Abstract In a hectic Continuous Integration (CI) environment, where several builds are triggered concurrently, legitimate build failures (e.g., not caused by flaky tests) may not always be related to the current push. These unrelated build failures can burden developers as they devote hours to attest whether errors are truly associated with their present changes. In this paper, we extract 77,354 CI build failures from 7 open source projects to understand and identify unrelated build failures. We attempt to provide an indication for developers about whether a build failure is likely to be related to the current push or not. Our results reveal that developers likely invest a median of 4 hours to determine whether a build failure is (un)related to their pushes. We perform a document analysis on a sample of 371 unrelated build failures (based on the 95% confidence level and 5% confidence interval from 10,316 potentially unrelated failures) to understand why build failures are deemed as unrelated by developers. The themes generated from our document analysis reveal that unrelated tests failures represent 20% of the cases of why build failures are deemed unrelated by developers. To predict whether a build failure is unrelated to the current push, we extract 33 features from issue reports, issue comments, and from the commits pertaining to the triggering push. We build semi-supervised PU-learning models over seven Apache projects and achieve precision ranging from $$0.70 \pm 0.01$$ to $$0.88 \pm 0.02$$ , recall ranging from $$0.30 \pm 0.03$$ to $$1.00 \pm 0.00$$ , and F1-scores ranging from $$0.44 \pm 0.03$$ to $$0.91 \pm 0.00$$ , while the area under the ROC curve (AUC) spans $$0.63 \pm 0.02$$ to $$0.97 \pm 0.03$$ . Our analysis of feature importance reveals that (i) the time taken from a submitted patch to the build-triggering push (CI latency), (ii) build failures sharing similar error messages with recent failures, and (iii) the number of comments preceding the build failure, are all efficient indicators for identifying potential unrelated build failures. The semi-supervised approach proposed in this work can help developers identify build failures that are unrelated to their current push, providing actionable guidance such as re-running builds, inspecting infrastructure logs, or prioritizing code-level debugging based on prediction outcomes.

Referring expression counting (REC) algorithms are for more flexible and interactive counting ability across varied fine-grained text expressions. However, the requirement for fine-grained attribute understanding poses challenges for prior arts, as they struggle to accurately align attribute information with correct visual patterns. Given the proven importance of “visual density”, it is presumed that the limitations of current REC approaches stem from an under-exploration of “contextual attribute density” (CAD). In the scope of REC, we define CAD as the measure of the information intensity of one certain fine-grained attribute in visual regions. To model the CAD, we propose a U- shape CAD estimator in which referring expression and multi-scale visual features from GroundingDINO can interact with each other With additional density supervision, we can effectively encode CAD, which is subsequently decoded via a novel attention procedure with CAD-refined queries. Integrating all these contributions, our framework significantly outperforms state-of-the-art REC methods, achieves 30% error reduction in counting metrics and a 10% improvement in localization accuracy. The surprising results shed light on the significance of contextual attribute density for REC. Code will be at github.com/Xu3XiWang/CAD-GD.

Accurate prediction of multi-agent future trajectories is crucial for autonomous driving systems to make safe and efficient decisions. Trajectory refinement has emerged as a key strategy to enhance prediction accuracy. However, existing refinement methods often overlook the topological relationships between trajectories, which are vital for improving prediction precision. Inspired by braid theory, we propose a novel trajectory refinement approach, Soft-Braid Refiner (SRefiner), guided by the soft-braid topological structure of trajectories using Soft-Braid Attention. Soft-Braid Attention captures spatio-temporal topological relationships between trajectories by considering both spatial proximity and vehicle motion states at ``soft intersection points". Additionally, we extend this approach to model interactions between trajectories and lanes, further improving the prediction accuracy. SRefiner is a multi-iteration, multi-agent framework that iteratively refines trajectories, incorporating topological information to enhance interactions within traffic scenarios. SRefiner achieves significant performance improvements over four baseline methods across two datasets, establishing a new state-of-the-art in trajectory refinement. Code is here https://github.com/Liwen-Xiao/SRefiner.

Class-agnostic counting (CAC) aims to count objects of interest from a query image given few exemplars. This task is typically addressed by extracting the features of query image and exemplars respectively and then matching their feature similarity, leading to an extract-then-match paradigm. In this work, we show that CAC can be simplified in an extract-and-match manner, particularly using a vision transformer (ViT) where feature extraction and similarity matching are executed simultaneously within the self-attention. We reveal the rationale of such simplification from a decoupled view of the self-attention.The resulting model, termed CACViT, simplifies the CAC pipeline into a single pretrained plain ViT. Further, to compensate the loss of the scale and the order-of-magnitude information due to resizing and normalization in plain ViT, we present two effective strategies for scale and magnitude embedding. Extensive experiments on the FSC147 and the CARPK datasets show that CACViT significantly outperforms state-of-the-art CAC approaches in both effectiveness (23.60% error reduction) and generalization, which suggests CACViT provides a concise and strong baseline for CAC. Code will be available.

Large-scale datasets with point-wise semantic and instance labels are crucial to 3D instance segmentation but also expensive. To leverage unlabeled data, previous semi-supervised 3D instance segmentation approaches have explored self-training frameworks, which rely on high-quality pseudo labels for consistency regularization. They intuitively utilize both instance and semantic pseudo labels in a joint learning manner. However, semantic pseudo labels contain numerous noise derived from the imbalanced category distribution and natural confusion of similar but distinct categories, which leads to severe collapses in self-training. Motivated by the observation that 3D instances are non-overlapping and spatially separable, we ask whether we can solely rely on instance consistency regularization for improved semi-supervised segmentation. To this end, we propose a novel self-training network InsTeacher3D to explore and exploit pure instance knowledge from unlabeled data. We first build a parallel base 3D instance segmentation model DKNet, which distinguishes each instance from the others via discriminative instance kernels without reliance on semantic segmentation. Based on DKNet, we further design a novel instance consistency regularization framework to generate and leverage high-quality instance pseudo labels. Experimental results on multiple large-scale datasets show that the InsTeacher3D significantly outperforms prior state-of-the-art semi-supervised approaches.