Computer Vision
BMFA: Boundary-Minority Free-Energy Adaptive Screening
Wenyan Xu and Alizer Wong
Vision Transformers process spatially redundant
tokens efficiently only when coarse
token summaries preserve the evidence required by exponential
attention aggregation. We identify a boundary-minority underestimation failure in which a spatially small, high-response region contributes dominant Gibbs mass while remaining nearly invisible to a block mean. We formalize the failure through the discrepancy between normalized log-mean-exp free energy and mean summarization, prove that minority Gibbs mass can remain non-vanishing as its spatial support and mean contribution vanish, and characterize the limitations of finite-order moment corrections. Building on the resulting analysis, we introduce Boundary-Minority Free-Energy Adaptive Screening (BMFA), which constructs a hierarchical piecewise-constant approximation and recursively refines blocks according to a computable lower-bound increment of local free energy. Controlled synthetic tests, COCO and LVIS diagnostic probes, closed-loop DeiT-Tiny evaluations, and ImageNet-1K experiments establish a consistent evidence chain. BMFA reduces the mean synthetic underestimate from 2.582 to 0.261 at a 5.794% leaf ratio, lowers the COCO image-edge mean gap from 2.254 to 0.526, and preserves 71.520% ImageNet Top-1 accuracy at a 55.861% leaf ratio. The current prototype evaluates selection quality after full QK computation; the reported leaf ratio therefore characterizes representation granularity rather than verified sparse-kernel speedup.
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Computer Vision
LFM: Leveraging Foundation Models for Source-Free Universal Domain Adaptation
Jing Li, Pan Liu, Meng Zhao et al. (+7 more)
Source-free universal domain adaptation (SF-UniDA) adapts a pre-trained source model to an unlabeled target domain under both
covariate and label shifts, without access to source data. However, existing SF-UniDA methods rely on inefficient techniques such as threshold tuning and clustering. Foundation models (FMs), known for their generalization and zero-shot capabilities, remain underexplored in SF-UniDA. In this paper, we propose a framework that leverages foundation models (LFM) for SF-UniDA. We use a
vision-
language model (VLM) to compute similarities between target samples and text labels, including those for unknown classes generated by prompting a large
language model. The label shift type is determined by analyzing the coefficient of variation of a similarity-based sample-level score. Unknown samples are identified using a binary Gaussian mixture model fitted to another similarity-based metric. Under a consensus strategy, the pseudo-labels generated by the VLM are refined by the target model initialized with the pre-trained source model, integrating knowledge from both the source domain and foundation models. Finally, these refined pseudo-labels are used to train the target model. Extensive experiments across all possible label shifts and multiple
benchmarks demonstrate the effectiveness and superiority of our proposed LFM framework. Our code is available at https://github.com/iamjingli/LFM.
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All authors Jing Li, Pan Liu, Meng Zhao, Wanli Xue, Yanhong Yang, Xu Cheng, Fan Shi, Jianhua Zhang, Qinghua Hu, Shengyong Chen
Computer Vision
Direct Clinical Joint Angle Extraction from Parametric Body Model Rotation Matrices
Joey Páolo Kardolus, Daan Hendriks and Jaap Jansen
Quantitative joint angles are rarely available in routine care because the tools are slow, costly, or confined to a laboratory. We show that clinical joint angles can be read directly from the per-segment rotation
matrices a parametric body model already produces, with no inverse-kinematics or musculoskeletal-model fitting step. On the OpenCap LabValidation cohort, using the GEM-X body-model
estimator on single-smartphone
video, our pooled mean absolute error is 4.50 degrees over the fifteen joint angles that match the OpenCap
Monocular reference set, the same accuracy range as OpenCap
Monocular's 4.8 degrees on the same cohort and reference standard, from a much simpler pipeline. The step that connects a body model to clinical angles is a small calibration table rather than an optimisation, so the same procedure transfers unchanged to other body models: repeating it on SAM 3D Body, changing only the table, gives 4.66 degrees, statistically indistinguishable from GEM-X, and runs in real time from a live single-camera stream. The method needs no per-recording inputs beyond the
video itself: no participant height, no camera-intrinsics database, no per-subject model scaling. This broadens where movement analysis is practical, from in-clinic and at-home recording to telerehabilitation and large-scale decentralised studies.
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Computer Vision
Reviving Ancient Paintings via Poem: A Colorization Framework for Aligning Cultural Semantics
Junming Gao, Biao Zhu, Xiaosong Wang et al. (+1 more)
The irreversible fading of ancient paintings disrupts the "congruence between poems and paintings", a core aesthetic principle where visual imagery harmonizes with literary inscriptions. Although
diffusion models provide strong generative priors, restoring historically faithful colors remains difficult: visual restoration is inherently ambiguous, while direct text guidance often causes modern semantic bias, over-saturation, and cross-boundary color leakage. To address this, we propose PoemColor, a poem-guided ancient painting colorization framework. Our method aligns poetic cultural semantics with painting restoration through two key designs. First, the Poetic Painting Projector (P3) converts implicit poetic context into a classical color-aware condition via poem-to-palette pretraining, reducing the ambiguity of poem-to-color mapping. Second, Structure-Aware Semantic
Attention (SASA) regulates how poetic color semantics are injected into the
diffusion backbone by jointly controlling their propagation direction and regional injection strength. In addition, we construct a hybrid restoration
dataset that integrates synthetic degradation with expert-restored artifacts, providing both scalable supervision and real classical color references. Extensive experiments demonstrate that our framework significantly outperforms state-of-the-art methods, delivering controllable colorization that revives both historical authenticity and poetic semantics.
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All authors Junming Gao, Biao Zhu, Xiaosong Wang, Tan Tang
Computer Vision
MixDiffusion: Mixing Diffusion-based Uni-condition Text-to-Image Generation Models for Multi-condition Image Synthesis
Pengcheng Wan, Liang Han, Lin Xu et al. (+2 more)
Recent advances in text-to-image (T2I) generation have enabled controllable image synthesis by incorporating conditions beyond text. However, most existing
diffusion-based methods are limited to a single type of control condition (e.g., bounding boxes or keypoints), which restricts their flexibility. To address this limitation, we propose MixDiffusion, a training-free
diffusion framework for multi-condition T2I generation. MixDiffusion theoretically supports an arbitrary number of control conditions, including bounding boxes, keypoints, sketches, depth maps, reference images, and text, by collaboratively integrating multiple pre-trained uni-condition
diffusion models. The key insight of the proposed approach is to derive the predicted noise distribution in each denoising step of the
diffusion-based multi-condition image generation model from the predicted noise distributions of multiple
diffusion-based uni-condition models with a derived integration formula, which is supported by rigorous theory proof. Owing to its training-free nature, MixDiffusion is easy to deploy and readily extensible to new control modalities.
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All authors Pengcheng Wan, Liang Han, Lin Xu, Bowen Xiao, Liqiang Nie
Computer Vision
Brain-Aligned Multi-Stream Video Transformers with Sparse Self-Selection
Amir Hosein Fadaei, Mahyar Maleki and Mohammad-Reza A. Dehaqani
Modern
video transformers typically ignore principles from primate
vision and are rarely evaluated against neural data, limiting their biological interpretability. We introduce a sparse winner-takes-all
token selection module that replaces dense self-
attention to improve efficiency and approximate competitive routing observed in biological visual
circuits. We further propose a neuro-inspired split-and-fuse
video transformer which uses two complementary pathways: a high-resolution, low-frame-rate "what" stream and a low-resolution, high-frame-rate "where" stream, fused before classification. On Kinetics-400 and Something-Something V2, our best variant operates on the Pareto frontier of accuracy versus inference time among models of comparable scale and pretraining, and showing improved robustness to spatial perturbations. Using representational similarity analysis between model
embeddings and time-resolved
EEG recordings for the same
video stimuli, our model attains a peak
brain-model correlation of 0.18 (about 78% of the noise ceiling) and consistently outperforms strong
video transformer baselines, suggesting that pathway specialization and sparse competition are useful inductive biases for efficient,
brain-aligned
video understanding.
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Computer Vision
Rarity-Aware Discrete Diffusion with Spatially Consistent Decoding for Photo-Realistic Image Super-Resolution
Ao Li, Yapeng Du, Yi Xin et al. (+5 more)
Continuous
diffusion models have become the dominant paradigm for photo-realistic image Super-Resolution (SR), but they typically formulate reconstruction as continuous signal-level denoising and incorporate semantic priors through external conditioning modules. This makes it less direct to exploit the unified
token-based scaling paradigm of modern
multimodal models. Autoregressive models provide a more native semantic representation by modeling images as discrete visual
tokens, yet their causal decoding is inefficient for high-resolution reconstruction. Discrete
diffusion offers a promising middle ground by enabling non-causal, parallel prediction over visual
tokens. However, directly adapting discrete
diffusion to SR remains non-trivial due to two task-specific challenges: (1) the long-tailed distribution of visual
tokens, which under-represents rare but perceptually critical textures; and (2) spatially inconsistent parallel decoding, which may introduce isolated artifacts. To address these issues, we propose DiMOO-SR, a rarity-aware
multimodal discrete
diffusion framework for photo-realistic image SR. During training, Inverse Frequency Sampling (IFS) prioritizes under-represented but information-rich
tokens. During inference, Spatial Consistency Ranking (SCR) refines
token confidence using local neighborhood agreement to improve structural coherence. Extensive experiments on widely used real-world SR
benchmarks demonstrate that DiMOO-SR achieves competitive perceptual quality with only a few parallel decoding steps, highlighting the potential of discrete
diffusion for generative image super-resolution. The code will be released upon publication.
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All authors Ao Li, Yapeng Du, Yi Xin, Lei Zhu, Le Zhang, Guangtao Zhai, Ce Zhu, Xiaohong Liu
Computer Vision
Coarse-to-fine Framework for Generative MEF via Implicit Neural Representation
Sangmin Han, Jinho Kim, Jinwoo Kim et al. (+2 more)
Multi-exposure fusion (MEF) expands the luminance range beyond what a single exposure can capture. Combining images taken at different exposure levels requires handling
geometric differences while naturally merging their complementary brightness information. It often demands generative completion where details are missing.
Diffusion-based generative methods address these challenges, however, they are computationally expensive and struggle to preserve fine structures in saturated regions. We propose LIIFusion, a coarse-to-fine framework that balances fusion quality and efficiency in generative MEF. The coarse stage performs low resolution generative fusion, enhanced by an adaptive exposure correction that recovers structure lost in saturated over-exposed areas. The fine stage adapts a local implicit image function into a multi-exposure fusion function: conditioned on the HR OE/UE sources and the coarse output, it queries arbitrary target coordinates and fuses source evidence regardless of the HR input resolution. LIIFusion achieves up to 3.5$\times$ speed-up over existing generative methods while maintaining or improving structural fidelity and perceptual quality. We believe this framework provides an effective pathway toward making generative MEF more practical in real-world applications.
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All authors Sangmin Han, Jinho Kim, Jinwoo Kim, Dongyoung Kim, Seon Joo Kim
Computer Vision
Semantic Color Naturalness Breaker: Preventing Illegitimate Colorization via Content-Aware Color Priors
Yuki Nii, Futa Waseda, Ching-Chun Chang et al. (+1 more)
Automatic image colorization enables large-scale and low-cost reuse of grayscale media (e.g., manga panels and archival photographs), facilitating unauthorized reuse and redistribution. Once released online, grayscale content can be readily turned into unauthorized colorized derivatives using off-the-shelf models, creating a practical need for proactive, content-side protection at publication time. Building on Uncolorable Examples (UE), which add imperceptible perturbations to released grayscale images to degrade unauthorized colorization, we propose Semantic Color Naturalness Breaker (SCNB) – a semantic-level UE framework that drives colorization outputs toward content-inconsistent colors while preserving the visual fidelity of the released grayscale media. We further introduce Content-aware Color Distributional Distance (CaCDD), a ground-truth-free, content-aware measure of color plausibility derived from semantic color priors, used both as the optimization objective of SCNB and as an evaluation metric. Experiments on ImageNet show that our method remains effective under small perturbation budgets and common post-processing, supporting practical deployment in real-world content-sharing pipelines.
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All authors Yuki Nii, Futa Waseda, Ching-Chun Chang, Isao Echizen
Computer Vision
ConsiSpace: Learning Geometric Consistency Matters for Video Spatial Reasoning
Ting Huang, Zhenyu Zhang, Wenyuan Huang et al. (+2 more)
Video spatial
reasoning is essential for
navigation-oriented perception and long-
video question answering, where models must infer spatial relations across long horizons under changing viewpoints. However, existing
multimodal large
language models (MLLMs) remain largely semantic-centric, and often fail to reliably aggregate consistent spatial evidence from redundant
video observations, leading to inefficient or unstable
reasoning. To address these issues, we propose ConsiSpace, a geometry-consistency-aware framework for geometry-sensitive
video spatial
reasoning that turns spatial consistency into both an evidence organization principle and an explicit post-SFT learning signal. We build a geometry-consistent
memory (GCM) including implicit evidence
tokens and explicit
geometric cues, and leverage efficient organization strategies to compactly preserve task-related spatial evidence. Furthermore, we utilize unified consistency self-supervised reinforcement learning (UC-SSRL) after supervised fine-tuning to improve cross-view stability, with answer-, metric-, and
topology-consistency rewards. Extensive experiments on three spatial-
reasoning benchmarks, VSI-Bench, OSI-Bench, and MMSI-
Video-Bench, show consistent gains, improving the average score by 12.6 points over the strongest baselines.
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All authors Ting Huang, Zhenyu Zhang, Wenyuan Huang, Jian Yang, Hao Tang
Computer Vision
Miles: Metric Learning with Expandable Subspace for Pre-Trained Model-Based Class-Incremental Learning
Kai Jiang, Zisong Lin, Hongyuan Zhang et al. (+2 more)
Class Incremental Learning (CIL) aims to learn new concepts consistently from a data stream without forgetting. Unlike typical CIL methods which need to learn a model from scratch, pre-trained model (PTM) can easily adapt to a new task with fine-tuning. However, existing PTM-based CIL methods fail to achieve a trade-off between performance and computational expenditure, i.e., they either adopt the same parameter space so that leading catastrophic forgetting, or expand a new branch for each task but adding more computational cost. To this end, we propose MetrIc Learning with Expandable Subspace (Miles) to harness the prior information within pre-trained knowledge, thereby orchestrating an efficient expansion of the parameter space through guided optimization. Specifically, it decouples the learnable modules with the pre-trained model, exploiting prior information from intermediate features of the backbone network to enable more flexible parameter expansion. Then, a central loss is adopted to guide the new category to cluster towards the corresponding prototype in the new task subspace while incorporating an auxiliary distance regularization term to maintain metric
equilibrium across tasks. Extensive experiments on six
benchmark datasets demonstrate that Miles achieves state-of-the-art performance in various CIL settings.
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All authors Kai Jiang, Zisong Lin, Hongyuan Zhang, Xueru Bai, Xuelong Li
Computer Vision
Renye Yan, Jikang Cheng, You Wu et al. (+8 more)
Latent
diffusion models (LDMs) enable efficient high-resolution image synthesis by denoising in a VAE-compressed latent space. However, fixed visual tokenizers can discard fine textures and structural details, while separate representation and
diffusion training creates a mismatch between reconstruction and generation objectives. These limitations have renewed interest in pixel-space
diffusion, which models raw pixels directly, removes the VAE bottleneck, and supports end-to-end optimization. This formulation better matches the demands of high-fidelity generation but introduces challenges in high-dimensional modeling, including noise scheduling, loss weighting,
token efficiency, and scalable architecture design. Pixel-space modeling also offers a promising basis for unified
multimodal systems: raw pixels, text, and task conditions can be represented in a shared
token space and jointly processed by a single
Transformer, narrowing the gap between visual understanding and generation. This paper reviews Pixel-Space
Diffusion Transformers (pDiTs) from the perspectives of model architecture, continuous generative mechanisms, and unified
multimodal modeling. We summarize representative methods, identify key technical challenges, and discuss future directions toward high-fidelity, end-to-end
vision foundation models that integrate generation and understanding.
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All authors Renye Yan, Jikang Cheng, You Wu, Ling Liang, Wei Peng, Athanasios V. Vasilakos, Qingyu Zhao, Yu Zhang, Yimao Cai, Kilian M. Pohl, Guoying Zhao
Computer Vision
Scalable Model-Assisted Multi-Target Estimation in Large Image Collections
Max Hamilton, Jinlin Lai, Daniel Sheldon et al. (+1 more)
Computer
vision models are increasingly used as measurement tools to estimate population-level quantities from large image collections, but prediction errors introduce bias and the resulting estimates lack statistical guarantees required in scientific applications. Prior work uses a Monte Carlo framework to combine model predictions with ground-truth annotations by sampling some images for humans to label and is able to provide unbiased estimates with controllable accuracy, but primarily addresses single-scalar estimation. We study the more general problem of multi-target estimation, where many quantities (e.g., class counts or proportions) must be estimated simultaneously, and adapt sampling and estimation strategies from survey sampling to this setting. Evaluations on five detection and
segmentation datasets with 7-80 classes show that importance sampling excels with moderate annotation budgets or fewer targets, whereas uniform sampling with control variates is superior when estimating many targets or operating with minimal labels. Additionally, a subset-based ratio
estimator remains highly competitive across all regimes. Ultimately, our framework effectively combines biased model predictions and limited human labels into rigorous scientific measurements.
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All authors Max Hamilton, Jinlin Lai, Daniel Sheldon, Subhransu Maji
Computer Vision · Cross-listed from cs.LG
JAGG: Jacobian-Aggregated Group Gradient for Efficient GRPO Training of Diffusion Models
Ruiyi Ding, Jie Li, He Kang et al. (+3 more)
Group Relative Policy Optimization (GRPO) is a powerful reinforcement learning
algorithm for aligning generative models with human preferences. While successful in large
language models~\cite{shao2024deepseekmathpushinglimitsmathematical}, its extension to
diffusion and flow matching models introduces a severe computational bottleneck: gradients must be back-propagated through the high-capacity DiT backbone at every timestep of the sampling trajectory, making high-resolution text-to-image (T2I) training prohibitively expensive. Training-free DiT inference acceleration methods (e.g., $\Delta$-DiT, ScalingCache) exploit the fact that DiT hidden states and velocity predictions vary smoothly and nearly linearly along the trajectory. We ask whether the same linearity can reduce the backward-pass cost of DiT RL training, and answer affirmatively with JAGG (Jacobian-Aggregated Group Gradient), which reduces full
transformer backward passes from $W$ to $2$ per group of $W$ consecutive steps. JAGG approximates intermediate-step Jacobians via $t$-weighted interpolation of the endpoint Jacobians, then aggregates per-step upstream signals into two composite gradients applied through a single joint backward pass. We prove this interpolation is exact when the velocity is linear in $(z,t)$, and a cosine-similarity routing rule (jagg\_frac) deploys JAGG only where the assumption holds. Experiments on T2I
benchmarks show JAGG delivers $\sim$2$\times$ backward speedup with negligible quality degradation. The code for this work can be accessed through https://github.com/SchumiDing/JAGG.
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All authors Ruiyi Ding, Jie Li, He Kang, Ziyan Liu, Chengru Song, Yuan cheng
Computer Vision
FlexiGrad: Adaptive Gradient Modulation for Hierarchical Fine-Grained Classification
Zilu Zhou, Dongliang Chang, Junhan Chen et al. (+1 more)
Many fine-grained recognition tasks contain hierarchical labels such as order, family and species. Although this supervision should be beneficial, jointly optimising all levels often leads to unstable training because coarse and fine classifiers impose inconsistent gradients on the shared backbone. This hierarchical gradient conflict prevents the model from learning a coherent coarse-to-fine representation. In this paper, we propose FlexiGrad, a simple and parameter-free method that regulates gradient interactions during backpropagation. FlexiGrad removes only the harmful conflicting component when tasks disagree and reinforces the shared direction when they partially agree through a smooth hierarchy-aware weighting function. This produces stable optimisation and preserves both global structure and fine-grained discriminative cues. FlexiGrad integrates into existing architectures without modification while improves multi-granularity accuracy on CUB-200-2011, FGVC-Aircraft and Stanford Cars. The code will be available at PRIS-CV/FlexiGrad.
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All authors Zilu Zhou, Dongliang Chang, Junhan Chen, Zhanyu Ma
Computer Vision
Hierarchy-Aware and Anatomy-Guided Learning for Lung Ultrasound Video Classification
Alya Almsouti, Lotfi Mecharbat, Noha Aboukhater et al. (+5 more)
Lung ultrasound (LUS) is a bedside tool for assessing pulmonary edema in patients at risk due to heart failure or impaired kidney function. However, automated LUS analysis remains challenging because of speckle noise, imaging artifacts, and operator-dependent acquisition variability. In this work, we present a deep learning framework for multi-class LUS
video classification that explores two components: hierarchy-aware training, and anatomy-guided learning. Starting from a strong baseline, we introduce hierarchical training strategies and then introduce pleural line mask supervision to guide model
attention toward anatomically relevant regions. We study four clinically relevant classes–healthy, B-lines, consolidations, and mixed B-lines with consolidations–using an open-access
dataset of 1,886
videos from 219 patients, evaluated with patient-level five-fold cross-validation. Results show that hierarchy-aware training improves pathological separation relative to flat classification, while mask-guided
attention supervision achieves the highest mean macro-F1 of 65.7% and produces more localized
attention patterns. Transfer experiments on the external COVID-BLUeS
dataset further show competitive and parameter-efficient adaptation while preserving pleural-focused
attention behavior. These findings suggest that combining clinically structured objectives with anatomy-guided supervision is a practical approach to robust, interpretable LUS
video analysis. Code and model implementations are available at https://github.com/Alya-Almsouti/LUS-
video-classification.
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All authors Alya Almsouti, Lotfi Mecharbat, Noha Aboukhater, Yousef Alabrach, Siddiq Anwar, Andre Kumar, Ibrahim Almakky, Mohammad Yaqub
Computer Vision
Thinking in Video: Can Video Generators Really Reason About the Real World?
Yongheng Zhang, Guang Yang, Ruihan Hou et al. (+12 more)
Recent advances in world models and
video generation have given rise to an emerging
reasoning paradigm that leverages
video generative models to simulate, predict, and reason about real-world dynamics. We redefine this paradigm as Thinking in
Video, where
video is not merely an output artifact but a medium for constructing, extending, and verifying causal thought. However, this promise remains unverified: convincing rollouts may reflect memorized appearances rather than causal understanding, while existing metrics separate perceptual fidelity from semantic logic. To evaluate whether
video generators support such
reasoning, we introduce the Causal-Generative Dual-Judge (CGDJ),
auditing World Model Consistency from two perspectives. Explicit Causal Perception tests whether a generator reads a
video scenario as a
reasoning problem through spatio-temporal flattened visual question answering, while Implicit Generative Perception-Prediction Gap evaluates whether it renders the causal consequence as a consistent future
video. Applying CGDJ to representative open- and closed-source generators reveals a clear Perception-Prediction Gap: open-source models produce plausible dynamics despite near-zero explicit causal perception, whereas advanced closed-source systems show stronger but still limited
alignment between
reasoning and generation. Further analysis exposes audio-visual misalignment, where models verbalize correct causal logic more reliably than they render it, challenging the "world simulator" narrative.
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All authors Yongheng Zhang, Guang Yang, Ruihan Hou, Qiguang Chen, Ziang Liu, Xiaolong Liu, Manman Zhang, Yanchao Hao, Zheng Wei, Hao Wu, Libo Qin, Peishan Dai, Yinghui Li, Di Yin, Xing Sun
Computer Vision
DecoyFace: Beyond Obfuscation via Controllable and Imperceptible Identity Misdirection for Privacy-Preserving Face Recognition
Zhihan Ren, Lijun He, Xinyao Wang et al. (+2 more)
Split face recognition reduces client-side computation but exposes intermediate features to feature inversion attacks and unauthorized analysis by honest-but-curious (HBC) servers. Existing
privacy-preserving face recognition methods mainly aim to resist unauthorized reconstruction, typically producing features whose inversion yields visibly degraded results, which may reveal the existence of protection and motivate adaptive attacks. To address this issue, we propose DecoyFace, an imperceptible decoy-oriented framework that steers unauthorized reconstruction toward a plausible but incorrect identity while preserving recognition utility. The key idea is to decompose the intermediate representation into a reconstruction-sensitive subspace and its complementary subspace. The client injects decoy identity cues into the reconstruction-sensitive subspace, while limited recognition-relevant evidence from the true sample is retained in the complementary subspace. On the server side, an authorized canonicalization module suppresses decoy-dominant components and recovers a recognition-friendly representation. This design addresses both attacker-side inversion from intercepted features and HBC server-side reconstruction from canonicalized representations. Experiments show that DecoyFace preserves competitive recognition accuracy while substantially reducing identity leakage to 2.93% under U-Net attacks and 0.74% under Flow-Matching attacks while yielding visually plausible and imperceptible reconstructions, with over 99.78% face validity on LFW
dataset.
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All authors Zhihan Ren, Lijun He, Xinyao Wang, Xinzhu Fu, Fan Li
Computer Vision
Generative Transmission: Rethinking Computation, Bandwidth, and Memory in Communication
Xiangyu Chen, Jixiang Luo, Yuankai Fan et al. (+3 more)
Under the AI Flow framework, communication is shifting from transmitting fidelity-oriented information flows toward delivering task-oriented and perception-oriented
token flows across heterogeneous network resources.
Video communication is a fundamental component of modern information networks. However, under ultra-low-bandwidth and weak-network conditions, conventional
video coding and transmission methods, which are primarily optimized for pixel-level fidelity, often struggle to balance visual usability, transmission efficiency, and robustness to unstable links. With the rapid advancement of generativemodels,
video communication is also moving from precise signal reconstruction toward receiver-side perceptual utility and system-level usability. In this paper, we propose Generative Transmission (GenTrans) for
video communication under ultra-low-bandwidth and weak-network conditions. Built upon Generative
Video Compression (GVC), GenTrans formulates
video transmission as a joint optimization problem involving bandwidth, computation, and
memory, rather than treating it merely as a signal coding task. By leveraging generative priors, cross-clip
memory reuse, runtime state reuse, and weak-network-aware transport, GenTrans significantly reduces transmission overhead while enabling visually coherent and practically useful reconstruction. Experimental results show that GenTrans supports effective
video transmission under ultra-low-bitrate and weak-network conditions, achieving improved transmission efficiency, decoding efficiency, and robustness while preserving perceptual quality.
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All authors Xiangyu Chen, Jixiang Luo, Yuankai Fan, Haibin Huang, Chi Zhang, Xuelong Li
Computer Vision
TraversRL: Traversable Pedestrian Pathway Generation With Reinforcement Learning
Bin Han, Robert Wolfe and Bill Howe
Automatically generating pedestrian pathways from aerial images requires producing a connected network suitable for routing, not just detecting where sidewalks appear. Sidewalks and crossings, in contrast to roads, may be partially occluded, implicitly defined, and exhibit complex connectivity patterns. Existing
segmentation-based approaches focus on labeling pixels to infer segments, but often produce disconnected or fragmentary graphs that are unreliable for
navigation. We introduce TraversRL, a
vision-conditioned model that iteratively grows a pathway network from an aerial image, simulating a traveler navigating the built environment. TraversRL uses an action space of short and long direction-distance segments designed to adapt to complex patterns and span occlusions, and uses a combination of graph-level and step-wise rewards to balance overall connectivity with precise edge placement. Across three visual backbones and three intersection
datasets, TraversRL substantially improves buffered IoU with the ground-truth graph relative to a state-of-the-art
segmentation baseline, and more than doubles metrics of connectivity. Moreover, combining global and local rewards produces cleaner graphs with fewer spurious branches while further improving overall performance. These results demonstrate that modeling pathway extraction as a sequential decision process from the perspective of a traveler, while optimizing for final graph quality with reinforcement learning, produces significantly more reliable pedestrian networks.
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Computer Vision
DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation
Siobhan Reid, Zhixiang Chi, Li Gu et al. (+3 more)
Few-shot Test-Time Domain Adaptation (FSTT-DA) seeks to adapt models to novel domains using only a handful of unlabeled target samples. This setting is more realistic than typical domain adaptation setups, which assume access to target data during source training. However, prior FSTT-DA approaches fail to effectively leverage source domain-specific knowledge, relying on shallow batch normalization updates,
prompt-based methods that treat the model as a black box, or ensembling strategies that do not capture cross-domain relationships. To address these limitations, we introduce a new FSTT-DA framework that integrates LoRA fine-tuning with model merging. In our approach, separate LoRA modules are fine-tuned on CLIP's
vision encoder for each source domain. Since LoRA modifies only a small fraction of the model's parameters, it retains the base model's generalized knowledge while internally learning domain-specific features. To adapt the learned knowledge to a specific target domain, we propose a hypernetwork trained via meta-learning that generates per-column merging factors to combine LoRA modules. Given a small batch of target images, the hypernetwork produces merging weights that fuse source LoRA modules into a single adapted representation. Our results demonstrate state-of-the-art performance across various domain adaptation
datasets. Our code is publicly available at https://github.com/nahbois4321/DA-MergeLoRA.
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All authors Siobhan Reid, Zhixiang Chi, Li Gu, Omid Reza Heidari, Ziqiang Wang, Yang Wang
Computer Vision
Bio-SFT: Asymmetric Cortical Guidance and Retinal Adaptation for Robust HDR Reconstruction
Tingyu Cheng, Ting Zhang, Chongyi Li et al. (+2 more)
Recovering high dynamic range (HDR) radiance from a single standard dynamic range (SDR) image is highly ill-posed. Extreme luminance variation and severe quantization in dark regions make accurate reconstruction challenging, often leading to visual artifacts and color distortions. To address this problem, we propose Bio-SFT, a bio-inspired spiking frequency
transformer for single-image HDR reconstruction. Bio-SFT incorporates three biologically motivated components. First, a learnable Naka–Rushton retinal adaptation frontend stabilizes the input under complex lighting conditions. Second, an explicit Parvo–Magno split introduces asymmetric Parvo-to-Magno guidance, allowing high-frequency structural cues to modulate low-frequency reconstruction. Third, an event-driven SNN hard gating module applies all-or-none spiking to suppress dark-region noise while preserving structural details. The module is trained with a sparsity prior to encourage efficient feature utilization. Built for end-to-end training within a
transformer backbone, these lightweight components provide strong parameter efficiency. Experiments on HDRTV1K show that Bio-SFT achieves competitive perceptual quality and consistently improves HDR-VDP-3 and $\Delta E_{ITP}$ while reducing artifact propagation in symmetric guidance pipelines.
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All authors Tingyu Cheng, Ting Zhang, Chongyi Li, Zhaoqing Pan, Tiesong Zhao