Current instance segmentation models achieve high performance on average predictions, but lack principled uncertainty quantification: their outputs are not calibrated, and there is no guarantee that a predicted mask is close to the ground truth. To address this limitation, we introduce a conformal prediction algorithm to generate adaptive confidence sets for instance segmentation. Given an image and a pixel coordinate query, our algorithm generates a confidence set of instance predictions for that pixel, with a provable guarantee for the probability that at least one of the predictions has high Intersection-Over-Union (IoU) with the true object instance mask. We apply our algorithm to instance segmentation examples in agricultural field delineation, cell segmentation, and vehicle detection. Empirically, we find that our prediction sets vary in size based on query difficulty and attain the target coverage, outperforming existing baselines such as Learn Then Test, Conformal Risk Control, and morphological dilation-based methods. We provide versions of the algorithm with asymptotic and finite sample guarantees.

Justice Department expands FACE Act enforcement to synagogue protests, with Assistant Attorney General Harmeet Dhillon citing cases against accused protesters.

Fox News poll finds 60% of voters believe you can find love with someone whose political views oppose their own this Valentine's Day season.

OpenAI President Greg Brockman and venture capitalist Ben Horowitz are funding a political group to support lawmakers who support AI regulatory frameworks.

Is there anything afuture user could do to mitigate theseundesirableharms? Although ConfLab's long-term vision is towards developing technology to assist individuals in navigating social interactions, the data could also affect a community in unintended ways: for instance, cause worsened social satisfaction, alackofagency,stereotype newcomers andveterans, or benefit only those members of the community who make use of resulting applications at the expense of the rest. More nefarious uses involve exploiting the data for developing methods that harmfully surveilorprofile people.

Specifically, as part of the question, it contains a referring query that references related video contexts, called referred context .

D.3 Open source performance on mini test set . . . . . . . . . . . . . . . . . . . . . A.1 V ersion 2 We have fixed some bugs in the evaluation code, resulting in slight differences compared to the previous release. The issue was that 149 samples were not evaluated in the previous version, and these have now been included in the new update. A.2 V ersion 3 We have clarified certain statements and added experimental results to address the reviewer's questions. B.1 Limitations Despite these advancements, our dataset does exhibit certain limitations, largely stemming from inherited biases from the source datasets: Currently, we only address scenarios where both the question and the answer span a single time duration. Given a question, the annotated time span must be a single, continuous duration, which might be limiting for all scenes. The presence of noisy or inaccurate annotations in the source datasets, including captions and timestamps, poses a challenge. Despite our efforts, some of these errors could not be automatically filtered out. The extent of this issue is detailed in the qualitative visualization conducted by our human reviewers, as presented in supplementary. The average duration of ground truth events in our dataset is relatively long. This characteristic has the unintended consequence of hindering the models' ability to detect and analyze fine-grained actions within shorter video segments. These drawbacks highlight areas for potential improvement and indicate the necessity for ongoing refinement to ensure the creation of more accurate and unbiased video language models. B.2 Social Impact Though we provide an assessment of temporal reasoning and moment localization, the types and scene diversity are still limited. We inherit the video classes from the two source video datasets, which may not be sufficient for a comprehensive assessment of all kinds of temporal reasoning. This limitation could introduce a bias. For both curated data and video data, they do not contain any personally identifiable information. Besides, some of the video samples in the source datasets might be slightly uncomfortable depending on the viewer. For example, some videos discuss tattoos and piercings, and some of them present news about social events including demonstrations or war reports. However, we only release the data of curated question-answer and time span.

Short-term load forecasting (STLF), which typically ranges from hour-ahead to a few days ahead, plays a multitude of critical roles.

Recently, various strategies for distributed training of large language models (LLMs) have been proposed.