Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv:2609.18493v1 Announce Type: new Abstract: Semantic labels for indoor mobile laser scanning (MLS) frames remain largely absent from current point cloud semantic segmentation benchmarks, which ma...
arXiv:2609.18623v1 Announce Type: new Abstract: State-of-the-art vision-language-action models (VLA) for autonomous driving face critical limitations: excessive parameter counts, inefficient high-res...
arXiv:2609.18632v1 Announce Type: new Abstract: Multi-modal talking avatar synthesis aims to generate realistic talking videos from a reference portrait and speech. Despite rapid progress in diffusio...
arXiv:2609.18773v1 Announce Type: new Abstract: Long-range infrared imaging frequently confronts dense target clusters whose diffraction-limited signatures merge into a single indistinguishable blob,...
arXiv:2608.03323v2 Announce Type: replace Abstract: Estimating room layouts from multi-view imagery is a core task for indoor scene understanding. Existing methods are typically limited either by poo...
arXiv:2608.00239v2 Announce Type: replace Abstract: Learning transferable representations from CT-report pairs requires combining whole-volume context with anatomy-specific evidence. Existing methods...
arXiv:2608.13014v2 Announce Type: replace Abstract: Understanding hand-object interaction from egocentric vision is essential for modeling how people physically engage with the surrounding world. Yet...
arXiv:2609.19042v1 Announce Type: cross Abstract: In medical imaging, developing robust deep learning models requires data from various domains. However, regulatory policies protecting patient privac...
arXiv:2412.00176v4 Announce Type: replace Abstract: We explore whether pre-training on datasets with paintings is necessary for a model to learn an artistic style with only a few examples. To investi...
arXiv:2505.07622v2 Announce Type: replace Abstract: Cross-view geo-localization is a promising solution for large-scale localization problems, requiring the sequential execution of retrieval and metr...
arXiv:2506.02294v4 Announce Type: replace Abstract: Large foundation models trained on extensive datasets demonstrate strong zero-shot capabilities in various domains. Knowledge distillation has beco...
arXiv:2603.14005v2 Announce Type: replace Abstract: To generalize deepfake detectors to future unseen forgeries, most existing methods attempt to simulate the dynamically evolving forgery types using...
arXiv:2603.29732v2 Announce Type: replace Abstract: Single-pixel imaging (SPI) is a promising imaging modality with distinctive advantages in strongly perturbed environments. Existing SPI methods lac...
arXiv:2605.29761v2 Announce Type: replace Abstract: Compositional implicit surface representations model scenes as collections of objects, each encoded by a Signed Distance Field (SDF). A fundamental...
arXiv:2606.10174v2 Announce Type: replace Abstract: Wildfire detection and monitoring are critical for mitigating fire spread and reducing environmental and infrastructural damage. In this work, we i...
arXiv:2607.23238v3 Announce Type: replace Abstract: Masked image modeling has become a dominant paradigm for SAR pre-training, yet the design of the reconstruction target remains fundamentally unsett...
arXiv:2502.16888v3 Announce Type: replace-cross Abstract: Motivated by the remarkable success of Bayesian additive regression trees (BART) in regression modelling, we propose a novel nonparametric Ba...
arXiv:2609.13617v2 Announce Type: replace Abstract: OpenAI released ChatGPT Images 2.5 on 8 September 2026, advertising more precise local edits, better consistency across edits, more faithful refere...
FedPGT introduces a progressive gradient transmission scheme for vehicular federated learning over time‑varying channels, where vehicles send high‑magnitude gradient entries according to instantaneous channel conditions. The authors derive a convergence bound showing diminishing returns governed by a power‑law decay, and formulate a stochastic optimization problem that is solved via a Lyapunov drift‑plus‑penalty approach with per‑slot surrogate variables. A low‑complexity resource allocation algorithm is proposed, and experiments on CIFAR‑10 and Argoverse demonstrate a 3.65% accuracy gain and a 12.66% reduction in displacement error compared to state‑of‑the‑art baselines.
OBC‑Prune introduces an outcome‑based calibration approach for pruning large reasoning models, focusing on the causal importance of each reasoning sentence rather than uniform activation salience. By pairing correct and incorrect rollouts and using intervention‑based analysis, it assigns per‑token weights that guide one‑shot pruning methods such as SparseGPT, Wanda, and ALPS. Experiments on DeepSeek‑R1‑Distill‑Qwen models show consistent accuracy gains and shorter reasoning traces across multiple benchmarks at 40‑50% sparsity.