#01Oct 31, 2026
cs.CV
Structured Channel Network Pruning with Bi-Sparse Matrix Factorization
Jingjing Liu, Ziyang Wang, Xianchao Xiu and 1 more
Structured channel pruning for convolutional neural networks (CNNs) plays an important role in pattern recognition and deep learning. Nevertheless, existing methods usually calculate channel importance scores based on the magnitudes of individual filters, making it difficult to distinguish shared redundancy from individual channel contributions. To tackle this limitation, we propose a novel structured channel pruning method termed bi-sparse matrix factorization (BSMF). Specifically, BSMF characterizes the convolutional weight matrix as output-channel coefficients over a shared basis with row-wise $\ell_{2,p}$-norm and column-wise $\ell_{2,q}$-norm regularization terms, respectively. Note that $p,q\in[0,1)$, which, to our best knowledge, has not been explored in the literature. To solve the resulting non-convex optimization problem, we develop an efficient update scheme built upon the alternating direction method of multipliers (ADMM), in which all subproblems admit closed-form solutions. After that, a compact CNN is obtained by reconstructing the convolutional filters from the learned factors and discarding the redundant channels. Extensive experiments on the CIFAR-10 and ImageNet-1K datasets demonstrate that our proposed BSMF achieves significant network compression while maintaining high reconstruction accuracy. The source code is publicly available at https://github.com/xianchaoxiu/BSMF
#02Oct 30, 2026
cs.CV
Spoofing the Latent State: Imperceptible Goal-Image Attacks Derail Planning in LeWorldModel
Kunal Bhosikar, Nicole Meng, Charu Sharma and 1 more
Joint-embedding predictive architectures (JEPAs) have recently emerged as general-purpose visual world models for control. A prominent example is LeWorldModel (LeWM), which trains end-to-end from pixels using an isotropic Gaussian anti-collapse regularizer (SIGReg) and plans by minimizing the squared Euclidean distance between predicted and goal embeddings. In this work, we investigate the test-time robustness of this latent planning interface. Analyzing the learned metric on Push-T, we first find that task-relevant object state heavily dominates latent distance: shifting the block by a single pixel displaces the embedding $3\times$ further than an equivalent agent shift, and swapping the block pose alone accounts for $99\%$ of the distance between scene pairs. However, because this pose information is distributed across a curved, high-dimensional manifold rather than a fixed linear subspace, the encoder cannot be trivially edited post-hoc to isolate or redirect it. We next demonstrate that this geometry is highly vulnerable at test time. A white-box $\ell_\infty$ perturbation of just $ε{=}4/255$ applied to a single input frame causes the encoder to register the block at an arbitrary, attacker-chosen pose in $73$--$95\%$ of scenes without disturbing the encoded agent state. Perturbing either the goal image or the live camera observation drops Push-T planning success from $87\%$ to $32\%$, whereas uniform random noise at twice the magnitude has negligible impact. This vulnerability persists across all four standard LeWM benchmarks (Push-T, Two-Room, Reacher, and OGBench-Cube). At $ε{=}8/255$, the attack bridges $82$--$97\%$ of the squared latent distance to the target embedding in every environment, inducing planning degradation proportional to each task's reliance on the spoofed entity.
#03Oct 30, 2026
cs.CV
Seeing the Unseen: Unified Visible--Invisible Motion for Physically Consistent Video Generation
Mingyang Bao, Fangda Ye, Xihao Chen and 3 more
Recent advances in video generation have achieved impressive visual quality, yet they often fail to produce physically consistent dynamics due to the lack of explicit modeling of underlying physical processes. Existing approaches either rely on simulators with strong assumptions or use coarse semantic guidance, and further struggle to effectively inject physical priors into generation models. In this work, we propose Seeing the Unseen, a unified framework for physics-grounded video generation. Our approach decomposes the problem into two stages: learning physical dynamics and injecting them into video synthesis. First, we introduce a lightweight Particle-based Graph Dynamics Simulator (PGDS) that learns generalizable physical interactions from data and predicts plausible 3D motion trajectories. Second, we propose a Visible--Invisible Motion Field (VIMF) that captures both motion observable in the input frame and motion that emerges over time due to object dynamics. This representation enables more complete and structured motion guidance compared to conventional physical signals. By integrating these trajectories into a diffusion-based generator, our method produces videos that are both visually coherent and physically consistent. Extensive experiments demonstrate improved motion accuracy, interaction consistency, and reduced physical artifacts compared to strong baselines.
#04Oct 31, 2026
cs.CV
Selecting or Solving? What Best-of-N Verification Delivers in Video Reasoning
Martin Q. Ma, Yuxiao Qu, Ruslan Salakhutdinov and 3 more
Answering a complex question about a long video is a grounding problem: the answer turns on a few seconds of evidence, and vision-language models remain well below human accuracy at finding them. The standard way to spend extra test-time compute on this kind of video reasoning is best-of-N selection: sample several chain-of-thought answers from a fixed model, then let a second model re-watch the video and pick one. Such pipelines are reported by a single headline number, their margin over a majority vote. That number conflates two effects with opposite deployment consequences: the benefit the second model adds by selecting among candidates, and the benefit it would have supplied by solving the question itself. We separate them, measuring the Selection Increment (SI) -- selection accuracy minus the same verifier's accuracy with the candidates withheld -- alongside the margin over self-consistency, across five long-video benchmarks. The Selection Increment is largest for the weakest verifiers: a verifier that solves poorly on its own leaves the candidates the most room to help. Beating the majority vote follows the opposite profile: where the vote already sits near its ceiling no verifier we test improves on it; below that ceiling only the strongest do. On Video-Holmes, the two come apart on a single pool of candidates -- a weak verifier gains +19.3 points from the candidates yet lands 7.1 points below the vote, while the frontier verifier gains the least and beats the vote. The weakest verifier also shows a failure mode: on EgoSchema it picks by where an option sits in the list rather than by what the video shows. Read as a merit score, SI puts a verifier that fails to beat a majority vote at the top of its ranking; read as an audit, it says which of the two effects a headline number delivers. Code is available at https://github.com/facebookresearch/best-n-selection-video-reasoning.
#05Oct 31, 2026
cs.CV
Words Before Pixels: Selective Modality Routing for Vision-Language Model Unlearning
Laura Yao, Haochen Zhang, Jinhao Duan and 2 more
Vision-language model (VLM) unlearning is often treated as an objective-design problem: given multimodal forget data, the goal is to optimize a loss that removes undesirable behavior while preserving retained capabilities. We argue that this view overlooks an equally important data-centric question: how should each forget example be represented before optimization? Existing methods typically keep image-conditioned failures in their original image-text form, implicitly assuming that pixels are the right route for unlearning whenever pixels appear in the input. We challenge this assumption by constructing modality-decomposed versions of VLM benchmarks, enabling controlled comparisons among image-only, text-only, and multimodal representations of the same examples. Our modality-mixing experiments show that unsafe instruction-following behavior is more effectively suppressed through textual representations than through images alone, while purely text-only unlearning can still weaken visual grounding when the target behavior depends on visual evidence. Motivated by this trade-off, we introduce ModRoute, a gradient-guided per-sample modality routing strategy that selectively converts high visual-pressure examples to text-only form while keeping the remaining examples multimodal. Notably, on VLGuard with LLaVA-1.5-7B, ModRoute has the strongest composite unlearning-utility score of 0.847 at $γ=0.5$, a 2.5% improvement over the best random-switching score and $\geq$46% better than pure multimodal or text unlearning. Overall, our results show that effective VLM unlearning depends not only on the objective, but also on the composition and per-sample modality routing of the unlearning data.