This paper tackles Chinese Mandarin visual speech recognition (VSR),where the tonal nature of the language and large vocabulary make lipreading more challenging than for non-tonal languages like English. Existing approaches use cascade architectures with intermediate representations like pinyin to bridge the gap,but this introduces error accumulation and increases inference latency. The core idea is a cascade-free multitask architecture that jointly learns phoneme and viseme representations during training, with on-demand activation during inference for efficiency-accuracy trade-offs. This matters because cascade-free designs could eliminate error propagation while maintaining the benefits of intermediate representations.

Multi-focus image fusion (MFIF) combines source images from different focal planes into a single all-in-focus image. This paper targets a critical flaw in diffusion-based MFIF: defocus blur warps geometric structures, producing artifacts. The authors propose ReDiffuse, which embeds B-Conv (Fourier-series-based rotation-equivariant filters) into a U-Net diffusion backbone. By enforcing that rotations induce predictable feature transformations, the method aims to preserve edge orientation and structural consistency while reducing model size through parameter sharing.

Whole Slide Images (WSIs) present a unique challenge for computational pathology due to their gigapixel scale and the scarcity of annotated data. This paper addresses few-shot weakly supervised WSI classification (FSWC) by proposing HIPSS, which combines parameter-efficient prompt tuning via Scaling and Shifting Features (SSF) in the text encoder with a hierarchical textual guidance strategy for WSI representation learning. The core innovation replaces expensive cross-attention mechanisms with lightweight linear transformations $y = \gamma \cdot x + \beta$ while avoiding hard instance filtering through soft cosine-similarity-based attention refinement, achieving up to 13.8\% accuracy gains with 18.1\% fewer parameters than state-of-the-art methods.

Most road extraction benchmarks focus on binary segmentation, lacking the hierarchical attributes critical for transport infrastructure planning and management. This paper introduces SYSU-HiRoads, a large-scale dataset spanning 3,631 km² with aligned pixel masks, vector centerlines, and three-level road grades, alongside RoadReasoner—a framework that combines frequency-domain feature extraction with vision-language models to infer road hierarchy from geometric descriptors. The work bridges a significant gap in automated mapping by moving beyond "where are the roads" to "what roles do these roads play."

This paper addresses privacy-preserving facial expression recognition (FER) in video without requiring identity labels—a critical gap since real-world deployment often lacks identity annotations. The core idea leverages intra- and inter-video knowledge priors to train an identity suppression network followed by a denoising module, enabling open-set privacy preservation. This matters because current methods either require closed-set identity supervision or suffer from entangled privacy-utility trade-offs that degrade performance.

The paper tackles semi-supervised 3D rotation regression from monocular images, addressing the rigidity of fixed entropy thresholds in pseudo-label filtering used by prior work like FisherMatch. It proposes HACMatch, a hardness-aware curriculum learning framework that dynamically selects unlabeled samples by difficulty using either multi-stage or adaptive strategies, paired with PoseMosaic, a patch-based augmentation that applies diverse transformations while preserving geometric integrity. This matters because rotation annotations are expensive to obtain, and effectively leveraging unlabeled data could reduce costs for autonomous driving and robotics applications.

Federated video action recognition faces a dual challenge: gradient sharing risks leaking sensitive motion patterns, while synchronizing high-dimensional video models incurs prohibitive bandwidth costs. This paper proposes FedDP-STECAR, which selectively fine-tunes only task-relevant layers under differential privacy and transmits only those layers, claiming over 99% communication reduction alongside strong privacy guarantees ($\epsilon \leq 1.33$). The work matters for enabling practical privacy-preserving video analysis in healthcare and surveillance where data cannot be centralized.

Open-vocabulary panoptic segmentation aims to recognize and segment arbitrary object categories beyond training vocabularies, but suffers from two coupled failures: mask transformers discard proposals for unseen categories due to biased objectness scoring, while CLIP's global image-text alignment poorly localizes to image regions. OVRCOAT addresses both via COAT—which adjusts foreground probabilities using CLIP's classification confidence to rescue out-of-vocabulary masks—and OVR, a memory-efficient fine-tuning protocol for region-text alignment. The approach achieves +5.5% PQ gains on ADE20K and reduces training memory by 56% versus prior SOTA.

Existing visual privacy benchmarks treat privacy as a binary property, but this work argues that privacy is fundamentally compositional: benign attributes in isolation can combine to create severe violations. The authors introduce the Compositional Privacy Risk Taxonomy (CPRT), a four-level framework aligned with regulations like GDPR and HIPAA that assigns continuous severity scores based on attribute interactions. They construct a dataset of 6,736 images annotated for 22 privacy attributes and evaluate frontier vision-language models, finding that while structured taxonomic guidance improves alignment, models systematically underestimate composition-driven risks.

UniMotion addresses the fragmentation in human motion modeling by unifying motion, text, and RGB understanding/generation within a single 1.5B parameter architecture. Unlike prior work relying on discrete tokenization or handling only partial modality subsets, it treats motion as a continuous first-class modality via a Cross-Modal Aligned Motion VAE (CMA-VAE). The framework introduces Dual-Posterior KL Alignment to distill visual semantics into motion representations without requiring images at inference, and Latent Reconstruction Alignment to bootstrap the motion pathway through dense self-supervision before sparse text calibration.

StreamingEval introduces a unified evaluation framework for Video-LLMs under realistic streaming constraints, moving beyond offline benchmarks to assess continuous, real-time video understanding with limited memory. The protocol enforces a fixed-capacity memory bank and jointly measures encoding throughput (MaxFPS), decoding latency (TTFT), memory usage, and task accuracy via a composite StreamingScore. Experiments reveal that current "online" models often fail under strict streaming constraints, while offline models adapted with FIFO memory banks frequently outperform specialized streaming architectures at the cost of higher resource consumption.

F4Splat tackles inefficient Gaussian allocation in feed-forward 3D Gaussian Splatting (3DGS), where existing methods uniformly assign Gaussians per pixel or voxel, causing redundancy and fixed budgets. The core idea is a learnable densification score that predicts spatial regions needing additional Gaussians based on geometric complexity and multi-view overlap, enabling adaptive allocation and explicit budget control without retraining. This matters because it delivers compact scene representations—using 10–28% of the Gaussians of prior work—while maintaining or improving rendering fidelity.

PRM-as-a-Judge addresses the fundamental limitation of binary success metrics in robotic manipulation by repurposing Process Reward Models (PRMs) as dense evaluators. The paper introduces the OPD (Outcome–Process–Diagnosis) metric system, which decomposes execution quality via a task-aligned progress potential $\Phi(x_t) \in [0,1]$ induced from trajectory videos. Validated on the RoboPulse benchmark and RoboTwin policy auditing, the work shows that trajectory-supervised PRMs achieve superior micro-resolution compared to foundation models, revealing behavioral signatures invisible to outcome-only evaluation.

This paper addresses long-tailed (LT) learning by proposing that the head-tail performance trade-off stems from "tail performance degradation"—where models overfit to head classes and forget tail classes. The core idea reframes LT learning as continual learning, using a Grouped Knowledge Preservation (GKP) module to maintain class-specific optimal parameters and a Grouped Sharpness Aware (GSA) module to find flatter minima. The method operates without external data or pre-trained models, showing improvements on CIFAR-LT, ImageNet-LT, and iNaturalist benchmarks.

This paper introduces a new computer vision task called Anytime Interframe Semantic Segmentation: predicting dense semantic segmentation at arbitrary timestamps between low-frame-rate RGB frames using only a past frame and asynchronous event data. The core idea is feature propagation via event-driven motion fields rather than direct multi-modal fusion. The method is motivated by the perceptual gaps created by LFR cameras in high-speed autonomous driving scenarios, where critical events (e.g., pedestrians entering paths) may be missed between frames.

Single-view reference-to-video methods struggle to preserve identity when faces rotate through large angles. This paper proposes Mv2ID, a multi-view conditioning framework that uses region-masking and a decoupled positional encoding scheme to prevent view-dependent copy-paste artifacts without requiring expensive cross-paired training data. The work is relevant for digital character creation and visual effects where identity must remain consistent across extreme viewpoints.

The paper addresses the problem of identifying "Who said what and when" in multi-speaker video conversations, which current Omni-modal LLMs fail at due to sparse visual sampling (1-2 fps) and "shortcut learning" on visual biases. The authors introduce VR-SDR (Visual-Registered Speaker Diarization and Recognition), a rigorous benchmark that forces models to bind identities from natural language descriptions without visual shortcuts. They propose HumanOmni-Speaker, featuring a Visual Delta Encoder that samples video at 25 fps yet compresses inter-frame motion residuals into only 6 tokens per frame to capture fine-grained visemes while avoiding token explosion.

Instance segmentation in dense microscopy images requires separating tightly packed, touching cells—a task where binary masks and pixel-wise losses often merge adjacent instances. This paper proposes predicting continuous signed distance functions (SDFs) mapped to probabilistic segmentations via a learnable sigmoid, trained with a differentiable Modified Hausdorff Distance (MHD) loss. The approach eliminates the need for interactive prompting or watershed post-processing while aiming to improve boundary fidelity in high-throughput cellular imaging.

MRI acquisition is inherently slow due to sequential k-space sampling. This paper proposes TRUST-MRI, an active sampling framework that leverages discrete anatomical tokens from the pretrained MedITok tokenizer and a latent Transformer to guide measurement selection. The core innovation uses token prediction entropy as an uncertainty signal, introducing two policies: Latent Entropy Selection (LES) projects patch-wise entropy to k-space to select lines, while Gradient-based Entropy Optimization (GEO) uses gradients of total entropy with respect to input measurements. The approach trades pixel-wise fidelity for perceptual quality and computational efficiency, achieving superior feature-based metrics while running at 0.97 fps compared to 0.01 fps for diffusion-based active methods.

RGBD SLAM with 3D Gaussian Splatting (3DGS) struggles to balance scalability against rendering fidelity: global Gaussians consume excessive GPU memory, while view-tied Gaussians (fixed at depth) suffer from limited novel-view quality. This paper proposes pixel-aligned Gaussians that can adjust their positions along viewing rays via learned depth offsets, paired with a fast geometry-similarity tracking strategy using Generalized ICP on depth distributions. The approach claims state-of-the-art rendering and tracking performance while maintaining smaller active memory footprints than prior 3DGS-based methods.