Computer Vision
Concept Score Relearning: A Unified Cross-Architecture Attack on Concept Erasure
Hong Xi Tae, Jiaming Zhang, Wenwen He et al. (+2 more)
Concept erasure aims to suppress undesirable knowledge in text-to-image generative models. However, existing robustness evaluations typically rely on relearning attacks tailored to specific model architectures. We study concept reactivation across two substantially different generative paradigms: noise-prediction U-Nets and flow-matching
Transformers. We introduce Concept Score Relearning (CSR), a unified parameter-level framework that reactivates erased concepts by optimizing each model within its native prediction space. CSR requires no external target-concept image
dataset and applies the same concept-directed objective to both U-Net-based Stable
Diffusion and
Transformer-based FLUX. Experiments across diverse concepts and multiple erasure methods demonstrate consistent concept reactivation across both architectures, highlighting the cross-architecture applicability of CSR and the persistent recoverability of apparently erased concepts. For strict nudity, CSR reaches average ASRs of 50.47% on FLUX and 40.29% on Stable
Diffusion, consistently ranking first across all evaluated safety settings.
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All authors Hong Xi Tae, Jiaming Zhang, Wenwen He, Xuan Wang, Wei Yang Bryan Lim
Computer Vision · Cross-listed from cs.LG
Elucidating the Design Space of Regression-based Diffusion Reinforcement Learning
Toyota Li, David Zhao and Alan Zhao
A nascent family of methods that forgoes the policy gradient and reweights a supervised regression instead has garnered momentum in reinforcement learning for
diffusion and flow models. DiffusionNFT, FlowAWR, and RAM are representative regimes with contrasting motivations. It is yet opaque what, if anything, they share. We substantiate that each is the solution of one divergence-constrained reward-maximization problem, and they are differentiated only by the convex generator that defines the constraint. Under the unified modeling framework, we unravel the relaxations that prior art made during building the advantage-embedded regression target: approximating the KKT condition and posterior normalizer for the linear and exponential tilt shapes DiffusionNFT and FlowAWR respectively, while preserving the exact sparsemax projection onto the probability simplex for linear tilt leads to another superior model type in this work. Beyond the theoretical underpinnings, we further empirically investigate the design space and shed light on the training recipe for regression-style
diffusion RL. Retaining the merits discovered during our exploration gives rise to DiffusionRFT, our paradigm that converges faster, trains more stably, and attains the top performance.
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Computer Vision
TC-ADA: One-Shot Active Domain Adaptation for Semantic Segmentation
Weihao Yan, Yeqiang Qian, Yueyuan Li et al. (+3 more)
Manual dense annotation remains a major obstacle to deploying semantic segmentation models in new driving environments. Active domain adaptation (ADA) seeks label-efficient transfer by annotating only a selected portion of the target domain. Existing ADA methods commonly implement this process through multiple rounds of acquisition, annotation, and retraining. We study a practical one-shot image-level setting that selects and densely annotates a fixed target subset in a single round, followed by uninterrupted adaptation. Within this setting, we develop Target-Calibrated Active Domain Adaptation (TC-ADA) as a joint design of complete-image acquisition and target-calibrated adaptation. Stage~1 uses visual representations from a
vision foundation model (VFM) together with semantic predictions from a fixed unsupervised domain adaptation model to select representative and informative target images without target annotations. Stage~2 jointly uses labeled source data, labeled target data, and the remaining unlabeled target data, while calibrating source and target supervision under limited target labels. Extensive experiments across five synthetic-to-real and real-to-real driving transfers show consistent improvements over representative ADA baselines. With only 23 to 46 labeled target images on four transfers and 140 on Mapillary, TC-ADA stays within 1.9 mean intersection over union (mIoU) points of target-only full supervision. Code will be available at https://github.com/ywher/TC-ADA.
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All authors Weihao Yan, Yeqiang Qian, Yueyuan Li, Tao Li, Chunxiang Wang, Ming Yang
Computer Vision · Cross-listed from cs.LG
A Light Bilevel Refinement Aligns Self-Supervised Representations for Stronger Task-Specific Learning
Gustav Wagner Zakarias and Zheng-Hua Tan
Self-supervised pretraining learns representations that are broadly transferable across downstream tasks, yet direct fine-tuning can be suboptimal due to misalignment between self-supervised and downstream task objectives, potentially degrading pretrained features beneficial to the downstream task. The BiSSL framework addressed this by introducing a transitional training stage formulated as a bilevel optimization problem, in which the downstream task objective guides the self-supervised learning process in refining pretrained representations to better facilitate subsequent fine-tuning. However, BiSSL relies on conventional bilevel optimization solving techniques whose costly implicit hypergradient approximations render the method increasingly impractical for contemporary model architectures. To make it efficient and scalable, we introduce BiSSLight, which combines M-FAC-based implicit gradient approximation with parameter-efficient fine-tuning via LoRA, enabling efficient application at larger scales that were previously impractical. Evaluation across multiple downstream tasks and contemporary model architectures shows that BiSSLight consistently improves downstream performance, with gains becoming more pronounced as model size increases despite stronger baselines. The method is highly computationally efficient, reducing computation time by more than a factor of ten compared to its predecessor on a ViT-H backbone.
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Computer Vision
TT-VidT: Decoupling the Temporal Axis for Efficient Motion-Centric Video Pretraining
Shih-Ying Yeh, Daniel Z. Kaplan, Xuehai Wang et al. (+3 more)
Comparisons in
video self-supervised learning often evaluate complete training recipes rather than isolating the method itself: architecture, objective, data exposure, schedule, scale, and decoder capacity can all vary at once. This makes it hard to identify which choices yield motion-prioritized representations, whose gains concentrate on frame-to-frame change while retaining useful appearance. We address this with a matched $4 \times 6 = 24$ architecture-objective study at roughly 170M ~ 190M encoder scale on $\sim$1.7M OpenVid and Moments-in-Time v2 clips for 8 epochs, and propose TT-VidT. TT-VidT combines a DINOv3-initialized ViT-B/16 per-frame spatial path with a compact Temporal Transfer Layer, trained by Diff Compression to reconstruct target frames from a first-frame appearance anchor and frame-specific motion
tokens. The sweep shows that TT3D with Diff Compression, not either component alone, enters the strongest motion-sensitive regime, and decoder ablations favor a compact
video-pretrained decoder. In final comparison, TT-VidT leads Jester, Something-Something V2, ARID, and Diving48 fine-tuning simultaneously, improving over the strongest non-TT row by 54% ~ 121%, while using 48% fewer encoder FLOPs than DisMo and 55% fewer than VideoMAE or V-JEPA2. HMDB51, IARD, and EPIC-Kitchens bound the claim.
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All authors Shih-Ying Yeh, Daniel Z. Kaplan, Xuehai Wang, Fu-En Yang, Min-Hung Chen, Shang-Hong Lai
Computer Vision
TTRSD: Test-Time Reinforcement Learning with Self-Distillation for Vision-Language Models
Shuning Wang, Zhiheng Wu, Xun Zhou et al. (+8 more)
Test-time reinforcement learning enables
vision-
language models (VLMs) to adapt using unlabeled inputs. However, repeated sampling under fixed visual conditions can reinforce shared perceptual errors, while sequence-level rewards fail to isolate visual perception the foundational bottleneck that anchors
multimodal reasoning risking the degradation of pre-trained
reasoning capabilities. We propose TTRSD, a test-time reinforcement learning framework combining multi-view answer-level self-
distillation with visual contrastive
token selection. A shared policy aggregates teacher predictions across original, cropped, and downsampled views into an answer distribution. Student trajectories generated from the original image receive rewards based on the support for their final answers in this distribution. To allocate this feedback precisely toward perceptual bottlenecks, we compare the log-probabilities of the same sampled
tokens under original and visually ablated inputs while holding their textual prefixes fixed, selecting visually sensitive positions for policy-gradient updates. TTRSD separates update direction, determined by group-relative advantages, from update position, determined by visual sensitivity, without requiring ground-truth labels, external verifiers, or a separate teacher. With only 20 unlabeled adaptation samples, TTRSD improves performance across seven
benchmarks and three VLMs, raising InternVL3-2B's MMMU accuracy from 35.79% to 49.32%(+13.53%), demonstrating cross-
dataset generalization while preserving inherent
reasoning integrity.
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All authors Shuning Wang, Zhiheng Wu, Xun Zhou, Chongyang Cui, Chen Jia, Bowen Liu, Chuanjie Li, Xiang Chen, Yi Yang, Yumeng Zhang, Wenjie Huang
Computer Vision
Resolving State-Representation Mismatch: State-Space Visual Reasoning for Open-Loop VLA Planning
Junhao Xiao, Haoxiang Zhao, Menghao Fang et al. (+8 more)
Despite rapid progress in
vision-
language-action (VLA) models, existing
reasoning paradigms still face a fundamental state-representation mismatch in open-loop planning. Given only an initial observation, models must internally simulate action-conditioned state transitions, whereas text-, pixel-, and latent-space
reasoning can suffer from lossy spatial compression, error-accumulating visual generation, and bypass of intermediate latent
tokens, respectively, undermining reliable long-horizon planning. We propose State-Space Visual
Reasoning (SSVR), which decouples static visual context,
language constraints, and a recurrent latent state. SSVR encodes the initial image and instruction once, then conditions each action prediction on the latent state and updates it with an action-conditioned GRU. Using Qwen2.5-VL as the backbone, SSVR achieves 99.5/99.6, 96.3/98.0, and 83.9/90.6 EM/PR on FrozenLake, Maze, and MiniBehavior, substantially outperforming prior methods. Extensive experiments support the effectiveness of recurrent state modeling for VLA open-loop planning across input transformations and transfer settings. By reusing static visual-textual context and updating a compact recurrent state, SSVR supports efficient multi-step inference, achieving up to $98.58\times$ faster Maze decoding rollouts than the evaluated baselines with the prefix cache prebuilt.
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All authors Junhao Xiao, Haoxiang Zhao, Menghao Fang, Jinkui Zhang, Jinghan Yu, Xinyu Huang, Zhiyu Wu, Kaiming Xu, Yi Chen, Youjun Bao, Zhiyuan Ma
Computer Vision
Peixi Wu, Mingzhou Jiang, Feipeng Ma et al. (+11 more)
Universal
multimodal retrieval requires compact
embeddings that preserve task-relevant semantic information across diverse modalities. Prior works have incorporated latent
reasoning into
multimodal embedding learning to refine this information before
embedding extraction. However, most existing approaches remain confined to deterministic latent paths, without exploring alternative trajectories to discover better
embeddings. Thus, we propose VaME (Variational
Multimodal Embeddings), a framework that models latent
reasoning as a learnable distribution over trajectories. Specifically, we first introduce Variational Latent
Reasoning (VLR) to enable autoregressive exploration in latent space, guided by answer reconstruction through a lightweight decoder. Meanwhile, we augment the original
embedding-
token readout with a latent-fused
embedding to facilitate exploration during subsequent reinforcement learning. Finally, we optimize latent
reasoning over
stochastic variational trajectories through reinforcement learning, using Semantic Decoding Reward (SDR) to favor semantically meaningful trajectories with interpretable decoded outcomes. On the 78-task MMEB-V2
benchmark, spanning image,
video, and visual-document
retrieval, VaME outperforms most explicit CoT-based models and all latent-
reasoning baselines. VaME also demonstrates robust performance on
reasoning-intensive
benchmarks such as MRMR, with substantial gains after reinforcement learning. Importantly, VaME achieves these gains with at least a 4.25x inference speedup over the deterministic latent autoregressive baselines. The code will be made publicly available.
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All authors Peixi Wu, Mingzhou Jiang, Feipeng Ma, Biao Yang, Yunhao Zhou, Wei Yuan, Bosong Chai, Huizu Lin, Jie Chen, Zhangchi Hu, Fan Yang, Wenwu Ou, Hebei Li, Xiaoyan Sun
Computer Vision
Groupwise Selective State-Space Filtering for Accurate and Streaming Action Boundary Detection
Mustafa Bora Çelik
Action boundary detection partitions untrimmed
video into intervals without assigning action classes. We present a boundary-detection adapter operating on pre-extracted
video features, learning temporal representations via groupwise selective scans. Learned group fusion and temporal modeling convert these into transition scores, which are decoded into boundary timestamps. Trained with boundary-time supervision, the class-agnostic model is evaluated on Breakfast, GTEA, and 50Salads using temporal tolerances and bipartite matching, achieving boundary $F_1$ scores of 0.457, 0.622, and 0.611. A stateful variant enables feature-streaming inference with zero neural look-ahead, one-sample peak confirmation, and bounded
memory. Downstream systems can subsequently assign s
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Computer Vision
SciGen-Verifier: A Multimodal Reasoner for Explainable Verification in Scientific Image Generation
Jiali Chen, Zhengteng Lin, Zuqi Wang et al. (+6 more)
In realistic education, a solution is often expressed not only in words but in a drawing–a
circuit, a
geometric construction, a function plot–and a teacher must grade the drawing as carefully as the text. Recent advances in unified
multimodal models have enabled scientific image generation, yet verifying the correctness of these specialized visual outputs remains a critical bottleneck: errors often arise from intricate domain knowledge, structural
reasoning, and multi-step instruction rather than surface-level artifacts. Existing verifiers mainly target natural images and compress judgement into scalar scores, leaving scientific coverage and explainable feedback for error correction underexplored. To bridge this gap, we make three main contributions. (1) We construct SciGen-Verify, a
benchmark dedicated to explainable verification of scientific image generation, spanning instruction following, multidisciplinary
reasoning, and world knowledge domains. It contains a three-tier hierarchical protocol over the binary judgement, supporting explanation, and corrective editing instruction. (2) We develop SciGen-Verifier, a
reasoning-driven
multimodal verifier trained via cold-start supervised fine-tuning followed by a curriculum-based two-stage reinforcement learning pipeline. The rubric-guided process rewards first strengthen scientific
reasoning exploration and outcome rewards subsequently align output with ground-truth annotation. (3) On SciGen-Verify, SciGen-Verifier achieves competitive performance against much larger proprietary models. It further serves as a practical online critic for iterative image rectification.
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All authors Jiali Chen, Zhengteng Lin, Zuqi Wang, Shirong Lin, Xi Yu, Xusen Hei, DingBa Fu, Jiayuan Xie, Yi Cai
Computer Vision
PulseQuant: Propagation-Guided Subspace Correction for 4-Bit Video Diffusion Transformers
Yutong Wang, Xingtong Ge, Enhuai Liu et al. (+4 more)
Quantization errors in
video diffusion transformers can be amplified or attenuated by subsequent denoising updates, making local reconstruction error an incomplete predictor of final impact. We introduce PulseQuant, a 4-bit post-training quantization method that combines trajectory sensitivity with activation geometry to guide offline calibration. Isolated block–step interventions estimate propagation risk, which prioritizes sensitive trajectory states during row-radius selection. With these radii fixed, response-subspace correction uses neighboring-code edits to reduce residual components along dominant activation directions. Both stages preserve the original 4-bit weight representation. Controlled interventions show that short-horizon propagated error predicts final latent error more reliably than immediate block-output error, supporting calibration beyond local reconstruction objectives. Evaluations on Wan models, Self Forcing, and MiniMax-H3 demonstrate improvements in key consistency and dense-reference metrics while remaining competitive on other attributes across model scales and generation paradigms.
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All authors Yutong Wang, Xingtong Ge, Enhuai Liu, Yunke Wang, Tianfan Xue, Xinyuan Chen, Chang Xu
Computer Vision · Cross-listed from cs.RO
Seungyeon Kim, Junhoo Lee, Minkyu Kim et al. (+2 more)
A central goal in robotics is to enable manipulation across changing tasks and environments.
Vision-
language-action (VLA) models provide broad manipulation capabilities but can struggle when execution requires diagnosing failures and adapting behavior. Strong
agents can discover effective interventions through interaction with these policies. We propose Recursive Harness
Distillation to accumulate this experience as reusable guidance across
agents. A strong
agent distills its experience into a playbook for a light
agent, then recursively refines the playbook using the light
agent's execution feedback. The resulting playbook enables
agents to reuse accumulated intervention knowledge in new task instances without updating model parameters. In real-world manipulation, the harness improves success from 37.3% to 64.0%. On SimplerEnv Bridge, the light
agent with the playbook achieves 66.7% success, compared with 41.7% for the GR00T-only baseline, and outperforms the strong
agent without a playbook. The same playbook also benefits the strong
agent, which reaches 79.2% success. These results demonstrate the feasibility of harness
distillation for robotics: intervention experience can be accumulated, refined through execution, and reused across
agents to improve manipulation.
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All authors Seungyeon Kim, Junhoo Lee, Minkyu Kim, Baekseung Kim, Nojun Kwak
Computer Vision · Cross-listed from cs.LG
TNF based Spectral Embedding for Effective Application of Supervised Machine Learning Techniques in Automobile Insurance Fraud Detection
Rohan Yashraj Gupta, Lalith Srikanth Chintalapati, Satya Sai Mudigonda et al. (+2 more)
Fraud detection is an important area of research in the insurance business due to its financial implications. The primary aim of a fraud detection model is to identify fraud and non-fraud cases with high accuracy along with other important metrics such as Sensitivity, Specificity, Precision, F1-score, False Positive Rate, False Discovery Rate, AUC etc. To achieve this, we need to explore a suitable classification model to identify fraud and non-fraud cases. In this work, we have used auto insurance data set and explored classification models such as Decision Tree (DT), Random Forest (RF), XGBoost, LightGBM and Gradient Boosting Machine (GBM). To overcome the problem of data imbalance, we have employed MWMOTE and TGAN techniques. We have used Topological Node Feature(TNF) based spectral
embedding for low dimensional data representation along with some popular
embedding methods like MDS, Isomaps and t-SNE. After studying all the 65 possible combinations of these models, we have proposed an innovative method for effective automobile insurance fraud detection. For the given
dataset, our results show that using a combination of MWMOTE as a data imbalance handling technique (Phase I), TNFSE2 as data
embedding (Phase II) and Random Forest as classification (Phase III) provides the best result in comparison to all other combinations. This work also highlights the efficacy of TNF based spectral
embedding in automobile insurance
dataset
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All authors Rohan Yashraj Gupta, Lalith Srikanth Chintalapati, Satya Sai Mudigonda, Pallav Kumar Baruah, Raghunatha Sarma Rachakonda
Computer Vision
When Does Geometric View Synthesis Help Wine Label Retrieval? A Public One-Shot Benchmark Across Self-Supervised and Vision-Language Backbones
Yueh-Cheng Huang
Geometric view synthesis can expand a single wine-label photograph into a training set, but its value with pretrained image encoders is unclear. We study this on a public WineSensed-derived
benchmark of 1,000 classes, one enrollment photograph per class, and 4,295 real queries. With the earlier DINO
vision transformer (ViT-S/16) recipe,
geometric views raise top-1 accuracy from 34.1% to 62.6-63.7%, about three times the gain from two-dimensional (2D) augmentation. Frozen SigLIP 2-B already reaches 94.7%. A linear head over its frozen features gains 1.2-1.3 percentage points with the two
geometric pipelines localized by the Segment Anything Model (SAM), while the other pipelines gain an inconclusive 0.3-0.6 points. Low-rank adaptation (LoRA) and validation-selected full fine-tuning show no clear gain within the reported confidence intervals; fixed-budget full fine-tuning loses 9-24 points. SAM localization supplies all six views for 99% of sources, compared with 43% for the edge-based front end. Recognition differences between the two cylinder constructions depend on the training recipe and are confounded by their crop and canvas conventions. Rendered-cylinder tests show different responses to source tilt, but an uncalibrated rim-ratio proxy establishes no corresponding trend in recognition on real photographs. An author-confirmed audit of 50 residual errors identifies 21 query-enrollment appearance mismatches, without establishing an irreducible error rate. These results support
geometric synthesis for the tested self-supervised recipe and a smaller benefit through frozen-feature adaptation of the text-supervised encoder.
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Computer Vision
Focus and Supplement: Dual-Enhanced Vision Transformer for Multi-Class Anomaly Classification
Xurui Li, Enjie Xu, Chenzhou Li et al. (+4 more)
Multi-class anomaly classification in industrial
vision remains challenging due to noisy/incomplete anomaly representations and the unknown number of anomaly classes. To overcome this, we propose MACO, a novel multi-class anomaly classification framework that learns comprehensive representations and dynamically estimates class number without prior knowledge. First, a soft-focus
attention uses anomaly maps to concentrate on relevant abnormal regions, while suppressing background noise. Second, auxiliary classification ([A-CLS])
tokens complement the [CLS]
token. They collectively attend to diverse anomaly sub-regions, yielding more holistic and discriminative features. These [A-CLS]
tokens are also effective across more tasks and domains. To infer the class number, we propose Correlation-based Number Estimation strategy. It computes the average correlation among labeled classes and transfers its separability cue to the unlabeled set. Experiments on MVTec AD and MTD
datasets demonstrate our superiority. Under known class number, MACO improves ARI by 6.5% and $\textbf{16.3%}$ on both
datasets, respectively. In the more challenging unknown number scenario, it achieves an $\textbf{11.2%}$ NMI gain on MTD and outperforms existing number estimation strategies by $\textbf{24.1%}$ UPS on MVTec AD. Code will be released at https://github.com/HUST-SLOW/MACO.
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All authors Xurui Li, Enjie Xu, Chenzhou Li, Shilei Zeng, Dayou Huang, Tianyi Ma, Yu Zhou
Computer Vision
ReLoc: Rethinking Scene Coordinate Regression Architecture for Robust Outdoor LiDAR-based Localization
Heejoon Moon, Yurim Cho and Je Hyeong Hong
Scene Coordinate Regression (SCR) has recently emerged as a promising approach for
LiDAR-based localization, achieving accurate localization without requiring an explicit 3D map. Despite their effectiveness, existing SCR methods rely on scene classification-based global
embedding that struggles to provide fine-grained discrimination among nearby locations. Moreover, their reliance on uniform sampling of local features during training assigns equal importance to all points, thereby inadvertently propagating features from dynamic objects or unstable regions and potentially degrading training stability. In this paper, we present ReLoc, a revamped SCR architecture that can effectively address these limitations. First, we redesign the global
embedding module by combining learnable context
tokens with a feature aggregator to capture richer and more discriminative scene context. Second, we introduce an
attention-based local feature enhancement module to mitigate the impact of noisy local features while encouraging context-consistent structures, yielding more robust local feature representations. Experimental results on two large-scale outdoor
datasets demonstrate that our approach achieves state-of-the-art accuracy over previous SCR-based methods while maintaining real-time inference performance.
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Computer Vision
OPERA: A Unified Omnimodal Progressive Spatio-Temporal Reasoning Agent for Referring Video Segmentation
Jingchen Ni, Yuji Wang, Shannan Yan et al. (+3 more)
Referring
video segmentation with heterogeneous
multimodal queries—spanning text, audio, and reference images—demands both robust cross-modal understanding and precise spatio-temporal
reasoning. We propose OPERA (Omnimodal Progressive spatio-tEmporal
Reasoning Agent), a unified
reasoning agent built on a single MLLM that performs dual-axis progressive
reasoning via three specialized stages. Along the temporal axis, a Temporal
Reasoning Agent narrows the frame search space through coarse-to-fine filtering to identify the most informative key frame. Along the spatial axis, a
Distillation Agent establishes what to locate via cross-modal semantic
distillation, and a Grounding
Agent enhanced with GRPO determines where the target appears, with dense mask propagation completing the pixel-level output. OPERA sets a new state of the art on OmniAVS and Ref-AVS and transfers zero-shot to standard referring
video segmentation
benchmarks.
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All authors Jingchen Ni, Yuji Wang, Shannan Yan, Haoru Li, Sitong Chen, Chun Yuan
Computer Vision
FeCoSplat: Feedback-Guided Compression for Feed-Forward 3D Gaussian Splatting
Yuxuan Li, Yihang Chen, Yufeng Zhang et al. (+2 more)
Feed-forward 3D Gaussian Splatting (3DGS) enables efficient novel-view synthesis from sparse multi-view images, yet its representations remain costly to store and transmit. Existing approaches compress either the input images, incurring heavy receiver-side reconstruction, or the reconstructed Gaussian primitives, which are difficult to compress due to their heterogeneous and irregular attributes. We instead compress compact intermediate features, providing a better balance between compression efficiency and receiver-side complexity. Based on this paradigm, we propose FeCoSplat, a feedback-guided compression framework for feed-forward 3DGS. FeCoSplat first compresses multi-view features to obtain an intermediate 3DGS, whose rendered views are used as feedback to guide a second-stage compression for further refinement. The resulting bitstreams are decoded into a compact implicit state, from which the final Gaussian primitives are reconstructed with a lightweight predictor. Experiments demonstrate that FeCoSplat achieves favorable rate–distortion performance, particularly at low bitrates, while requiring only 3.45M parameters for receiver-side Gaussian reconstruction. Code will be released soon.
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All authors Yuxuan Li, Yihang Chen, Yufeng Zhang, Jianfei Cai, Weiyao Lin
Computer Vision
VisionHOPE: Visual Backbones as Self-Modifying Learning Systems
Siran Peng, Tianshuo Zhang, Tianyu Fu et al. (+8 more)
Visual backbones have evolved from Convolutional Neural Networks (CNNs) with local aggregation to
Vision Transformers (ViTs) with global interactions, State-Space Models (SSMs) with input-dependent state transitions, and Test-Time Training (TTT) layers that adapt an inner learner while processing an image. Across this progression, visual computation has become increasingly adaptive to each input, yet the rules governing that adaptation remain largely prescribed by the trained backbone. We introduce VisionHOPE, the first generic visual backbone formulated as a self-modifying learning system, in which what the model remembers and how it learns co-evolve within an image. Building on the self-referential construction of Nested Learning (NL), VisionHOPE realizes this co-
evolution through five coupled
memories that store content, generate key and value representations, and govern learning rate and retention. These
memories evolve jointly as visual context accumulates along each scan. However, directly applying the unconstrained self-referential update to a visual backbone leads to instability. We therefore derive a stability-matched step-size control scheme that combines a soft cap on self-referential injection with a spectral clamp on the retained
memory transition, and prove that the resulting
memory dynamics are non-expansive along each scan. For two-dimensional feature maps, we adapt NL's chunk formulation by aligning chunks with image rows and columns across four directional scans. The proposed VisionHOPE achieves competitive results on ImageNet-1K, COCO, and ADE20K, establishing self-modifying learning systems as a practical foundation for general-purpose visual backbones. The code is available at https://github.com/PSRben/VisionHOPE.
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All authors Siran Peng, Tianshuo Zhang, Tianyu Fu, Weisong Zhao, Haoyuan Zhang, Jiankuo Zhao, Minghui Wu, Ping Jiang, Xiangyu Zhu, Chenxu Zhao, Zhen Lei
Computer Vision
PIC-UIE: Predicting Image-Adaptive Corrections for Lightweight Underwater Image Enhancement
Cunhao Zhu, Dongliang Xu, Xiangtao Kong et al. (+3 more)
Underwater image enhancement (UIE) aims to restore visibility, color fidelity, and structural detail from images degraded by wavelength-dependent attenuation and backscatter. State-of-the-art UIE methods often rely on large backbones and dense image-to-image prediction, limiting their practicality for edge deployment. Moreover, operating entirely in a single color space couples degradation estimation with luminance and chroma correction. To address these challenges, we propose PIC-UIE, a lightweight predictor–executor framework that predicts image-adaptive corrections from a fixed $256\times256$ RGB thumbnail and applies them to the native-resolution input in the YCbCr color space. The predictor produces seven outputs, organized into spatial correction, nonlinear luminance and coupled chroma mapping, and image-level color calibration. A depth map regularizes the transmission proxy during training, whereas inference uses only the RGB input. With 9,486 parameters and 0.094 GFLOPs at $256\times256$, PIC-UIE achieves 24.137 dB PSNR and 0.9216 SSIM on UIEB-90 and 21.320 dB PSNR on zero-shot LSUI. It further processes native 4K images at 55.0 FPS under the comparison protocol. These results show that structured correction prediction provides an effective and practical alternative to dense RGB reconstruction for underwater image enhancement.
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All authors Cunhao Zhu, Dongliang Xu, Xiangtao Kong, Xiaoyan Lu, Tianyu Wang, Yue Yao
Computer Vision · Cross-listed from cs.RO
SocialHumanoid: Towards Expressive Humanoid Behavior via One-Step Co-Speech Motion Generation
Chengqun Yang, Tengjie Zhu, Liang Xu et al. (+10 more)
Humanoid
robots are increasingly expected to serve as embodied social
agents that communicate naturally with humans through face-to-face interaction. During such communication, humanoid
robots require body behaviors that are synchronized with
speech, affectively expressive, and suitable for real-time execution. However, existing co-
speech methods are primarily developed for digital humans and lack joint support for affective control and low-latency continuous generation on physical embodiments. To bridge this gap, we present SocialHumanoid, a system for expressive humanoid behavior via one-step co-
speech motion generation. Given response
speech and a specified affective condition, SocialHumanoid generates each full-body motion window in a single forward pass and connects successive windows through motion-history conditioning. The generated human motion is further converted online into embodiment-compatible
robot references and tracked by a whole-body controller for physical execution. To provide explicit supervision for affective body expression, we further introduce AffectMoCap, a 4-hour
dataset captured from two professional actors, containing synchronized
speech, body motion, fine-grained hand motion, and emotion annotations. On BEAT2, SocialHumanoid achieves the best FGD among the compared generation methods, competitive
speech-motion synchrony, and approximately $6\times$ faster inference than GestureLSM under the same protocol. Perceptual evaluations further show that training with AffectMoCap improves affect recognition from generated body motion, while real-
robot experiments demonstrate continuous affect-conditioned behavior and stable long-horizon execution. Our project page is https://rex0191.github.io/SocialHumanoid/.
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All authors Chengqun Yang, Tengjie Zhu, Liang Xu, Fulong Liu, Guanzhu Ren, Yitong Xing, Xuefeng Lu, Fei Shi, Siyuan Fan, Weijie Dong, Yao Mu, Xiaokang Yang, Yichao Yan
Computer Vision
LoopTrack: A Simple Baseline for Parameter-Efficient Transformer Tracking
Liang Peng, Chenxiao Li, Libo Zhang et al. (+2 more)
Current
Transformer-based tracking methods typically stack multiple
Transformer blocks with separate parameters to model interactions between the target template and the search region for target localization. These trackers often incur substantial parameter overhead from stacked blocks, making their deployment on resource-limited devices difficult. To address this, we propose a parameter-efficient
Transformer tracking framework, dubbed LoopTrack, which repeatedly applies a set of
Transformer blocks with shared parameters to interact features in a looped architecture for tracking, significantly reducing the number of parameters. To further exploit target cues, we present two lightweight designs, including target-aware looping (TAL) and gated target
memory (GTM). The former applies intermediate target information generated by one loop to guide feature interaction in the subsequent loop, enabling progressive feature refinement, while the latter maintains a compact
memory across frames, which is incorporated into the loop process to provide long-term information to the tracker, mitigating temporal drift in tracking. Compared to existing
Transformer trackers, LoopTrack enables multiple rounds of feature interaction with fewer model parameters, making it resource-friendly for deployment. In extensive experiments on multiple
datasets, LoopTrack shows a favorable accuracy-parameter trade-off. In particular, our LoopTrack$_{\rm One}$, with a single shared
Transformer block, achieves 66.2% SUC score on LaSOT with only 3.4M parameters, while LoopTrack$_{\rm Three}$, using three shared blocks, achieves 69.3% SUC score with 6.4M parameters, surpassing existing parameter-efficient tracking methods with comparable or larger model size. With LoopTrack, we aim to establish a simple yet strong baseline for parameter-efficient
Transformer tracking. Our code and models will be released.
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All authors Liang Peng, Chenxiao Li, Libo Zhang, Xingping Dong, Heng Fan
Computer Vision
Relevance Does Not Imply Applicability: Experience Activation for Personal GUI Agents
Fuyao Zhang, Xuan Wang, Zherui Li et al. (+3 more)
Personal Graphical User Interface (GUI)
agents rely on interaction history to infer what a user wants from ambiguous instructions and to anticipate recurring routines. Existing approaches retrieve task-relevant history and append it to the policy's context, implicitly assuming that experience relevant to a task remains useful for each decision within it. We find that this help is largely spent at the first decision: retrieved history strongly improves the opening step of an episode, yet provides little sustained benefit over the remaining 90% of steps, and offers weak guidance on whether a proactive suggestion is warranted. A relevant record may tell the
agent where to begin, but not which past action applies to the current screen or whether a routine is due now. The underlying issue is that relevance does not imply applicability}: relevance is determined at the task level, whereas applicability depends on the situation at decision time. We therefore recast personalization as experience activation and introduce ExpActivator, a training-free framework that activates only the experience applicable to the current situation. During execution, ExpActivator matches each new screen to historical states in the frozen GUI backbone's latent space and supplies the corresponding action as a reference. Before execution, it activates a recurring intent only when the current time and scenario provide sufficient support, and otherwise abstains. Across four GUI backbones, ExpActivator improves within-trajectory step success by 28% on average, achieves the best personalized execution on every backbone while using about one-fifth as many history
tokens, and reaches approximately 2.3$\times$ the Matthews correlation coefficient of the strongest proactive baseline. Experience pays where it is activated, not where it is appended.
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All authors Fuyao Zhang, Xuan Wang, Zherui Li, Jiaming Zhang, Longtao Huang, Wei Yang Bryan Lim
Computer Vision
Informative Viewpoint Selection for Episodic-Memory Embodied Question Answering using Omnidirectional Images
Kaname Kitamura and Asako Kanezaki
Embodied Question Answering (EQA) requires
agents to answer natural
language questions about surrounding environments from visual observations. In this work, we focus on open-vocabulary episodic-
memory EQA (EM-EQA), where an
agent answers free-form questions using recorded observation histories. Omnidirectional images are promising for this task, as they provide wide field-of-view observations that can capture surrounding context without requiring explicit camera rotations. However, omnidirectional images introduce two challenges for EQA: (i) equirectangular projection causes severe
geometric distortion that degrades
vision-
language model (VLM) recognition accuracy, and (ii) feeding equirectangular images directly into VLMs introduces excessive irrelevant background information, reducing answer accuracy and increasing the visual-
token burden. To address these challenges, we propose a viewpoint selection method for EM-EQA using omnidirectional images. Our method converts equirectangular observations into perspective views via cubemap projection, estimates question-conditioned relevance with fine-tuned BLIP-2, and selects informative and diverse viewpoints through diversity-aware greedy selection. Experiments on the Habitat-Matterport 3D (HM3D) subset of OpenEQA show that our method achieves state-of-the-art model performance among the reported model results with equirectangular observations. Moreover, after removing rotation views, which reduces observation frames by 65.5%, our method largely maintains its answer accuracy.
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Computer Vision
InfoEdit: Probing Global Layout Reasoning in Infographic Editing
Cheng Yang, Chufan Shi, Huijuan Wang et al. (+6 more)
Multimodal foundation models edit natural photographs at production quality, yet the same models struggle with structured visual content such as infographics. Unlike photographs, infographics encode information through logical relations; editing one element often requires surrounding elements to be adapted. We refer to this global layout
reasoning capability as reflow. Existing image-editing
benchmarks neither provide a dedicated setting for structured visual content nor evaluate the reflow capability. We introduce InfoEdit, a novel
benchmark of 1,000 infographics across eight logical-relation families, paired with 4,000 editing instructions across four editing tasks, and a reflow-aware evaluation protocol. Across eight frontier editors, only GPT-Image-2 clears 60% average success rate; most models fall below 7%, and no editor exceeds 36% on the Swap-Block task even with perfect target localization. We further show that code-level editing can match the strongest pixel-level editor, revealing complementary strengths across tasks. InfoEdit identifies reflow as a central challenge in structured visual content editing and provides a diagnostic
benchmark to facilitate future progress.
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All authors Cheng Yang, Chufan Shi, Huijuan Wang, Bo Shui, Yaokang Wu, Muzi Tao, Yibo Yan, Xuezhe Ma, Taylor Berg-Kirkpatrick
Computer Vision · Cross-listed from cs.CL
The Text Beside the Image: Detection, Utility and Leakage for Trustworthy Multimodal Medical Data and Beyond
Andreas Maier, Monica Hinrichs-Mayer, Franziska Weber et al. (+4 more)
Medical images are released with the reports that describe them, and protecting the image does not protect the report. This paper measures the text component of such releases. We measure identifier detection, downstream utility and residual identity leakage on the same documents, with the pseudonymisation policy as the variable under test: 15 detectors, three release conditions and four corpora of medical reports, legal judgments, news and other genres, and e-mail, in German, English, Chinese and Arabic. A fixed 13-detector union reaches a person sensitivity of 0.9998 at specificity 0.8686 on the medical reports, 0.9958 at 0.8504 on the legal judgments, 0.9352 at 0.9318 on news and other genres, and 0.9906 at 0.6235 on e-mail. With this ensemble, frequency matching with a public name list recovers zero identities by
alignment across the four corpora; the names it got right were ones the detector missed, left in clear text. Cross-document linkage ranks the correct person first for 0.93% of e-mail queries without training and 3.94% with it, against 1/3697 chance and 71.98% on unmodified text. On the medical reports it recovers nothing without training and 0.71% of 138 queries with it, against 1/207 chance and a 2.73% ceiling on unmodified text.
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All authors Andreas Maier, Monica Hinrichs-Mayer, Franziska Weber, Niklas Lackner, Matthias May, Bernhard Kainz, Siming Bayer
Computer Vision · Cross-listed from cs.RO
Q-WAM: 4-Bit Quantization of World Action Models with Action-Subspace Protection
Arash Akbari, Arman Akbari, Jingwu Luo et al. (+7 more)
World Action Models (WAMs) jointly generate
video and
robot actions through iterative
diffusion and perform strongly in robotic manipulation. However, their prohibitive compute and
memory costs pose substantial deployment challenges. Post-training quantization (PTQ) can reduce these costs, but existing PTQ methods such as smoothing and rotation are insufficient to maintain the precision of action generation. To overcome this limitation, we propose Q-WAM, a new 4-bit weight-activation quantization for WAMs that preserves the actions the model generates. Specifically, we introduce the Action Observability Gramian (AOG), which measures how much rounding errors in each weighted combination of a layer's input channels change the final action through all denoising steps. We also develop Action-Subspace Protection (ASP), which keeps the few most action-sensitive channel combinations in a tiny 16-bit low-rank branch and quantizes the complementary weights and activations to 4 bits, both as dense
matrix multiplications that run efficiently on GPUs. Finally, to preserve action quality with minimal overhead, we identify the experts that matter most for the generated action by aggregating the AOG-derived action mass across the layers of each expert and apply ASP only to those experts. We evaluate Q-WAM on three WAMs, both in simulation and in real-world deployment. On the RoboTwin 2.0
benchmark, it reaches 89.6–93.0% average success rate, within 1.1 percentage points of the 16-bit models, while reducing the
memory of the quantized blocks by 3.1–3.4$\times$. Our method outperforms the strongest baseline, SVDQuant, by 2.5–8.7 percentage points. On a Unitree G1 humanoid and a bimanual UR3
robot, it improves success over SVDQuant by 12.8-17.6 percentage points.
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All authors Arash Akbari, Arman Akbari, Jingwu Luo, Yuhao Lei, Yi Gao, Weiwei Chen, Xuan Zhang, Zhenman Fang, Geng Yuan, Yanzhi Wang
Computer Vision
VehDyn: A Driving World Model Benchmark for Vehicle Dynamics
Tianyi Wang, Wangsheng Du, Jiazhou Chen et al. (+12 more)
Video world models are emerging as data engines, action planners, and generative simulators for autonomous driving, but existing
benchmarks primarily assess visual fidelity and coarse physical plausibility, providing limited evidence on whether generated driving futures obey realistic vehicle kinematics and dynamics. This limitation is further compounded by the lack of
datasets in which vehicle, road, maneuver, and speed conditions are independently controlled, and ground-truth vehicle states are recorded in synchrony with
videos. We introduce VehDyn, a driving world model
benchmark for vehicle dynamics. VehDyn is built on a CARLA-CarSim co-simulation platform where photorealistic rendering is coupled with a validated multi-body dynamics model, and it contains 10,080 configurations from a full factorial design over five vehicle types, four tire-road friction coefficients, three maneuvers, four target speeds, 14 scenes, and three illuminations, each paired with synchronized position, velocity, and attitude sequences. Built on this
dataset, VehDyn introduces a hierarchical evaluation framework that measures trajectory
alignment, kinematic consistency, and dynamic consistency, and
benchmarks 12 state-of-the-art
video world models. We further assess the
video quality using two established protocols and correlate it with the VehDyn score. Trajectory-level metrics are nearly saturated, with ten of twelve models within 20% of ground truth, while no model reaches 92% of ground truth on dynamic consistency, and visual-quality metrics are only weakly correlated with vehicle-dynamics fidelity. DrivingWorld achieves the highest VehDyn score, followed by Cosmos 3 Nano and LTX-
Video 2.5, and the VehDyn score agrees closely with human judgment. VehDyn provides a systematic foundation for developing driving world models that are physically consistent and visually realistic.
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All authors Tianyi Wang, Wangsheng Du, Jiazhou Chen, Tianyi Zeng, Xiangyu Li, Jiseop Byeon, Yujin Wang, Yiming Xu, Yangyang Wang, Bingzhao Gao, Sikai Chen, Zhaomiao Guo, Junfeng Jiao, Christian Claudel, Alexandre Bayen
Computer Vision
EngIntervene: Benchmarking Multimodal Engineering State Understanding and Design Intervention Reasoning
Jinchang Zhang, Yingda Tao, Jiakai Lin et al. (+1 more)
Multimodal engineering
benchmarks largely evaluate static understanding, such as recognizing components, interpreting diagrams, or answering technical questions. This leaves a missing middle between engineering perception and full design generation: whether a model can use an understood system state to reason about relations, constraints, and the consequences of design changes. We introduce EngIntervene, a
benchmark for this capability. It contains 3,229 questions across seven engineering domains and organizes evaluation into four levels: state grounding (T1), relational and mechanistic
reasoning (T2), constraint-aware diagnosis (T3), and intervention
reasoning (T4), which asks whether a proposed modification achieves its target while preserving required constraints. The tasks instantiate a unified engineering state representation spanning objects, relations, constraints, and design objectives, and T2–T4 are scored against structured reference answers with atomic criteria. Across open- and closed-weight
multimodal models, stronger grounding does not reliably translate into better diagnosis or intervention, and the best open-weight model trails the best closed model by 14.7 percentage points on the T2–T4 average. Removing or shuffling visual evidence consistently degrades performance, while
benchmark-specific supervised fine-tuning improves T1 but not T2–T4. T4 further exposes a large gap between satisfying individual revision criteria and producing a fully valid intervention. Engineering
reasoning thus requires not only recovering the current state, but also reliably using it to reason about constraints and post-intervention consequences. Code and
benchmark artifacts are available at https://github.com/changcv2021/EngIntervene
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All authors Jinchang Zhang, Yingda Tao, Jiakai Lin, Guoyu Lu
Computer Vision · Cross-listed from cs.RO
PORTER: Edge-Cloud Residency for Persistent 3D Scene Graph Memory
Yue Chang, Yifan Tian, Jiajing Peng et al. (+5 more)
Recent task-driven and just-in-time 3D Scene Graph (3DSG) methods reduce per-task representations by constructing or activating only task-relevant information. Yet sparse per-task working sets do not bound onboard
memory usage over a
robot's lifetime: as tasks change, payloads accumulated for earlier tasks may become irrelevant to the current task but can be useful again in future tasks. Over repeated task switches and expanding environments, retaining such reusable payloads causes local
memory to grow, whereas discarding them entirely can lead to costly repeated construction of the same payloads later. We introduce PORTER, which decouples persistence from residency: lightweight anchors remain in the limited
memory of the edge
robot while heavy object payloads migrate between the edge and the cloud. Relevance alone is insufficient for deciding residency because multiple relevant payloads may provide redundant information. We therefore decompose each task into functional requirements and introduce Irreplaceable Support Erasure (ISE), which measures the loss in requirement coverage caused by offloading. ISE discounts replaceable support and penalizes losses more strongly when the remaining coverage of a requirement is weak. PORTER constructs a budget-aware local working set by repeatedly offloading the payload with the smallest marginal ISE per byte. Experiments on JITOMA-Bench evaluate PORTER across four 3DSG builders. Under progressive compression, pooled relative mR@3 remains at 100% through 91% payload-byte offloading.
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All authors Yue Chang, Yifan Tian, Jiajing Peng, Dazhi Huang, Rufeng Chen, Zhaofan Zhang, Li Chen, Sihong Xie