#01Nov 10, 2026
cs.CV
Metric-Aligned Group Relative Policy Optimization for Explainable Deepfake Detection
Luka Dragar, Peter Peer, Vitomir Štruc and 1 more
Deepfake detectors are commonly evaluated on real/fake classification alone, but real-world applications require explanations of the visual evidence behind each prediction. Joint vision-language models struggle with authenticity assessment, producing "real" narratives for manipulated images. To address this, we propose a decoupled predict-then-explain pipeline as our submission to the ACM Multimedia 2026 Explainable Deepfake Detection Challenge, with methods trained and evaluated on the XPlainVerse benchmark. A vision-foundation-model ensemble first classifies the image, then a Qwen3-VL-8B model generates a decision-conditioned explanation of the visual evidence. For fake images, a second Qwen3-VL-8B compresses that text into a short non-expert summary without using the image. We further apply metric-aligned Group Relative Policy Optimization (GRPO) to optimize both explainers against the challenge criteria. Under matched conditions, GRPO raises complex BERTScore-F1 from 0.686 to 0.712 and also improves grounding (entity and evidence F1); our submission ranked first on complex BERTScore-F1 and seventh overall.
#02Nov 10, 2026
cs.CV
CroCoMAE: Cross-modal self-supervised collaborative representation learning for SAR-optical remote sensing data
Vito Recchia, Giuseppe Guarino, Cássio Fraga Dantas and 3 more
Remote sensing increasingly captures the Earth's surface through complementary sensors such as SAR and optical, yet ground-truth annotations remain scarce, limiting supervised approaches. Self-supervised learning (SSL) addresses the label scarcity, and cross-modal SSL further exploits the complementarity between co-located sensors, but existing cross-modal frameworks align modalities mostly through global image-level representations or treat them as largely redundant, leaving their fine-grained complementary structure underexploited. We propose CroCoMAE, a cross-modal SSL framework for SAR-optical remote sensing that unifies contrastive learning and masked image modeling within a ViT-based architecture, operating at two complementary levels. At the image level, an intra-modality contrastive loss on the class token yields globally discriminative, augmentation-invariant representations. At the patch level, masked image modeling drives fine-grained per-modality representations, while a cross-modal contrastive objective aligns a shared subspace across SAR and optical patch tokens, leaving the complementary subspace unconstrained to retain modality-specific information. On two SAR-optical benchmarks covering scene classification and semantic segmentation, CroCoMAE improves over uni-modal SSL baselines and outperforms state-of-the-art cross-modal frameworks in most configurations, with gains in both single-modality and multi-modal evaluation across varying label regimes and the largest improvements on semantic segmentation.
#03Nov 10, 2026
cs.CV
SpecValley: Aggregation as Spectral Valley in Dense Heterophilic Graphs
Christoph Jahn, Delvin Bacho, Urs Waldmann and 1 more
Node classification in heterophilic graphs remains an open problem: aggregating over neighbourhoods can damp precisely the part of the signal that carries the labels. The field has answered with ever more expressive propagation operators, leaving the aggregation step that precedes them fixed by degree normalisation. However, degree normalisation acts as a spectral valley in its own right, attenuating a fixed middle band whose position at lambda equal 1 is set by the normalisation rather than by the data, exactly where the eigenvalues of dense graphs accumulate. We therefore begin with the representations rather than the propagation, in two stages: a learnable aggregation followed by an ordinary spectral filter. The first, SpecValley learns the position, sharpness and flank gains of the valley for every graph. We show formally how the aggregation operator and downstream filter interact. We establish that the response 1 minus lambda of degree normalisation vanishes exactly at that accumulation point. A spectral energy budget shows that what aggregation removes from a band, no linear spectral filter can recover. Crowding alone says nothing about the benefit. That is decided by how much label energy the valley removes, which we measure for every graph. SpecValley achieves strong results across heterophilic benchmarks, and a controlled ablation over aggregation operators and downstream filters attributes the gain to aggregation rather than the filter.
#04Nov 10, 2026
cs.RO
Elastic-WAM: Adaptive Spatiotemporal Imagination for Efficient Robot Control
Haitian Li, Tianfu Li, Xuxi Zhao and 8 more
World-Action Models improve robot control by predicting future visual dynamics, but iterative video denoising incurs substantial inference overhead. Recent approaches reduce this cost through partial future denoising, yet typically rely on fixed refinement budgets. Our empirical observations reveal that preferred refinement depths vary across tasks and states, while different future regions exhibit varying relevance to action generation. Motivated by these findings, we propose Elastic-WAM, which adaptively allocates world refinement computation across temporal denoising time and spatial future space. A unified spatiotemporal refinement demand field governs when to stop world refinement, how far to advance each denoising step, and which regions to refresh. We parameterize this field with a shared temporal basis and spatial amplitudes, and learn it through a three-stage action-grounded supervised procedure. At inference, the learned field guides recurrent sparse world refinement to improve task success while reducing inference overhead. Evaluations across diverse simulation benchmarks and real-world long-horizon manipulation demonstrate state-of-the-art control performance with reduced inference latency.
#05Nov 10, 2026
cs.CV
TaoMate-R1: Temporal Role Specialization for Streaming Multi-Reference Video Generation
Qijun Gan, Chenwei Zhang, Xinyang Li and 2 more
Streaming multi-reference video generation establishes a scene from specified people and objects, then continues it under changing instructions. We introduce TaoMate-R1, a streaming multi-reference generator that organizes adaptation parameters through temporal role specialization. It assigns separate low-rank adapters to scene establishment and continuation on a frozen backbone. Each adapter performs both denoising and context writing for its role; Self Gradient Forcing jointly trains the roles through the history they construct. This allocation contrasts with operation-based specialization, which separates denoising from context writing across all chunks. A fixed-replay gradient analysis characterizes the update directions admitted by the two sharing schemes. On 32 multi-reference cases, we compare the schemes at the same two-adapter budget. Across ten approximately two-minute streams with changing instructions and shared teacher-audio conditioning, TaoMate-R1 reduces the mean boundary luminance jump from 1.96 to 0.613, with higher boundary feature similarity. It also achieves higher mean CLIP and DINO temporal similarity across short interactions, long streams, and public reference compositions. These results support grouping adaptation parameters by scene establishment and continuation for streaming multi-reference generation. Our code and model are released at https://github.com/TaoLiveAIGC/TaoMate-H3.