Nicholas C. Ikechukwu

Continual Panoptic Segmentation (CPS) requires methods that can quickly adapt to new categories over time. The nature of this dense prediction task means that training images may contain a mix of labeled and unlabeled objects. As nothing is known about these unlabeled objects a priori, existing methods often simply group any unlabeled pixel into a single "background" class during training. In effect, during training, they repeatedly tell the model that all the different background categories are the same (even when they aren’t). This makes learning to identify different background categories as they are added challenging since these new categories may require using information the model was previously told was unimportant and ignored. Thus, we propose a Future-Targeted Contrastive and Repulsive (FuTCR) framework that addresses this limitation by restructuring representations before new classes are introduced. FuTCR first discovers confident future-like regions by grouping model-predicted masks whose pixels are consistently classified as background but exhibit non-background logits. Next, FuTCR applies pixel-to-region contrast to build coherent prototypes from these unlabeled regions, while simultaneously repelling background features away from known-class prototypes to explicitly reserve representational space for future categories. Experiments across six CPS settings and a range of dataset sizes show FuTCR improves relative new-class panoptic quality over the state-of-the-art by up to 28%, while preserving or improving base-class performance with gains up to 4%.

Humans develop object orientation understanding progressively, from recognizing which way an object faces, to mentally rotating it, to reasoning about how multiple objects are oriented relative to each other. Yet current vision-language benchmarks treat orientation as an afterthought, conflating it with positional relationships and general scene understanding. We introduce Discriminative Orientation Reasoning Intelligence (DORI), a cognitively grounded hierarchical benchmark that establishes object orientation as the primary evaluation target. Building on stages of human orientation cognition, DORI decomposes orientation into four dimensions, each assessed at both coarse (categorical) and granular (metric) levels. Built from 13,652 images across 14 sources, DORI yields 33,656 multiple-choice questions spanning 67 object categories in real-world and synthetic environments. Its coarse to-granular design isolates orientation from confounds such as object recognition difficulty, scene clutter, and linguistic ambiguity through bounding-box isolation, standardized spatial reference frames, and structured prompts. Our evaluation of 24 state-of-the-art vision-language models reveals a consistent pattern: models competent on general spatial benchmarks remain near-random on object-centric orientation tasks. Even the best models achieve only 54.2% on coarse and 45.0% on granular judgments, with the largest drops on compound rotations and inter object reference frame shifts. Large coarse-to-granular gaps further expose that models rely on categorical heuristics rather than geometric reasoning, a limitation invisible to existing benchmarks. These findings establish orientation understanding as an unsolved challenge in multimodal systems, with direct implications for robotic manipulation, 3D scene reconstruction, and human-AI interaction