9 accepted papers
Recent studies operationalize self-improvement through coding agents that edit their own codebases, grow a tree of self-modifications through expansion strategies that favor higher software engineering benchmark performance, considering that this implies more promising subsequent self-modifications…
Contemporary evaluation techniques are inadequate for agentic systems. These approaches either focus exclusively on final outcomes---ignoring the step-by-step nature of the thinking done by agentic systems---or require excessive manual labour. To address this, we introduce the **Agent-as-a-Judge** f…
World modelling is essential for understanding and predicting the dynamics of complex systems by learning both spatial and temporal dependencies. However, current frameworks, such as Transformers and selective state-space models like Mambas, exhibit limitations in efficiently encoding spatial and te…
Various human-designed prompt engineering techniques have been proposed to improve problem solvers based on Large Language Models (LLMs), yielding many disparate code bases. We unify these approaches by describing LLM-based agents as computational graphs. The nodes implement functions to process mul…
Recently, self-training strategies have shown outstanding performance in the unsupervised medical image registration field. These strategies use their own network to generate pseudo-displacement fields (PFs) to supervise network training. However, limited diversity and accuracy of these PFs hinder t…
Previous methods for Video Frame Interpolation (VFI) have encountered challenges notably the manifestation of blur and ghosting effects. These issues can be traced back to two pivotal factors: unavoidable motion errors and misalignment in supervision. In practice motion estimates often prove to be e…
Recent deep learning-based optical flow estimators have exhibited impressive performance in generating local flows between consecutive frames. However, the estimation of long-range flows between distant frames, particularly under complex object deformation and large motion occlusion, remains a chall…
Deep neural network (DNN) shows impressive performance on many tasks but they usually suffer from adversarial examples with human eyes invisible slight perturbation. Such examples can not be distinguished by human but can mislead DNN classifiers leading to its important role in DNN attack and defens…
Due to the low scan rate of LiDAR sensors, LiDAR point cloud streams usually have a low frame rate, which is far below that of other sensors such as cameras. This could incur frame rate mismatch while conducting multi-sensor data fusion. LiDAR point cloud temporal interpolation aims to synthesize th…