Video inpainting for object removal is scored over the whole hole, although the background behind the object is visible in other frames of the shot for some hole pixels and in no frame for others. A whole-hole score therefore mixes copying with generation and cannot show which of the two a remover does well. To separate the two, we propose recoverability-stratified evaluation, in which a point tracker run on the clean video labels each hole pixel by whether and how far away it is observed, so that fidelity and temporal stability can be reported per stratum against a copy reference. To reuse the observed content, we also propose evidence-guided sampling (EGS), which blends tracked colors into the clean-latent prediction of a video diffusion remover without training. On DAVIS, the perceptual advantage of three diffusion removers over flow propagation shrinks where the content is visible a few frames away (LPIPS difference-in-differences +0.046 to +0.056 against E2FGVI-HQ), and real paired captures show the same pattern for two of them. Their DAVIS hole warping error is two to four times that of ProPainter, and copying along clean-video tracks lowers their recoverable LPIPS by a quarter to two fifths. EGS closes 43% and 56% of the gap to this copy reference on held-out DAVIS when its colors come from clean-video tracks. With colors tracked on the masked input, a pre-registered confirmation finds no gain on MiniMax-Remover and a loss on ROSE. Diffusion removers thus leave observed content unused on DAVIS, and training-free reuse does not yet hold with tracked evidence.
Bandwidth-limited aerial cameras must compress their images before a remote detector can find the many tiny objects in them. Threshold-based region-of-interest (ROI) coding sends only confidently detected regions, and because tiny objects provide limited visual evidence and often score low, it can discard the regions that contain them. We propose rate-utility region coding (RUO), which sends a region when its expected detection utility justifies its measured byte cost, choosing to drop it or to send it at low or high quality with one multiplier shared across images. RUO trains no model, uses a standard image codec and decodes in a few milliseconds. On all 1,610 VisDrone-DET test-dev images, RUO uses 42.4% fewer bits than a tuned threshold-based ROI coder, averaged over their shared tiny-object average precision (AP) range. RUO also needs fewer bits for tiny objects than the AV1 Image File Format (AVIF), High Efficiency Video Coding (HEVC) and three published learned codecs, and its advantage over threshold-based ROI coding persists when the sender and receiver use different detectors. For AP over all object sizes RUO needs 25.4% more bits than the tuned ROI coder, because its allocation drops large regions first at low rates, so it suits tasks that target tiny objects. We plan to release the code.
3D Gaussian Splatting sorts every splat by depth in every frame. Viewers that draw splats with the hardware rasterizer and sort them on the CPU, as most web and mobile viewers do, spend most of the frame time in this sort. We present AbacusGS, which stores the blending order of a 3DGS scene in the scene layout. AbacusGS groups the splats into octree boxes of at most 128 splats and stores each box in order along an axis chosen at write time. In each frame, AbacusGS sorts only the visible boxes, and one dot product per box decides whether the box is read forward or backward. A short order-aware fine-tuning converts an existing 3DGS model, and the over operator of 3DGS stays unchanged. On 13 standard scenes, AbacusGS loses 0.13 dB PSNR on average against an equally fine-tuned 3DGS model. In an OpenGL viewer that orders splats on the CPU, AbacusGS renders frames 4.5 times faster than per-splat sorting. We also present a compact format of 17 bytes per splat and a CUDA tile renderer that use the same order. The code will be released.
We present See, Think, Use, a protocol that traces each answer of a vision-language model to perception, reasoning or the use of perception. The diagnosis shows a developer which stage to improve and directs test-time compute to the stage that fails. Benchmarks score the final answer, and an answer that rests on the image earns the same credit as a guess or a memorized prior. The protocol asks the model two extra questions per item and needs no access to its weights. A localization probe asks for a box around every instance and measures seeing, and a text-replacement probe answers from a description and measures thinking. Use is the rate at which the answer follows the model's own perception across counterfactual image pairs. The rate needs no answer labels, because the question and its prior are the same on both images. On two model families and six datasets the protocol shows that the knob that helps follows the task family. Answers follow the model's perception except on images that contradict a common prior, and asking the model to localize first repairs this failure when its boxes are right. Allocating compute by the measured stages comes close to the best uniform policy at lower cost and beats a random allocation of the same compute wherever a knob raises the short stage. We will release the code, the prompts and the per-question outputs.
Shiqiang Tian, Cheng Ding, Qin Chen East China Normal University, Jie Zhou, Liang He East China Normal University
原文摘要
Knowledge editing is a promising method for updating Large Language Models efficiently. However, previous studies often suffer from poor specificity in continual editing, as they typically focus on single edits or preventing knowledge forgetting. To address this, we propose TamEdit, a trajectory-aware meta-learning method that preserves specificity for continual knowledge editing. TamEdit unifies three levels: Inner Optimization performs multi-step fast fine-tuning on the single edit; Trajectory-based Editing unifies continual edits with a growing memory; and Outer Optimization leverages meta-learning to distill cross-task strategies for preserving specificity. By capturing the relationships between different single edits within the trajectory, our method learns how to effectively avoid specificity drift. Experiments across multiple LLMs show TamEdit significantly outperforms baselines in continual editing, improving specificity by 14.81% with fast speed (requiring only 8.84% of the time cost of most baselines), while preserving general capabilities.
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表