#01Oct 23, 2026
cs.CV
Video-MemComp: Taylor-Guided Sensitivity-Aware Memory Management for Streaming Video Understanding
Yonghui Niu, Yuhao Ji, Chang Nie and 6 more
The rapid growth of video data has created an urgent need for efficient video understanding systems that can handle long and continuous visual inputs, posing significant challenges for Multimodal Large Language Models (MLLMs) due to severe memory bottlenecks. Moreover, existing compression methods often overlook the explosive accumulation of approximation errors. To address this issue, we propose Video-MemComp, a training-free and sensitivity-aware context management framework for memory-constrained streaming video understanding. Our method theoretically decomposes compression error into orthogonal local approximation error and layer-wise structural sensitivity via a first-order Taylor expansion. To minimize local approximation error, we perform token aggregation in a pre-RoPE safe zone with an attention-conserving logit bias, mitigating geometric distortions caused by RoPE rotations. To manage layer-wise structural sensitivity, we dynamically allocate the KV cache budget based on offline gradient priors and online context entropy, prioritizing high-sensitivity layers. Video-MemComp outperforms or achieves performance comparable to full-cache and state-of-the-art compression methods on benchmarks including MLVU, VideoMME, and EgoSchema. Notably, it improves the overall accuracy of Qwen2.5-VL-3B from 62.3% to 64.1% while reducing the KV cache to an equivalent of 6,000 tokens per layer, saving over 90% of KV memory.
#02Oct 23, 2026
cs.CV
LoCA: Enabling Multi-Modality in Geospatial Location Encoders
Panagiotis Koromilas, Eleanna Vali, Diogenis Antonopoulos and 7 more
Location encoders map geographic coordinates to high-dimensional descriptors through contrastive alignment with paired Earth Observation imagery. We prove that this construction is structurally limited: any Lipschitz location encoder produces an output of intrinsic dimension at most 2, regardless of architecture or bandwidth, which forces the contrastive loss to strictly exceed its asymptotic optimum. Empirically, joint training also collapses the encoder's embedding covariance to low rank. Although dimension-limited, current location encoders sidestep this spectral collapse by training against a frozen image encoder, which we reinterpret as a covariance regularizer that supplies a high-rank target for the location encoder to fold onto. However, extending this approach to multimodal inputs is challenging, as location encoders should simultaneously map a single location to multiple, potentially incompatible, descriptors. We identify two requirements a regularizer must satisfy for location encoding in multimodal regimes: co-alignment across modalities, since adapters alone cannot align independently pretrained encoders; and high effective rank, since the location encoder inherits its target's spectrum. We propose LoCA (Location Contrastive Alignment), a two-stage pipeline that satisfies both. Stage 1 jointly trains Sentinel-1 and Sentinel-2 encoders with contrastive learning, producing a co-aligned, high-rank multimodal target. Stage 2 freezes the image encoders and trains a location and a tabular metadata encoder as projection adapters with multi-view contrastive learning. Against five state-of-the-art encoders on 17 tasks ranging from land cover and wildfire forecasting to geophysical, socio-economic, biodiversity and climate applications, LoCA achieves the most task wins, is the only model to outperform every other on a majority of tasks, and wins 68.8% of all per-task comparisons.
#03Oct 23, 2026
cs.AI
Closing the Modality Gap via Simplex-Constrained Representations
Shubham Gupta, Siva Reddy, Perouz Taslakian and 2 more
While multimodal contrastive models like CLIP and CLAP successfully learn shared representations, their embeddings typically exhibit a persistent \emph{modality gap}. We propose addressing this separation through a purely geometric approach: changing the embedding domain from the unit sphere, $S^{d-1}$, to the probability simplex, $Δ^{d-1}$. Although both manifolds share the same intrinsic dimension, the simplex acts as a structural prior; its nonnegative, unit-mass constraints serve as a cross-modal calibration that forces both modalities to ground themselves in the same coordinate system. By attaching lightweight softmax adapters and using Total Variation similarity, we reduce the centroid $\ell_2$ gap by 97--99\% across five multimodal retrieval benchmarks. This confirms that simplicial geometry induces genuine semantic alignment, matching or exceeding the retrieval performance of standard unconstrained models.
#04Oct 23, 2026
cs.CV
STROKE: Simple Sparse Structural Occlusions for Black-Box Adversarial Attacks
Ankit Gupta, Akshat Pandya, Paul Lin and 2 more
We introduce STROKE, a query-efficient score-based black-box adversarial attack that constructs perturbations from simple oriented rectangular occlusions. STROKE optimizes these perturbations through a lightweight greedy add-and-refine procedure. Across diverse image classifiers, STROKE achieves high attack success with low query counts at nominal 1--2\% area settings, showing favorable performance relative to existing patch-based attacks in several configurations. Matched comparisons further demonstrate substantial reductions in wall-clock attack time, while ablation studies examine the key design choices and effectiveness across structural primitives. These findings suggest that simple structural occlusions offer an effective and computationally efficient search space for black-box adversarial attacks.
#05Oct 23, 2026
cs.CV
FlexGlue: Sparse-to-Dense Feature Matching and Visual Disambiguation
Philipp Lindenberger, Paul-Edouard Sarlin, Ondrej Miksik and 1 more
To estimate correspondences across images, existing approaches match either sparse interest points, which is highly efficient but limited by repeatability, or dense pixel grids, which is robust but expensive. Furthermore, these approaches are trained with only matching pairs and thus often fail to reject unrelated pairs that exhibit visual symmetries, which is equally important in applications like Structure-from-Motion (SfM). We bridge these gaps by introducing FlexGlue, a neural network for asymmetric sparse-to-dense matching and disambiguation. Given two images and coordinates of points in the first image, FlexGlue estimates the coordinates of the corresponding points in the second image while simultaneously rejecting the pair if non-matching. By searching for a limited set of correspondences among dense pixels, our approach is both fast and effective at both matching and disambiguation: it combines the sub-pixel accuracy of dense matching with the speed of sparse matching, while rejecting incorrect pairs with high precision. Experiments on multiple datasets show that FlexGlue pushes the Pareto frontier in relative pose estimation and significantly outperforms existing approaches for SfM and visual localization. FlexGlue is publicly available at https://github.com/cvg/flexglue .