#01Oct 30, 2026
cs.CV
PGGT-Loc: Primitive-Grounded Geometry Transformer for Feed-Forward Camera Localization
Hanqiao Ye, Yuzhou Liu, Mengqi Rong and 3 more
Camera localization methods are mostly multi-stage pipelines relying on dedicated and complex maps that are costly to construct, store, and maintain over time. Alternatively, well-structured maps that organize scenes with compact yet expressive primitives are often readily available, providing rich structural and semantic cues that humans can interpret. However, their potential for direct camera localization remains largely unexplored due to the significant modality gap between map primitives and visual observations. This motivates a new concept for localization, which estimates camera poses by grounding query observations to map primitives. In this paper, we instantiate the concept in the context of planar primitives with PGGT, the Primitive-Grounded Geometry Transformer network. Given a single query image and a set of planar map primitives, the network is trained to predict multiple localization targets for feed-forward 6-DoF camera localization that generalizes to unseen environments. With this straightforward and scalable architecture, our method pushes the state-of-the-art localization performance on both dense surface maps and minimal layout models. The code and models are available at https://github.com/3dv-casia/PGGT-Loc
#02Oct 30, 2026
cs.AI
ReuScope: Auditing Data Reuse and Evaluation Sensitivity in Capsule Vision 2024
Eunseob Choi, Kyeonghun Kim, Hyuk-Jae Lee and 1 more
We develop ReuScope, a protocol for auditing data reuse and evaluation sensitivity, using Capsule Vision 2024 as a case study. It combines measurements of data relationships with two comparisons: changing the evaluation population while holding predictions fixed, and changing training data while holding evaluation fixed. Byte-identical files cross the public training-validation boundary, and all 41 Kvasir-Capsule recordings that contribute frames occur in both partitions. Shared source recordings establish cross-split overlap; missing cross-recording patient identifiers prevent counting unique shared patients. Across ten paired seeds, removing all training rows attributed to Kvasir-Capsule, plus five rows from other sources flagged as near-matches to it, lowers public-validation balanced accuracy from 82.5% to 61.2%. Evaluating the same fitted models on the validation rows of the other sources reverses the comparison: removal improves balanced accuracy by 1.38 percentage points. The public-validation drop measures sensitivity to source removal, not an amount of leakage-induced inflation. Matched controls show that sample size and class counts alone do not explain the observed score differences. An exploratory experiment removes the training neighbors of validation frames that have close training matches, or equally many distant training rows, and yields a difference-in-differences estimate, relative to matched unflagged frames, of -0.355 percentage points in true-class probability; the paired-t interval for accuracy spans zero. The resulting procedure distinguishes observed reuse from conditional performance sensitivity and specifies the population, class support and uncertainty unit for each conclusion.
#03Oct 30, 2026
cs.CV
TwinPast: Benchmarking Historical Evidence Use in Streaming Video Understanding with Evidence Contrastive Video Pairs
Haotian Yang, Yong Chen, Cheng Peng and 4 more
High accuracy on streaming video benchmarks does not establish whether a model uses visual history. Our review of OVO-Bench and StreamingBench finds that recent frames suffice for 32% of sampled historical questions, while models also answer many questions correctly from the question and options alone. We introduce TwinPast to test whether answers correctly follow changes in history, using 350 human-reviewed video pairs spanning five historical tasks. Each pair holds the recent video context, question, and ordered options fixed while changing critical historical evidence and the correct answer. Each video is evaluated independently, and PairAcc requires correct answers for both histories. We evaluate 34 model and input configurations. For Qwen3-VL-8B and DeepSeek-Flash, historical inputs improve performance on TwinPast, reversing the recent-frame advantage on existing benchmarks. The strongest evaluated streaming system reaches 49.14% PairAcc, compared with 66.57-91.43% for the evaluated general-purpose models. This gap persists across all five tasks. Case studies further show that models select states from the wrong time or contradict recorded event order despite retaining the relevant evidence. These findings highlight the need to connect historical memory and retrieval with reasoning that uses evidence according to the question's temporal references and event relations.
#04Oct 30, 2026
cs.CV
GeoFlip: Benchmarking Geometry-Sensitive Physics in Video Generation
Yuqi Li, Junming Liu, Bowen Hu and 4 more
Video generation models synthesize realistic motion, but can they respond correctly to changes in initial geometry? We introduce GeoFlip, a video generation benchmark centered on physical outcome flips induced by geometry. With shared physical settings and textual prompts held fixed, GeoFlip applies controlled interventions to support extent, object placement, and mechanism configuration, constructing paired scenes with contrasting reference events, such as remaining stable versus falling or tipping. These pairs test whether models translate geometric differences in the first frame into correct physical evolution. The benchmark comprises 482 scene pairs and 964 reference videos across six physical categories, with reference outcomes established through procedural scene construction, physics simulation, and numerical validation and verification of physical events. We evaluate 16 models for image to video generation along three dimensions: event correctness, structural integrity, and similarity to reference videos. We examine evaluation consistency with independent vision language judges. On the 442 conditions shared by all models, the highest event correctness averaged across tasks is 27.98%, revealing substantial limitations in physical generation conditioned on geometry. Model strengths vary across physical mechanisms, and the three evaluation dimensions capture distinct capabilities. Typical failures include ignoring geometric changes, generating incorrect object motion, bending rigid objects, and introducing additional components. Rank correlations between judges reach 0.906 for event correctness and 0.929 for structural integrity. By combining controlled geometric interventions, verifiable physical references, and multidimensional evaluation, GeoFlip assesses progress from visual realism toward physical consistency conditioned on geometry.
#05Oct 30, 2026
cs.CV
Open-Weight MLLMs Cannot Compare What They Cannot Name
Wenxin Su, Anna Kreshuk
Multimodal Large Language Models (MLLMs) are increasingly capable of understanding images and performing complex visual reasoning, suggesting the possibility of using them to automate biological image analysis. This need is becoming increasingly urgent as advances in technology continue to generate ever larger and more diverse microscopy datasets, already exceeding the scale at which manual visual analysis is feasible. With the final aim to use MLLMs for visual comparison of complex phenotypes, we investigate a very simple baseline: comparison of the size of body parts in synthetic images of ants. We find that state-of-the-art open-weight MLLMs fail for some body parts, but not for others, even though the comparison itself is visually trivial for all of them. We evaluate different in-context learning strategies and analyze performance on the related object recognition task. Our results suggest a recognition-driven grounding failure: structures that the models cannot recognize are instead associated with a recognizable semantic concept, impairing the grounding and comparison of both. Further analysis of the vision encoder is consistent with the hypothesis that fine-grained visual structure may be less well preserved without semantic support. Together, our results reveal a surprising limitation of current MLLMs: semantic recognition can gate simple visual comparisons that do not intrinsically require semantic understanding. Our code and data are available at https://github.com/kreshuklab/Open-Weight-MLLMs-Cannot-Compare-What-They-Cannot-Name