mechanism that effectively integrates subject identity into a one-step
diffusion model. In addition, we incorporate a mask-guided rescaling strategy to further enhance subject contextualization within a single
diffusion step. Extensive experiments demonstrate that SwiftPie not only delivers superior image personalization speed but also achieves comparable performance with multi-step approaches in both identity fidelity and
prompt alignment. This work opens new opportunities for real-time, high-quality personalized image generation, paving the way for interactive visual synthesis.
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All authors Huy Duong, Trong-Tung Nguyen, Cuong Pham, Anh Tran, Khoi Nguyen, Minh Hoai
Computer Vision
OmniEncoder: See, Hear, and Feel Continuous Motion Like Humans With One Encoder
Detao Bai, Shimin Yao, Weixuan Chen et al. (+4 more)
Recent advances in omni-modal large
language models have enabled remarkable progress in joint
vision-audio understanding. However, prevailing architectures rely on modality-specific encoders with a
video-coarse, audio-dense design – sampling visual frames at 1–2 fps while processing audio waveforms at 25 fps – resulting in systems that perceive
video frame by frame, modality by modality rather than holistically as humans do. Such a discrepancy leaves models with impoverished cross-modal interaction during encoding and an inability to capture fine-grained visual motion. To bridge this gap, we present Omni-Encoder, a unified
Transformer backbone designed to co-embed visual and audio signals at a symmetrical 25 fps within a shared latent space. This architecture leverages three core innovations – the Omni-Encoder
Token Template, Omni-RoPE, and Temporal Window Shifting – to effectively reconcile the dual challenges of modality disentanglement and computational efficiency. Experiments demonstrate that, compared to the modality-specific baseline Qwen2.5-Omni under the same input
token budget to the LLM decoder, Omni-Encoder delivers substantial gains on visual continuous understanding tasks – such as sign
language recognition and fine-grained sports action analysis – while maintaining competitive performance on established audio-visual
benchmarks such as AVQA and Speaker Identification and Localization. These results suggest that unified omnivorous encoding offers a promising direction for building omni-modal models that more closely reflect the integrated nature of human perception.
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All authors Detao Bai, Shimin Yao, Weixuan Chen, Chengen Lai, Yuanming Li, Zhiheng Ma, Xihan Wei
Computer Vision
RADMI: Latent Information Aggregation as a Proxy for Model Uncertainty
William Stevens, Mohit Prabhushankar and Ghassan AlRegib
Epistemic uncertainty estimation is essential for identifying regions where deep learning system outputs may be unreliable. However, existing approaches require computationally expensive ensemble methods or multiple
stochastic forward passes, limiting their scalability to dense prediction tasks like
segmentation. We propose Resolution-Aggregated Decoder Mutual Information (RADMI), a single-pass method that estimates prediction uncertainty by measuring mutual information (MI) between consecutive decoder layers in
segmentation networks. We observe that elevated inter-layer MI correlates with prediction uncertainty, as the network must integrate conflicting contextual information at ambiguous regions such as class boundaries. Evaluating on a seismic facies
segmentation benchmark, RADMI achieves the highest correlation with deep ensemble uncertainty among all single-pass methods, outperforming the next-best baselines by 5.5% in Pearson and 10.7% in Spearman correlation coefficients. Compared to baselines that either lack spatial precision or demand significant computational overhead, RADMI yields sharp, boundary-localized uncertainty maps without architectural modifications. Our results suggest that linear aggregation of normalized information flow provides a principled and efficient proxy for prediction uncertainty in encoder-decoder architectures.
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Computer Vision
Towards Visual Query Localization in the 3D World
Liang Peng, Bohan Tan, Zhipeng Zhang et al. (+4 more)
Visual query localization (VQL) aims to predict the spatio-temporal response of the most recent occurrence in a sequence given a query. Currently, most research focuses on visual query localization in 2D
videos, while its counterpart in 3D space has received little
attention. In this paper, we make the first attempt to address visual query localization in the 3D world by introducing a novel
benchmark, dubbed 3DVQL. Specifically, 3DVQL contains 2,002 sequences with around 170,000 frames and 6.4K response track segments from 38 object categories. Each sequence in 3DVQL is provided with multiple modalities, including point clouds, RGB images, and depth images, to support flexible research. To ensure high-quality annotations, each sequence is manually annotated with multiple rounds of verification and refinement. To the best of our knowledge, 3DVQL is the first
benchmark for 3D
multimodal visual query localization. To facilitate comparison in subsequent research, we implement a series of representative 3D
multimodal VQL baselines using point clouds and RGB images. The experimental results show that existing methods exhibit significant performance variations across different fusion modules. To encourage future research, we propose a lift-and-
attention fusion
algorithm named LaF, which significantly outperforms existing baseline models. Our
benchmark and model will be publicly released at https://github.com/wuhengliangliang/3DVQL.
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All authors Liang Peng, Bohan Tan, Zhipeng Zhang, Haobo Li, Yifan Jiao, Xingping Dong, Libo Zhang
Computer Vision
SF20K Competition 2025: Summary and findings
Ridouane Ghermi, Xi Wang, Vicky Kalogeiton et al. (+1 more)
This report presents the results and findings of the first edition of the Short-Films 20K (SF20K) Competition, held in conjunction with the SLoMO Workshop at ICCV 2025. The competition is designed to advance story-level
video understanding beyond short-clip action recognition, introducing an open-ended
video question-answering task built on a corpus of amateur short films. This setup ensures that models must rely on
multimodal understanding rather than memorization of popular movies. Evaluation is conducted using the SF20K-Test
benchmark (95 movies, 979 question-answer pairs) and scored via LLM-QA-Eval, an automated judge based on GPT-4.1-nano. The competition attracted 22 teams and 286 submissions across two tracks: a Main Track with unrestricted model size and a Special Track limited to models under 8 billion parameters. The winning team achieved 65.7% accuracy on the Main Track and 48.7% on the Special Track, against a human performance ceiling of 91.7%. Our analysis reveals several key findings: narrative-aware, shot-level processing consistently outperforms uniform frame sampling; well-designed multi-stage pipelines using smaller models can match or exceed end-to-end inference with models over 30x larger; and subtitle quality is a dominant factor in performance. These results highlight that the primary bottleneck in long-form
video QA lies in information selection and
reasoning structure rather than raw model capacity, and that a substantial gap remains between current methods and human-level narrative comprehension.
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All authors Ridouane Ghermi, Xi Wang, Vicky Kalogeiton, Ivan Laptev
Computer Vision
CGFformer: Cluster-Guidance Frequency Transformer for Pansharpening
Zijian Zhou, Jianing Zhang, Kai Sun et al. (+3 more)
Pansharpening aims to generate high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) images with high-resolution panchromatic (PAN) images. However, the current mainstream frequency-based pansharpening methods employ fixed frequency filters, which cannot precisely adapt to complex and spatially diversified frequency distributions in PAN and MS images. Furthermore, existing denoising strategies insufficiently exploit frequency components for denoising and struggle to suppress various noise types accurately. To address these challenges, we propose CGFformer, a cluster-guidance frequency
Transformer that focuses on varying frequency distribution and interactions between frequency and spatial components. Specifically, we design an adaptive separation module that integrates local features and non-local information through K-means clustering, enabling more precise separation of high- and low-frequency components. Subsequently, we introduce a dual-stream refinement module combined with
Transformer-based cross-
attention to remove various noise, allowing the network to jointly suppress frequency-relevant and irrelevant disturbances. In addition, we develop a frequency-spatial fusion module designed to enhance detail and facilitate spatial-frequency interaction, ensuring more effective reconstruction of spatial structures in the fused results. Extensive experiments on multiple
benchmark datasets demonstrate that the proposed CGFformer achieves notable improvements over existing pansharpening approaches.
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All authors Zijian Zhou, Jianing Zhang, Kai Sun, Xiangyu Zhao, Chunxia Zhang, Xiangyong Cao
Computer Vision
Research on Vision-Language Question Answering Models for Industrial Robots
Ping Li and Bartlomiej Brzozka
A hierarchical cross-modal fusion model is proposed for
vision-
language question answering (VLQA) in industrial robotics, targeting the challenges of semantic ambiguity, complex environmental layouts, and domain-specific terminology common in modern manufacturing. The framework integrates advanced object detection, multi-scale visual encoding, syntactic parsing, and task-aware semantic
attention to unite
vision and
language signals into a joint
reasoning space. Region-based deep networks extract visual features, weighted
embeddings aggregate, and recurrent neural parsing encodes sentence structures. Through fine-grained semantic
alignment driven by adaptive fusion and cross-
attention mechanisms, the system can handle operational queries, instruction steps, and anomaly detection with higher reliability. Compared to the existing VLQA
benchmarks, validation experiments conducted on the IVQA and RIF
benchmarks indicate improvements in semantic
alignment, Top-1 accuracy, and robustness to ambiguous or procedural task queries. Ablation studies further quantify the impact of each architectural module, confirming the necessity of multi-level feature integration and context-driven gating for dependable industrial deployment. The technical advancements reported here provide core methodologies to improve the interpretability and operational effectiveness of industrial
robots faced with diverse human-
robot interaction tasks.
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Computer Vision
AttnRouter: Per-Category Attention Routing for Training-Free Image Editing on MMDiT
Guandong Li and Mengxia Ye
We study training-free image editing on Qwen-Image-Edit-2511, a 60-block multi-modal
diffusion transformer (MMDiT) that concatenates noise and source-image
tokens within a single
attention stream. We make three contributions. (i) We introduce KVInject, a single-forward
attention manipulation that alpha-blends source-half key/value projections into the noise-half within a localized layer/step band. KVInject is simpler than the classical two-pass MasaCtrl recipe and avoids the
prompt-mismatch failure mode that disables MasaCtrl on MMDiT (composite score drops 31% versus baseline). (ii) We show that no single
attention operation dominates across edit types, motivating AttnRouter, a per-category routing table that dispatches edits to the operation that best preserves source structure for that type. With ground-truth categories the router improves the CLIP-T+DINO-I composite by 6.4% over the editing baseline; an automatic CLIP zero-shot classifier closes 98% of this gap despite only 55% category accuracy. (iii) Through layer-, step-, and alpha-band ablations we localize the editing-effective
attention sub-
circuit: K/V injection in early denoising steps (S0-7) recovers nearly all of the gain of full-step injection, while injection in early (L0-15) or late (L45-60) layer bands fails to drive editing entirely; alpha in [0.3, 0.5] is a stable sweet spot. We also report negative results that highlight what does not transfer from the UNet folklore: simple K/V rescaling never beats baseline and aggressive variants collapse generation entirely (composite 0.084). We release code, pre-computed routing tables, and a 100-sample stratified subset of ImgEdit-Bench used in all ablations.
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Computer Vision
CSGuard: Toward Forgery-Resistant Watermarking in Diffusion Models via Compressed Sensing Constraint
Jiewei Lai, Lan Zhang, Chen Tang et al. (+4 more)
Latent-based
diffusion model watermarking embeds watermarks into generated images' latent space to enable content attribution, offering a training-free solution for intellectual property protection and digital forensics. However, these methods exhibit a critical vulnerability to the forgery attack, attackers can extract the watermark by inverting the watermarked image and re-generating it with an arbitrary
prompt, thereby enabling false attribution on malicious content. In this paper, we propose the CSGuard, the first forgery-resistant watermarking schema that leverages compressed sensing to bind the watermarked image generation and verification to a secret
matrix. This ensures that only users possessing the secret
matrix can correctly embed or verify the image watermark, prevents the illegal users from forgery without compromising generation quality and watermark integrity. Experimental results demonstrate that CSGuard achieves strong forgery resistance, reduces the attack success rate from 100.0% to 28.12%, and achieve 100% detection rate on benign watermarked images without compromising watermarking effectiveness.
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All authors Jiewei Lai, Lan Zhang, Chen Tang, Pengcheng Sun, Zhaopeng Zhang, Yunhao Wang, Hui Jin
Computer Vision
LIE: LiDAR-only HD Map Construction with Intensity Enhancement via Online Knowledge Distillation
Kanak Mazumder and Fabian B. Flohr
Online High-Definition (HD) map construction is a key component of autonomous driving. Recent methods rely on multi-view camera images for cost-effective HD map
segmentation, but cameras lack depth information for accurate scene geometry. In contrast,
LiDAR provides precise 3D measurements but lacks dense semantic cues. In this work, we propose LIE,
LiDAR-only semantic map construction method that employ Knowledge
Distillation (KD) to handle the lack of dense semantic and texture cues. Specifically, the teacher branch fuses student
LiDAR features and the corresponding 2D intensity map tile to provide dense supervision for segmenting map elements using online
distillation scheme. Experimental results show that our method outperforms all single-modality approaches, achieving 8.2% higher mIoU than the state-of-the-art camera-based model on nuScenes. LIE is robust over long ranges and under challenging weather and lighting, and efficiently adapts to Argoverse2 with only 10% fine-tuning, surpassing camera-based models trained on the full
dataset. Source code will be available \href{https://iv.ee.hm.edu/lie/}{here}.
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Computer Vision
Decision Boundary-aware Generation for Long-tailed Learning
Jiacheng Yang, Ruichi Zhang, Chikai Shang et al. (+5 more)
Long-tailed data bias decision boundaries toward head classes and degrade tail class accuracy.
Diffusion-based generative augmentation address this problem by generating additional data, while head-to-tail transfer further mitigate the generator bias inherit from long-tailed
dataset. However, we show that while head-to-tail transfer helps balance the decision space of the classifier, it also induces latent non-local feature mixing that entangles inter-class features, causing decision boundary overlap and tail class distribution shift. To address this, we first identify the problem of boundary ambiguity and then propose Decision Boundary-aware Generation (DBG) framework, which promotes near-boundary representation learning by generating informative near-boundary samples. Overall, DBG rebalances the long-tailed
dataset while yielding more separable decision space for long-tailed learning. Across standard long-tailed
benchmarks, DBG consistently improves tail class and overall accuracy with less inter-class overlap. The code of DBG is available at https://github.com/keepdigitalabc-svg/DBG.
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All authors Jiacheng Yang, Ruichi Zhang, Chikai Shang, Mengke Li, Xinyi Shang, Junlong Gao, Yonggang Zhang, Yang Lu
Computer Vision · Cross-listed from math.OC
Quaternion Nonlinear Transform-Induced Nuclear Norm for Low-Rank Tensor Completion
Biswarup Karmakar and Ratikanta Behera
Tensor completion has emerged as a powerful framework for recovering missing data in multidimensional signals by exploiting low-rank tensor structures. Among existing approaches, linear transform-based tensor
nuclear norm (TNN) methods have achieved considerable success by enforcing low-rankness on transformed frontal slices. However, the low-rank structure revealed by linear transforms remains inherently limited. To better capture intrinsic correlations, nonlinear transform-based TNN (NTTNN) models have been proposed, significantly enhancing low-rank representation through composite transforms. Despite their effectiveness, existing NTTNN methods are restricted to real-valued tensors and fail to model quaternion-valued data, which are essential for preserving inter-channel dependencies in color images and
videos. Extending nonlinear TNN models to the quaternion domain is challenging due to the non-commutativity of quaternion multiplication and the complexity of quaternion singular value decomposition. To address the limitations encountered in prior works, we propose a quaternion nonlinear transform-induced tensor
nuclear norm (QNTTNN) via a real
embedding of quaternions, enabling tractable
nuclear norm definitions and efficient optimization. Building upon QNTTNN, we formulate a quaternion tensor completion model and develop a proximal alternating minimization
algorithm with rigorous convergence guarantees. Extensive experiments on
benchmark color
video inpainting
datasets validate the superior performance of the proposed method over existing approaches.
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Computer Vision
SplAttN: Bridging 2D and 3D with Gaussian Soft Splatting and Attention for Point Cloud Completion
Zhaoyang Li, Zhichao You and Tianrui Li
Although multi-modal learning has advanced point cloud completion, the theoretical mechanisms remain unclear. Recent works attribute success to the connection between modalities, yet we identify that standard hard projection severs this connection: projecting a sparse point cloud onto the image plane yields an extremely sparse support, which hinders visual prior propagation, a failure mode we term Cross-Modal
Entropy Collapse. To address this practical limitation, we propose SplAttN, which replaces hard projection with Differentiable Gaussian Splatting to produce a dense, continuous image-plane representation. By reformulating projection as continuous density estimation, SplAttN avoids collapsed sparse support, facilitates gradient flow, and improves cross-modal connection learnability. Extensive experiments show that SplAttN achieves state-of-the-art performance on PCN and ShapeNet-55/34. Crucially, we utilize the real-world KITTI
benchmark as a stress test for multi-modal reliance. Counter-factual evaluation reveals that while baselines degenerate into unimodal template retrievers insensitive to visual removal, SplAttN maintains a robust dependency on visual cues, validating that our method establishes an effective cross-modal connection. Code is available at https://github.com/zay002/SplAttN.
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Computer Vision
SRGAN-CKAN: Expressive Super-Resolution with Nonlinear Functional Operators under Minimal Resources
Roberto Isai Navaro-Aviña, Eduardo Said Merin-Martinez, Andres Mendez-Vazquez et al. (+1 more)
Single-Image Super-Resolution (SISR) aims to reconstruct a High-Resolution (HR) image from a Low-Resolution (LR) observation, a fundamentally ill-posed problem where high-frequency details are severely degraded at large upscaling factors. Recent advances have been driven by
transformer-based architectures and
diffusion models improve global context modeling and perceptual quality at the cost of increased computational complexity. In contrast, this work focuses on enhancing the expressivity of local operators under minimal resources. We propose SRGAN–CKAN, a hybrid super-resolution framework that integrates Convolutional Kolmogorov–Arnold Networks (CKAN) into an
adversarial learning setting reformulating convolution as a nonlinear patch-based transformation. The proposed operator replaces linear local mappings with spline-based functional representations, allowing expressive modeling of complex local structures and high-frequency textures using minimal hardware resources. Experimental results demonstrate that the proposed approach improves perceptual quality while preserving reconstruction fidelity, achieving a favorable balance between distortion-based and perceptual metrics. These results are obtained under constrained computational settings, highlighting the efficiency of the proposed formulation. Overall, this work introduces a complementary direction to existing approaches by improving the representational power of local transformations, providing an efficient and scalable alternative to globally intensive architectures.
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All authors Roberto Isai Navaro-Aviña, Eduardo Said Merin-Martinez, Andres Mendez-Vazquez, Eduardo Rodriguez-Tello
Computer Vision
Registration-Free Learnable Multi-View Capture of Faces in Dense Semantic Correspondence
Panagiotis P. Filntisis, George Retsinas, Radek Daněček et al. (+3 more)
Recent frameworks like ToFu and TEMPEH provide an automated alternative to classical registration pipelines by predicting 3D meshes in dense semantic correspondence directly from calibrated multi-view images. However, these learning-based methods rely on the slow, manual registration pipelines they aim to replace for their training supervision. We overcome this limitation with MOCHI (Multi-view Optimizable Correspondence of Heads from Images), a multi-view 3D face prediction framework trained without requiring registered training data. MOCHI eliminates the registration data dependency by enforcing topological consistency through a pseudo-linear inverse kinematic solver. Semantic
alignment is guided by dense keypoints from a 2D landmark predictor trained exclusively on synthetic data. Our analysis further reveals that standard point-to-surface distances induce training instabilities and visual artifacts in registration-free settings. We propose pointmap- and normal-based losses instead, which provide smoother gradients and superior reconstruction fidelity. Finally, we introduce a test-time optimization scheme that refines network weights over a few dozen iterations. This approach bridges the gap between feed-forward efficiency and iterative optimization precision, allowing MOCHI to outperform traditional labor-intensive pipelines in both reconstruction accuracy and visual quality. Code and model are public at: https://filby89.github.io/mochi.
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All authors Panagiotis P. Filntisis, George Retsinas, Radek Daněček, Vanessa Sklyarova, Petros Maragos, Timo Bolkart
Computer Vision · Cross-listed from cs.RO
Decompose and Recompose: Reasoning New Skills from Existing Abilities for Cross-Task Robotic Manipulation
Xitie Zhang, Aming Wu and Yahong Han
Cross-task generalization is a core challenge in open-world robotic manipulation, and the key lies in extracting transferable manipulation knowledge from seen tasks. Recent in-context learning approaches leverage seen task demonstrations to generate actions for unseen tasks without parameter updates. However, existing methods provide only low-level continuous action sequences as context, failing to capture composable skill knowledge and causing models to degenerate into superficial trajectory imitation. We propose Decompose and Recompose, a skill
reasoning framework using atomic skill-action pairs as intermediate representations. Our approach decomposes seen demonstrations into interpretable skill–action
alignments, enabling the model to recompose these skills for unseen tasks through compositional
reasoning. Specifically, we construct a task-adaptive dynamic demonstration library via visual-semantic
retrieval combined with skill sequences from a planning
agent, complemented by a coverage-aware static library to fill missing skill patterns. Together, these yield skill-comprehensive demonstrations that explicitly elicit compositional
reasoning for skill composition and execution ordering. Experiments on the AGNOSTOS
benchmark and real-world environments validate our method's zero-shot cross-task generalization capability.
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Computer Vision · Cross-listed from cs.IR
Interactive Multi-Turn Retrieval for Health Videos
Chengzheng Wu, Ke Qiu, Baoming Zhang et al. (+3 more)
The growing availability of health-related instructional
videos creates new opportunities for clinical training, patient rehabilitation, and health education, yet existing
retrieval systems remain largely single-turn: a user submits one query and receives one ranked list. This interaction is brittle in health scenarios, where information needs are often vague at first and become clinically meaningful only after follow-up constraints such as posture, hand placement, contraindications, equipment, or patient condition are specified. We introduce interactive multi-turn semantic
retrieval for health
videos and construct MHVRC, a Multi-Turn Health
Video Retrieval Corpus, by combining
video-grounded descriptions from VideoChat-Flash with query refinements generated by DeepSeek. We further propose DATR, a Dialogue-Aware Two-Stage
Retrieval framework. DATR first performs efficient coarse
retrieval with a CLIP-style dual encoder and sparse frame sampling, then re-ranks the top candidates through multi-turn query fusion and a lightweight cross-encoder scoring module. Experiments on MHVRC show consistent gains over strong text-
video retrieval baselines, while user studies indicate that refined multi-turn queries better capture fine-grained procedural semantics than single-turn annotations. The work establishes a
benchmark and a scalable technical recipe for interactive health
video retrieval.
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All authors Chengzheng Wu, Ke Qiu, Baoming Zhang, Ruiyu Mao, Xulong Tang, Kaixing Yang
Computer Vision · Cross-listed from cs.CL
Injecting Distributional Awareness into MLLMs via Reinforcement Learning for Deep Imbalanced Regression
Yao Du, Shanshan Song and Xiaomeng Li
Multimodal large
language models (MLLMs) struggle with numerical regression under long-tailed target distributions.
Token-level supervised fine-tuning (SFT) and point-wise regression rewards bias learning toward high-density regions, leading to regression-to-the-mean behavior and poor tail performance. We identify the lack of cross-sample relational supervision as a key limitation of existing MLLM training paradigms. To address it, we propose a distribution-aware reinforcement learning framework based on Group Relative Policy Optimization, which introduces batch-level comparison-based supervision via the Concordance Correlation Coefficient-based reward to align predicted and ground-truth distributions in terms of correlation, scale, and mean. The framework is plug-and-play, requiring no architectural modification. Experiments on a unified suite of long-tailed regression
benchmarks show consistent improvements over SFT and existing MLLM regression methods, with particularly strong gains in medium- and few-shot regimes.
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Computer Vision
Recall to Predict: Grounding Motion Forecasting in Interpretable Motion Bank
Abhishek Vivekanandan, Ahmed Abouelazm and J. Marius Zöllner
Motion
forecasting often requires trading interpretability for predictive accuracy. Standard anchor-based architectures rely on opaque latent queries that are highly prone to latent collapse, or naive trajectory sampling that limits multi-modal diversity. We propose an end-to-end differentiable framework that grounds predictions in a comprehensive "motion bank", a structured
embedding space of physically realizable trajectories constructed via contrastive learning. Rather than regressing paths from a blank slate, our architecture dynamically retrieves explicit motion priors using a novel Anchor
Retrieval Layer. This module adapts orthogonally initialized queries via a Dual-Level Gated Cross-
Attention mechanism and executes discrete trajectory selection using a Straight-Through Gumbel-Softmax
estimator to preserve continuous gradient flow. The retrieved semantically grounded anchors are then geometrically refined by a DETR-style decoder, optimized jointly with a Winner-Takes-All (WTA) kinematic Gaussian Mixture Model (GMM), a latent diversity penalty, and a soft-min weighted endpoint loss. By strictly conditioning the decoding phase on diverse, interpretable motion primitives, our approach eliminates the "black box" of standard latent queries while achieving competitive multi-modal accuracy on the Argoverse 2 and Waymo Open Motion
datasets. Code is available at: https://github.com/abviv/recall2predict
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Computer Vision
VISTA: Video Interaction Spatio-Temporal Analysis Benchmark
Alejandro Aparcedo, Akash Kumar, Aaryan Garg et al. (+5 more)
Existing
benchmarks for
Vision-
Language Models (VLMs) primarily evaluate spatio-temporal understanding on simple single-action
videos, closed attribute sets and restricted entity types, failing to capture the freeform, multi-action interactions between diverse entities which characterize real-world
video understanding. Furthermore, the lack of a systematic framework for analyzing model failures across complementary spatio-temporal axes hinders comprehensive evaluation. To address these gaps, we introduce VISTA, a
Video Interaction Spatio-Temporal Analysis
benchmark designed for open-set, multi-entity and multi-action spatio-temporal understanding in VLMs. VISTA decomposes
videos into interpretable entities, their associated actions, and relational dynamics, enabling multi-axis diagnostics and unified assessment of relational, spatial, and temporal understanding. Our
benchmark integrates multiple
datasets into a single interaction-aware taxonomy and comprises ~12K curated
video-query pairs spanning diverse scenes and complexities. We systematically evaluate 11 state-of-the-art VLMs on VISTA, and break down aggregate performance across our taxonomy to reveal shortcomings and pronounced spatio-temporal biases obscured by traditional metrics. By providing detailed, taxonomy-driven diagnostics on a challenging
dataset, VISTA offers a nuanced framework to guide advances in model design, pretraining strategies, and evaluation protocols. Overall, VISTA is the first, large-scale, interaction-aware diagnostic
benchmark for spatio-temporal understanding in VLMs.
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All authors Alejandro Aparcedo, Akash Kumar, Aaryan Garg, Dalton Pham, Wen-Kai Chen, Anirudh Bharadwaj, Aman Chadha, Yogesh Rawat
Computer Vision
Sparse Representation Learning for Vessels
Chinmay Prabhakar, Bastian Wittmann, Paul Büschl et al. (+3 more)
Analyzing human vasculature and vessel-like, tubular structures, such as airways, is crucial for disease diagnosis and treatment. Current methods often rely on small sub-regions or simplified tree-like structures, rendering analysis of entire organ-level networks at clinical resolution computationally challenging. To this end, we propose VAEsselSparse, an efficient encoder-decoder model to obtain a meaningful yet compact representation of the entire organ-level vascular network at sub-millimeter resolution. VAEsselSparse leverages the inherent sparsity of 3D vascular structures via sparse convolutions and
attention mechanisms, achieving substantial spatial compression rates of 8 x 8 x 8. We demonstrate superior reconstruction performance compared to dense counterparts and previous methods. Importantly, the resulting latent space retains clinically relevant discriminative features readily usable for classification tasks, such as aneurysm/stenosis or subvariants of the circle of Willis. Moreover, the compact latent space of VAEsselSparse serves as an effective representation for learning vessel-specific priors through generative models, enabling the synthesis of realistic vasculature.
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All authors Chinmay Prabhakar, Bastian Wittmann, Paul Büschl, Hongwei Bran Li, Bjoern Menze, Suprosanna Shit
Computer Vision
VoxAfford: Multi-Scale Voxel-Token Fusion for Open-Vocabulary 3D Affordance Detection
Haowen Sun, Shaolong Zhang, Mingyang Li et al. (+6 more)
Open-vocabulary 3D affordance detection requires localizing interaction regions on point clouds given novel affordance descriptions. Recent methods extend
multimodal large
language models (MLLMs) with special output
tokens that are decoded into
segmentation masks. However, these
tokens are produced through autoregressive generation, which models sequential dependencies rather than spatial neighborhood relations, leaving them semantically rich but spatially impoverished for 3D localization. We propose Voxel-enhanced Affordance detection (VoxAfford), which bypasses this bottleneck by injecting multi-scale
geometric features from a frozen pre-trained 3D VQVAE encoder into the output
tokens after generation. Each output
token uses its affordance semantics as a query to retrieve relevant
geometric patterns from its paired voxel scale via cross-
attention, with a learned compatibility gate controlling the injection strength. The enhanced
tokens are then aggregated into a spatially-aware affordance
prompt through semantic-conditioned
attention and propagated alongside per-point features to generate the final mask. Experiments on open-vocabulary affordance detection tasks show that VoxAfford achieves state-of-the-art performance with approximately an 8% improvement in mIoU, and real
robot experiments confirm zero-shot transfer to novel objects.
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All authors Haowen Sun, Shaolong Zhang, Mingyang Li, Chengzhong Ma, Xinzhe Chen, Qiongjie Cui, Xingyu Chen, Zeyang Liu, Xuguang Lan
Computer Vision
AgriKD: Cross-Architecture Knowledge Distillation for Efficient Leaf Disease Classification
Minh-Dung Le, Minh-Duc Hoang, Hoang-Vu Truong et al. (+1 more)
Automated leaf disease classification is critical for early disease detection in resource-constrained field environments.
Vision Transformers (ViTs) provide strong representation capability by modeling long-range dependencies and inter-class relationships; however, their high computational cost makes them impractical for deployment on edge devices. As a result, existing approaches struggle to effectively transfer these rich representations to lightweight models. This paper introduces AgriKD, a cross-architecture knowledge
distillation framework for efficient edge deployment, which transfers knowledge from a
Vision Transformer (ViT) teacher to a compact convolutional student model. To bridge the representational gap between
Transformer and CNN architectures, the proposed approach integrates multiple
distillation objectives at the output, feature, and relational levels, where each objective captures a different aspect of the teacher knowledge. This enables the student model to better preserve and utilize
transformer-derived global representations. Experiments on multiple leaf disease
datasets show that the distilled student achieves performance comparable to the teacher while significantly improving efficiency, reducing model parameters by approximately 172 times, computational cost by 47.57 times, and inference latency by 18-22 times. Furthermore, the optimized model is deployed across multiple runtime formats, including ONNX, TFLite Float16, and TensorRT FP16, achieving consistent predictive performance with negligible accuracy degradation. Real-world deployment on NVIDIA Jetson edge devices and a mobile application demonstrates reliable real-time inference, highlighting the practicality of AgriKD for AI-powered agricultural applications in resource-constrained environments.
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All authors Minh-Dung Le, Minh-Duc Hoang, Hoang-Vu Truong, Thi-Thu-Hong Phan
Computer Vision
CHASE: Competing Hypotheses for Ambiguity-Aware Selective Prediction
Kartik Jhawar, Yuhao Geng, Atul N. Parikh et al. (+1 more)
Standard selective prediction methods typically estimate uncertainty from the output of a single predictive branch. While effective for general uncertainty estimation, these approaches often struggle under partial observability, where local temporal evidence can be contradictory and standard confidence scores become misleading. We introduce CHASE (Competing Hypotheses for Ambiguity-Aware Selective Prediction), a selective prediction framework that explicitly compares structured temporal explanations to determine whether to commit to a decision or abstain. Because genuine ambiguity causes the score gap between competing hypotheses to collapse, CHASE optimizes a ranking-aware selector over these hypothesis margins to globally separate safe commitments from fundamentally uncertain ones. We evaluate this framework on the problem of hidden connectivity inference, utilizing a controlled, physically grounded simulator inspired by the dynamics of
giant unilamellar vesicles (GUVs), alongside zero-shot qualitative transfer (without retraining or fine tuning) to representative real GUV
videos. Our experiments demonstrate that explicitly
reasoning over competing hypotheses provides a superior balance of metrics. Compared to canonical uncertainty baselines, CHASE achieves statistically significant gains in overall no-abstain accuracy, three-way accuracy, and overall ambiguity-aligned abstention (at 80% coverage). Specifically, it yields up to an 11.0% relative mean improvement in overall
alignment, alongside up to an 8.8% relative boost in three-way accuracy in the very-high ambiguity regime. By maintaining a selective risk boundary strictly at par with the best baselines at 80% coverage, and reducing overall risk by 9.9% at 90% coverage, this framework offers a more reliable approach to decision-making under structured ambiguity.
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All authors Kartik Jhawar, Yuhao Geng, Atul N. Parikh, Lipo Wang
Computer Vision
The Perceptual Bandwidth Bottleneck in Vision-Language Models: Active Visual Reasoning via Sequential Experimental Design
Anjie Liu, Ziqin Gong, Yan Song et al. (+6 more)
Visual perception in modern
Vision-
Language Models (VLMs) is constrained by a perceptual bandwidth bottleneck: a broad field of view preserves global context but sacrifices the fine-grained details required for complex
reasoning. We argue that high-resolution visual
reasoning is therefore not only semantic
reasoning but also task-relevant evidence acquisition under limited perceptual bandwidth. Inspired by active
vision and information foraging, we formalise this process as sequential
Bayesian optimal experimental design (S-BOED), where an
agent decides which visual evidence to acquire before answering. Since exact
Bayesian inference is intractable in continuous gigapixel spaces, we derive a tractable coverage–resolution objective as a proxy for task-relevant information gain. We instantiate this framework with FOVEA, a training-free procedure that refines VLM crop proposals through evidence-oriented probing. Experiments on high-resolution
benchmarks show consistent gains over direct and ReAct-style baselines, with particularly strong improvements in search-dominated remote-sensing settings.
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All authors Anjie Liu, Ziqin Gong, Yan Song, Yuxiang Chen, Xiaolong Liu, Hengtong Lu, Kaike Zhang, Chen Wei, Jun Wang
Computer Vision
Zero-Shot Interpretable Image Steganalysis for Invertible Image Hiding
Hao Wang, Yiming Yao, Yaguang Xie et al. (+2 more)
Image steganalysis, which aims at detecting secret information concealed within images, has become a critical countermeasure for assessing the security of steganography methods, especially the emerging invertible image hiding approaches. However, prior studies merely classify input images into two categories (i.e., stego or cover) and typically conduct steganalysis under the constraint that training and testing data must follow similar distribution, thereby hindering their application in real-world scenarios. To overcome these shortcomings, we propose a novel interpretable image steganalysis framework tailored for invertible image hiding schemes under a challenging zero-shot setting. Specifically, we integrate image hiding, revealing, and steganalysis into a unified framework, endowing the steganalysis component with the capability to recover the secret information embedded in stego images. Additionally, we elaborate a simple yet effective residual augmentation strategy for generating stego images to further enhance the generalizability of the steganalyzer in cross-
dataset and cross-architecture scenarios. Extensive experiments on
benchmark datasets demonstrate that our proposed approach significantly outperforms the existing steganalysis techniques for invertible image hiding schemes.
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All authors Hao Wang, Yiming Yao, Yaguang Xie, Tong Qiao, Zhidong Zhao
Computer Vision
Colinearity Decay: Training Quantization-Friendly ViTs with Outlier Decay
Jin Tong, Guang Liang, Peilin Sun et al. (+1 more)
Low-bit quantization is a practical route for efficiently deploying
vision Transformers, yet activation outliers complicate fully quantized deployment. Existing methods either handle quantization post-training or suppress large activations during training; however, aggressively restricting outliers in
vision models can lead to a poorer trade-off between full-precision and quantized accuracy. We argue that rather than simply suppressing outliers, the training objective should control the structural amplification that makes them harmful. To this end, we introduce Colinearity-Decay (CD), a structural regularizer for ordered
matrix pairs within
Transformer blocks. CD penalizes detrimental cross-
matrix alignment and mitigates extreme activations without altering the architecture or task loss. Applied as a decoupled update, CD is non-invasive and introduces minimal training overhead. Across ImageNet-1K pre-training, COCO detection, and downstream fine-tuning, CD consistently boosts quantized accuracy across multiple pipelines while preserving, or even improving, full-precision performance. Ultimately, our results demonstrate that structural regularization effectively prepares
vision Transformers for low-bit deployment with zero inference-time overhead.
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All authors Jin Tong, Guang Liang, Peilin Sun, Jianxin Wu