Computer Vision Lab

Beyond recognizing what is in a single frame, video understanding requires reasoning about how content unfolds over time — segmenting a stream into meaningful units, anticipating what comes next, locating precise moments of change, answering questions that require following a narrative, and tracking specific objects reliably as they move and occlude. The lab has a sustained program across all of these temporal reasoning problems, and a defining trend across the group's recent work is treating generative modeling — diffusion in particular — and large language models not as end tasks in themselves, but as flexible backbones for representing and reasoning about temporal structure. Goal: develop a 3D symmetry detection method robust to partial observations (occlusion, incomplete scans) and shape deformation (physical damage, noisy scans), then validate it on real 3D shapes captured with an RGBD camera. Building on the lab's work on correspondence-based symmetry detection, the intern will treat symmetry detection as a self-matching problem, using geometry-aware biases such as group-equivariance to handle noisy, incomplete real-world geometry. Tasks: design/implement the pipeline, train and evaluate on synthetic and real RGBD data, and analyze robustness to occlusion and deformation. Requirements: systematic programming skills and linear algebra, and basic knowledge of (or interest in) abstract algebra and group theory. Prior 3D vision/deep learning experience is a plus, not required. Goal: Enhance the robustness of robot action policies, such as vision-language-action (VLA) models and world-action models (WAM). Specifically, we explore neural network architectures and training strategies that enable these models to operate robustly against visual input degradation, paraphras