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ECCV 2026

Exploratory, Communicative, and Deployable: Vision-Driven Embodied Agents for Open-World Mobile Manipulation

Boyu Mi, Mengchen Ma, Yifei Yao, Xing Gao, Hanqing Wang, Junting Chen, Yangzi Li, Zihou Zhu, Guohao Li, Zhenfei Yin, Tai Wang, Yao Mu, Jiangmiao Pang

Real-world deployment of embodied agents requires activeexploration, visual grounding, and interactive intent disambiguation. How-ever, existing frameworks often rely on privileged simulator states or as-sume complete instructions, bypassing realistic deployment challenges.To bridge this gap, we present REAL, an agentic framework for open-world mobile manipulation. REAL establishes sim-to-real-consistent en-vironment APIs without oracle perception and integrates a simulateduser to enable human-in-the-loop interaction. Within this environment,we design diverse task compositions to drive data collection, supervisedfine-tuning, and online reinforcement learning, systematically optimizingagent performance. To comprehensively evaluate this approach, we in-troduce REAL-Bench, a benchmark spanning 241 tasks across activeexploration, visual distraction, articulated manipulation, and interactivedisambiguation. Experimental results demonstrate that our trained agentoutperforms leading commercial closed-source VLMs on interactive taskswith a 56.9% success rate. Further empirical analysis reveals that ourhierarchical training pipeline successfully aligns the model’s tool-use ca-pabilities while maintaining robust open-vocabulary reasoning under ex-tended exploration horizons. Finally, we deploy and evaluate our frame-work on a physical dual-arm mobile robot, where it achieves a 78.3%end-to-end success rate over 60 real-world episodes. These physical trialsdemonstrate robust zero-shot transferability to unseen household scenar-ios, validating that our sim-to-real-consistent design successfully bridgesthe reality gap for long-horizon mobile manipulation. Code is availableat https://github.com/InternRobotics/REAL.

ECCV 2026

ESNE: Efficient Surface Normal Estimation for LiDAR Point Clouds with Sequential Modeling and Variability Guidance

Kohei Matsuzaki, Keisuke Nonaka

While surface normal estimation from point clouds is a fun-damental problem in computer vision, there has been limited explorationon LiDAR point clouds. Recent approaches adopt Transformer-basedmodels designed for large-scale point clouds to achieve single-step esti-mation, still struggling to estimate surface normals with high computa-tional efficiency and accuracy. In this paper, we propose an efficient sur-face normal estimation method for LiDAR point clouds, termed ESNE.It converts point clouds into a sequence data format and leverages astate space model to capture long-range dependencies. To capture lo-cal structure, the proposed method introduces a local attention moduleincorporating an efficient and explicit search for neighboring points onthe sequence. Furthermore, the proposed method introduces a featurerefinement module using structural variability as guidance to enhancethe shape representations of local regions. Experimental results on large-scale LiDAR point cloud datasets demonstrate that the proposed methodimproves the mean accuracy by over 33.4% and achieves more than twicethe inference speed compared to state-of-the-art methods.

ECCV 2026

Escaping the Low-Frequency Bias: Adversarial Frequency Perturbation for Generalisable Gaze Estimation

Yang Xu, Feng Lu

Deep learning-based gaze estimation methods suffer severeperformance degradation in cross-domain scenarios. Our frequency anal-ysis reveals that overfitting to source-domain low-frequency componentsassociated with domain-specific factors, such as illumination, background,and skin tone, is a critical cause of cross-domain degradation. Basedon this finding, we propose the Adversarial Low-Frequency Perturba-tion (ALFP) framework, which employs a Perturbation Generation Net-work (PGNet) to adversarially generate instance-specific perturbationimages and mix their low-frequency amplitude statistics with those ofsource images through an AdaIN-based mechanism. The resulting inputs,with perturbed low-frequency distributions, are used to train the GazeEstimation Network (GazeNet). Adversarial training between PGNet andGazeNet progressively mitigates GazeNet’s overfitting to source-domainlow-frequency components, thereby improving cross-domain generaliza-tion. Experiments show that ALFP outperforms state-of-the-art meth-ods across various cross-domain settings, improving over the baseline byup to 34.9% and the best existing method by 15.0%, without requiringtarget-domain data.

ECCV 2026

EcoVideo: Entropy-Orchestrated Video Generation Paradigm in Cloud-Edge Dynamics

Jiayu Chen, Hengyi Zhang, Maoliang Li, Minyu Li, Zihao Zheng, Xuanzhe Liu, Guojie Luo, Xiang Chen

DiT video generation is latency-intensive due to iterativefull-frame denoising, while prior cloud-edge methods largely rely on staticinter-step decoupling and cannot leverage inter-frame similarity or adaptto system dynamics. We propose EcoVideo, an entropy-orchestratedframework for dynamic inter-frame decoupling: early-stage self-attentionentropy provides a training-free estimate of frame-wise information den-sity for frame selection; a cloud large model denoises sparse high-entropykeyframes; and an edge lightweight model reconstructs the remainingframes via motion-aware interpolation with refinement for temporal sta-bility. EcoVideo further adapts the keyframe budget and edge refine-ment depth to real-time bandwidth and compute availability, optimizingend-to-end latency under constraints. Experiments on representative DiTvideo generators show improved quality–efficiency trade-offs and up to2.9× end-to-end speedup in low-bandwidth, compute-limited edge set-tings. Code is available at https://github.com/IF-LAB-PKU/EcoVideo.

ECCV 2026

Early Estimation of Language to Latent Alignment in Diffusion Models

Vasco Ramos, Regev Cohen, Idan Szpektor, Joao Magalhaes

Conditional diffusion models frequently suffer from language-image misalignments. Due to the ambiguity of intermediate noise cor-rupted latents, assessing prompt adherence currently requires complet-ing the entire sampling trajectory. This late-stage evaluation incurs evenhigher computational costs during test-time scaling strategies, such asBest-of-N (BoN) sampling, as all misaligned trajectories must finish gen-eration before being discarded. To tackle this, we propose NoisyCLIP, anoise-aware twin-tower model that enables early language-to-latent align-ment estimation. By learning a vision encoder on noise-corrupted latents,we allow the model to "see" through the ambiguity of intermediate diffu-sion steps. To facilitate this training, we investigate noise-data augmen-tation sampling strategies and introduce two new benchmark datasets:Noisy-Conceptual-Captions and Noisy-GenAI-Bench. When applied asan early-stopping criterion for BoN, NoisyCLIP at half cost matches orbeats frozen CLIP at full cost. Ultimately, this transforms alignment as-sessment from an expensive final check into a continuous monitoring tool,drastically reducing compute costs without sacrificing semantic fidelity.

ECCV 2026

DTI: Dynamic Trajectory Initialization for Generative Face Video Super-Resolution

Yingwei TANG, Chen Yan, Wendi Liu, Qiang Hu, Xiaoyun Zhang

As the most perceptually powerful Face Video Super-Resolution (FVSR) method, existing works in Generative FVSR (GFVSR) mainly exploit the generative prior of pretrained diffusion models. However, viewed as full generation, they suffer from fixed sampling and expensive inference costs if without large-scale auxiliary training. Furthermore, an excessive pursuit of generic perceptual metrics often results in low fidelity. To address these issues, we present Dynamic Trajectory Initialization (DTI) paradigm for GFVSR, which reformulates GFVSR as an input-driven directional restoration. With a novel enhancement-andinjection conditioning mechanism for pretrained DiT backbone, fidelity of our model has been significantly improved without compromising perceptual quality. To dynamically set the starting sampling point, we propose a Discriminative Guide (DG) trained via objective Signal-to-Noise Ratio (SNR) alignment. With only minor model adaptation and finetuning, our method achieves a SOTA overall performance across diverse metrics and benchmarks. An analysis of relationship between actual comprehensive quality and common metrics is also conducted, which demonstrates the perception-distortion trade-off and that the LPIPS is the most convincing metric in our case. Code: https://github.com/MediaXSJTU/DTI

ECCV 2026

DOGE: Differentiable Bézier Graph Optimization for Road Network Extraction

Jiahui Sun, Junran Lu, Jinhui Yin, Yishuo Xu, Yuanqi Li, Yanwen Guo

Automatic extraction of road networks from aerial imageryis a fundamental task, yet prevailing methods rely on discrete poly-lines that inherently lack the high-order geometric continuity required bydownstream applications. While parametric curve representations, suchas our Bézier Graph, offer analytic smoothness and topological compact-ness, their extraction is bottlenecked by the absence of curve-level vectorground-truth (GT) and the fragility of heuristic vectorization pipelines.To overcome these challenges, we introduce DOGE, a novel differen-tiable rendering-driven framework for robust, curve GT-free road net-work extraction. DOGE elegantly decouples the extraction process viaa discrete-continuous coupling mechanism: a DiffAlign module contin-uously optimizes the graph’s geometric alignment against target masksusing differentiable rendering, while a TopoAdapt module applies dis-crete operators to evolve the network’s topology. This coupling preventstopological bottlenecks from paralyzing the gradient-based geometric op-timization. Extensive experiments demonstrate that our method sets anew state-of-the-art on the large-scale SpaceNet and City-Scale bench-marks. Driven purely by mask guidance, DOGE successfully reconstructshighly accurate, geometrically smooth, and topologically compact vectormaps, advancing a new paradigm for structured road network reconstruc-tion.

ECCV 2026

Direct Diffusion Score Preference Optimization via Stepwise Contrastive Policy-Pair Supervision

Dohyun Kim, Seungwoo Lyu, Seung Kim, Paul Hongsuck Seo

Diffusion models have achieved impressive results in gener-ative tasks such as text-to-image synthesis, yet they often struggle tofully align outputs with nuanced user intent and maintain consistentaesthetic quality. Existing preference-based training methods such asDiffusion Direct Preference Optimization help address these issues, butobtain their supervision targets from the forward process q(xt−1 | xt , x0 )derived from terminal samples, which is not directly aligned with themodel’s actual backward denoising transitions at each step. In this work,we introduce Direct Diffusion Score Preference Optimization (DDSPO),which defines stepwise preference supervision directly over backward de-noising transitions through a contrastive policy pair, rather than relyingon forward-process approximations from terminal samples. We proposetwo practical instantiations of the contrastive policy pair: training sepa-rate winning and losing models on preference data, and inducing a con-trastive policy pair without additional training by using a pretrainedreference model conditioned on an original prompt and a semanticallydegraded variant, requiring neither reward modeling nor manual annota-tions. Empirical results show that contrastive-policy-pair supervision ismore effective than forward-process-based supervision across text–imagealignment and aesthetic-quality tasks. Our implementation is availableat: https://dohyun-as.github.io/DDSPO

ECCV 2026

Cycle-World: Mitigating Error Accumulation in Long-term Video World Models via Reverse-Prediction Cycle Consistency

Zihan Su, Teng Hu, Jiangning Zhang, Ruiyan Wang, Ran Yi, Lizhuang Ma, Dacheng Tao

Autoregressive diffusion models have enabled high-qualityvideo generation, yet their sequential nature inherently suffers from erroraccumulation. In long-horizon video synthesis, minor prediction devia-tions compound over time, inevitably leading to unconstrained genera-tive drift, structural collapse, and severe visual degradation. To addressthis, we propose Cycle-World, a novel framework designed for stable andtemporally consistent long-video generation. Our approach tackles errordrift by enforcing strict temporal reversibility across both the trainingand inference phases. Theoretically, we demonstrate that forward gener-ative drift can be strictly bottlenecked by a cycle-consistency objective.During training, we integrate an efficient reverse-prediction model to im-plicitly embed causal constraints into the forward generator, compellingit to produce reversible sequences that tightly adhere to the natural videomanifold. At inference time, we repurpose this frozen reverse model as aruntime corrector. Through gradient-based cycle guidance, it iterativelyrefines the generated latent representations, actively suppressing accu-mulated errors before they are committed to the historical context. Ex-tensive experiments on the VBench benchmark demonstrate that Cycle-World’s dual-phase synergy significantly mitigates error drift, achievingstate-of-the-art overall generation quality and long-horizon temporal con-sistency in 60-second synthesis.

ECCV 2026

MegaFlow: Zero-Shot Large Displacement Optical Flow

Dingxi Zhang, Fangjinhua Wang, Marc Pollefeys, Haofei Xu

Accurate estimation of large displacement optical flow re-mains a critical challenge. Existing methods typically rely on iterativelocal search or/and domain-specific fine-tuning, which severely limits theirperformance in large displacement and zero-shot generalization scenarios.To overcome this, we introduce MegaFlow, a simple yet powerful model forzero-shot large displacement optical flow. Rather than relying on highlycomplex, task-specific architectural designs, MegaFlow adapts powerfulpre-trained vision priors to produce temporally consistent motion fields.In particular, we formulate flow estimation as a global matching problemby leveraging pre-trained global Vision Transformer features, which natu-rally captures large displacements. This is followed by a few lightweightiterative refinement to further improve the sub-pixel accuracy. Extensiveexperiments demonstrate that MegaFlow achieves state-of-the-art zero-shot performance across multiple optical flow benchmarks. Moreover, ourmodel also delivers highly competitive zero-shot performance on long-range point tracking benchmarks, demonstrating its robust transferabilityand suggesting a unified paradigm for generalizable motion estimation.Project Page: https://kristen-z.github.io/projects/megaflow/.

ECCV 2026

Continuous Adversarial Flow Models

Shanchuan Lin, Ceyuan Yang, Zhijie Lin, Hao Chen, Haoqi Fan

We propose continuous adversarial flow models, a type ofcontinuous-time flow model trained with an adversarial objective. Unlikeflow matching, which uses a fixed mean-squared-error criterion, our ap-proach introduces a learned discriminator to guide training. This changein objective induces a different generalized distribution, which empiri-cally produces samples that are better aligned with the target data dis-tribution. Our method is primarily proposed for post-training existingflow-matching models, although it can also train models from scratch.On the ImageNet 256px generation task, our post-training substantiallyimproves the guidance-free FID of latent-space SiT from 8.26 to 3.63 andof pixel-space JiT from 7.17 to 3.57. It also improves guided generation,reducing FID from 2.06 to 1.53 for SiT and from 1.86 to 1.80 for JiT.We further evaluate our approach on text-to-image generation, where itachieves improved results on both the GenEval and DPG benchmarks.

ECCV 2026

CaRe: Critical Parameter Rectification for Efficient Visual Modeling

Mingjia Li, Hongkun Xiong, Yuheng Shi, Hengxing Liu, Xiaojie Guo

Designing efficient vision backbones is increasingly challeng-ing. An intriguing avenue is mergeable re-parameterization, which canreshape training dynamics by introducing auxiliary parameterizationswithout inference overhead. In particular, RepVGG-style structures can beviewed as adding a fixed-support correction on top of a dense base kernelafter merging. In this paper, we propose Critical Parameter Rectification(CaRe), which generalizes fixed corrections to learned selective rectifi-cation. CaRe factorizes each layer as W = Wbase + Waux ⊙ ϕ(Wgate ),where a static parameter-space gate induces sparse activation in thecorrection term, concentrating updates on a small subset of critical pa-rameters during training. Since the gate depends only on parameters,CaRe is losslessly mergeable into a standard weight tensor, incurring zeroinference overhead. Built on CaRe, the CaReNet family establishes astrong accuracy-throughput Pareto frontier on ImageNet-1K, with consis-tent gains across model scales and strong transfer to downstream denseprediction tasks. Code is available at https://github.com/lime-j/care.

ECCV 2026

CAR-MIL: Counterfactual Attention Regularization for Multiple Instance Learning

Imane Chraki, Pierre Marza, Stergios Christodoulidis, Maria Vakalopoulou

Multiple Instance Learning (MIL) is widely used for weaklysupervised learning, particularly in digital pathology, where fine-grainedannotations are costly. Most MIL methods aggregate instance featuresvia attention mechanisms. However, attention weights do not alwaysfaithfully reflect instance importance and may focus on spuriously cor-related regions. In this work, we propose CAR-MIL, a framework thatexplicitly guides attention learning through a counterfactual attentionregularization objective inspired by counterfactual explanations. Built ona standard attention-based MIL architecture, our approach introduces alightweight counterfactual attention branch trained to produce an alter-native prediction while remaining close to the factual attention distribu-tion. This encourages prediction changes to arise from minimal, struc-tured redistributions of attention, leading to more informative evidenceallocation. The resulting factual and counterfactual attention maps cap-ture complementary evidence: the former highlights regions supportingthe prediction, while the latter reveals regions whose reweighting wouldchallenge it. We evaluate our method on synthetic MIL benchmarks withinstance-level ground truth enabling controlled analysis of attention be-havior and on five digital pathology datasets across four tasks. CAR-MILmaintains competitive classification performance, with the largest gainsobserved on more challenging tasks, while improving attention reliability,demonstrating the benefits of integrating counterfactual explainabilityreasoning into attention learning.

ECCV 2026

Beyond Where to Look: Trajectory-Guided Reinforcement Learning for Multimodal RLVR

Jinda Lu, Junkang Wu, Jinghan Li, Kexin Huang, Shuo Yang, Mingzhu Chen, Jiancan Wu, Kuien Liu, Xiang Wang

Recent advances in Reinforcement Learning with VerifiableRewards (RLVR) for multimodal large language models (MLLMs) havemainly focused on improving final answer correctness and strengthen-ing visual grounding. However, a critical bottleneck remains: althoughmodels can attend to relevant visual regions, they often fail to effectivelyincorporate visual evidence into subsequent reasoning, leading to reason-ing chains that are weakly grounded in visual facts. To address this issue,we propose Trajectory-Guided Reinforcement Learning (TGRL), whichguides the policy model to integrate visual evidence into fine-grained rea-soning processes using expert reasoning trajectories from stronger mod-els. We further introduce token-level reweighting and trajectory filteringto ensure stable and effective policy optimization. Extensive experimentson multiple multimodal reasoning benchmarks demonstrate that TGRLconsistently improves reasoning performance and effectively bridges thegap between visual perception and logical reasoning.

ECCV 2026

Background Blurring Matters: Improving Visual Grounding by Merging Text-Irrelevant Tokens

Ruilin Yao, Shengwu Xiong, Shanshan Yang, Tianyu Zou, Shili Xiong, Yi Rong

Visual grounding (VG) aims to precisely localize the objectin input images based on its natural language descriptions. Most recentlyproposed methods address this task using Transformer-based architec-tures that can inject textual information into visual features. However,according to the image tokenization procedure, a large number of vi-sual tokens will be located in text-irrelevant background areas. Thesetokens can introduce noise into the attention calculation, thus reduc-ing the significance of foreground object tokens and ultimately affect-ing the effectiveness of these methods. To this end, we propose a novelToken Blurring (ToB) module, which dynamically merges image tokensbased on the pair-wise visual similarity between them and their tex-tual relevance with input expressions. By reducing the number of text-irrelevant background tokens and preserving the density of text-referredones, ToB can improve both model effectiveness and efficiency in solvingVG tasks. Extensive experiments on RefCOCO, RefCOCO+, and Re-fCOCOg datasets show that Transformer-based models equipped withour ToB module yield better results while reducing computational over-head compared to various existing VG methods. Our code is available athttps://github.com/Mr-Bigworth/ToB.

ECCV 2026

AutoPhyX: Automatic Text-Condition Physics Property Generation

Bei Huang, Yixin Chen, Hongbin Zha, Yuru Pei, Siyuan Huang

High-fidelity 4D physical simulation requires accurate phys-ical parameter assignments that align with diverse material properties.Previous methods struggle with critical bottlenecks: they either rely oncomputationally intensive per-scene optimization or predict parametersfrom purely visual inputs. The latter suffers from inherent ambiguity,e.g., the inability to distinguish the stiffness of rubber from its ap-pearance alone. To bridge this gap, we propose AutoPhyX, a text-conditioned framework for predicting spatially-varying physical proper-ties. Since training robust feed-forward models requires fine-grained su-pervision, we first introduce a novel part-controllable data generationpipeline that decomposes complex 3D assets into semantically distinctcomponents and pairs them with physically plausible, diverse parame-ters and corresponding text descriptions. Leveraging this dataset, Auto-PhyX employs a cross-modal modulation mechanism where text featuresdynamically modulate visual features for precise physical grounding. Byformulating property prediction within a voxel field, AutoPhyX en-sures compatibility with diverse 3D representations, including meshes,point clouds, Gaussian Splatting, and NeRF. Experiments demonstratethat our method enables physically plausible, text-driven parameter as-signment in a single forward pass. It achieves high accuracy and robustgeneralization to in-the-wild objects, paving the way for future physics-intensive embodied and robotic applications.

ECCV 2026

Less Tokens, Better Forecasts: Sparse Residual Routing for Efficient Weather Prediction

Janet Wang, Yunbei Zhang, Lin Zhao, Xi Xiao, Jihun Hamm, Xiao Wang

Existing ViT-based weather forecasting models apply uni-form computation across all spatial tokens, even though nearby atmo-spheric grid points often contain similar values and large regions evolvesmoothly over time. This makes much of the intermediate per-token com-putation redundant. Standard token-efficiency methods, such as pruningor merging, reduce cost by removing or fusing tokens. However, weatherforecasting is a spatiotemporal dense prediction problem in which a his-tory of atmospheric states must be mapped to future values on the orig-inal latitude-longitude grid. Thus, every grid cell must retain a physi-cally meaningful representation, especially under autoregressive rollout.We introduce Sparse-Reslim, a parameter-free plug-in routing modulethat makes sparse token processing compatible with this fixed-grid re-quirement. Sparse-Reslim routes only 25% of spatial tokens through theexpensive middle transformer blocks and treats those blocks as residualupdates: it computes the change produced for the routed tokens and scat-ters only this delta back to the full sequence. Unselected tokens keep theirpre-routing representations exactly, so no grid cell is dropped or replacedby a mask token, and no fusion layer or additional parameters are intro-duced. Across ERA5 resolutions up to the operational 0.25° standard andtwo model families, a deterministic Transformer and a diffusion model,Sparse-Reslim improves forecast accuracy on every evaluated variablewhile substantially reducing cost: training is about 2.5× faster in themain settings and reaches 3.18× speedup at 0.25°, with over 2.2× lowerpeak memory. A controlled decomposition shows that the accuracy gaincomes primarily from sparse routing itself, while random token selectionprovides an additional regularization benefit without selector overhead.Code is available at https://github.com/janet-sw/Sparse-Reslim.

ECCV 2026

Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection

Ali Balapour, Faraz Hach

Detecting and localizing defects in 3D point clouds is chal-lenging because abnormal samples are scarce and diverse, while train-ing is often limited to normal data. We propose Anomaly Factory 3D(AF3AD), a modular framework that synthesizes diverse pseudo-anomaliesfrom normal point clouds to expand the training data for unsupervised3D anomaly detection methods that rely on pseudo-anomalies. AF3ADuses a center-conditioned parametric deformation model defined in localPCA frames, with kernel-controlled spatial falloff, anisotropy, directionalgating, and normal/tangential displacement fields, enabling a broad setof geometric defect presets. We demonstrate its ease-of-use and effec-tiveness by integrating AF3AD with an offset-prediction detector and areconstruction-based anomaly detection method, showing that AF3ADtransfers across detection paradigms. Experiments on AnomalyShapeNetand Real3D-AD show consistent improvements in object- and point-leveldetection and localization, supported by ablations on preset groups androbustness under noise. AF3AD is designed as a standalone synthesis toolto facilitate adoption across different 3D anomaly detection paradigms.Code is available at github.com/vpc-ccg/AF3AD.

ECCV 2026

An Inverse-Adversarial and Difficulty-Adaptive Robust Vision-Language Model

Ruobing Xu, Junhao Dong, Xiaohua Xie

Vision-language models (VLMs) pre-trained on large-scaledatasets demonstrate strong generalization in few-shot scenarios, oftensurpassing conventional visual models on natural samples. However, theyremain vulnerable to adversarial perturbations, and directly applyingadversarial training may disrupt the cross-modal alignment, degradinggeneralization to novel classes. To mitigate these issues, we proposean Inverse-Adversarial and Difficulty-Adaptive Robust Vision-LanguageModel (IADA-RVLM) for robust few-shot classification. Specifically, toconstrain the optimization trajectory, our method jointly leverages ad-versarial and inverse-adversarial samples, where inverse-adversarial sam-ples serve as semantic anchors to guide representations toward high-confidence regions. To improve robustness, we incorporate visual prompttuning and text adapters to inject task-specific knowledge into bothmodalities. To balance robustness and generalization under varying sce-narios, we quantify adversarial transfer difficulty via semantic distancesand dynamically adjust the fusion of general and specialized knowl-edge. Extensive experiments across multiple datasets and attack settingsdemonstrate that our method achieves a superior overall trade-off be-tween robustness and generalization.

ECCV 2026

AlphaRad: Grounded Zero-Shot Classification in Chest Radiology via α-Corrected Binary Cross Entropy and Factorized Latent Supervision

Jianzhong You, Yuan Gao, Chris McIntosh

Vision-Language Pretrained Models (VLPMs) offer a scal-able path to open-vocabulary chest radiology understanding, yet two as-pects remain underexplored: how structured clinical semantics extractedfrom medical reports can reduce in-batch noise during contrastive learn-ing, and how cross-modal fusion can be designed to produce more faithfulspatial grounding without added complexity. We introduce AlphaRad,addressing these opportunities through two contributions. First, we con-struct a large-scale structured medical concept space from medical re-ports parsed by a Large Language Model for training, thereby mit-igating in-batch learning noise and removing heuristic pair matchingin contrastive learning, and thus naturally positioning AlphaRad asa medical concept discriminator trained via α-Corrected Binary Cross-Entropy. Second, we propose FLaS (Factorized Latent Supervision), anextremely simple yet effective cross-modal feature fusion module thatfactorizes VLPM representations into independent subspaces, using ded-icated alignment supervision to enhance the expressiveness of spatialgrounding without introducing additional model parameters. Throughextensive empirical validation, AlphaRad shows strong zero-shot gen-eralization across diverse chest radiology tasks. Notably, it establishesstate-of-the-art average performance across 16 classification benchmarks,while achieving individual state-of-the-art results via distinct gains on 7grounding/phrase grounding and 3 segmentation datasets.

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