MATRIX: Mask Track Alignment for Interaction-aware Video Generation

Video DiTs have advanced video generation, yet they still struggle to model multi-instance or subject-object interactions. This raises a key question: How do these models internally represent interactions? To answer this, we curate MATRIX-11K, a video dataset with interaction-aware captions and multi-instance mask tracks. Using this dataset, we conduct a systematic analysis that formalizes two perspectives of video DiTs: semantic grounding, via video-to-text attention, which evaluates whether noun and verb tokens capture instances and their relations; and semantic propagation, via video-to-video attention, which assesses whether instance bindings persist across frames. We find both effects concentrate in a small subset of interaction-dominant layers. Motivated by this, we introduce MATRIX, a simple and effective regularization that aligns attention in specific layers of video DiTs with multi-instance mask tracks from the MATRIX-11K dataset, enhancing both grounding and propagation. We further propose InterGenEval, an evaluation protocol for interaction-aware video generation. In experiments, MATRIX improves both interaction fidelity and semantic alignment while reducing drift and hallucination. Extensive ablations validate our design choices. Codes and weights will be released.

S^4M: Boosting Semi-Supervised Instance Segmentation with SAM

Semi-supervised instance segmentation poses challenges due to limited labeled data, causing difficulties in accurately localizing distinct object instances. Current teacher-student frameworks still suffer from performance constraints due to unreliable pseudo-label quality stemming from limited labeled data. While the Segment Anything Model (SAM) offers robust segmentation capabilities at various granularities, directly applying SAM to this task introduces challenges such as class-agnostic predictions and potential over-segmentation. To address these complexities, we carefully integrate SAM into the semi-supervised instance segmentation framework, developing a novel distillation method that effectively captures the precise localization capabilities of SAM without compromising semantic recognition. Furthermore, we incorporate pseudo-label refinement as well as a specialized data augmentation with the refined pseudo-labels, resulting in superior performance. We establish state-of-the-art performance, and provide comprehensive experiments and ablation studies to validate the effectiveness of our proposed approach.

Referring Video Object Segmentation via Language-aligned Track Selection

Referring Video Object Segmentation (RVOS) seeks to segment objects throughout a video based on natural language expressions. While existing methods have made strides in vision-language alignment, they often overlook the importance of robust video object tracking, where inconsistent mask tracks can disrupt vision-language alignment, leading to suboptimal performance. In this work, we present Selection by Object Language Alignment (SOLA), a novel framework that reformulates RVOS into two sub-problems, track generation and track selection. In track generation, we leverage a vision foundation model, Segment Anything Model 2 (SAM2), which generates consistent mask tracks across frames, producing reliable candidates for both foreground and background objects. For track selection, we propose a light yet effective selection module that aligns visual and textual features while modeling object appearance and motion within video sequences. This design enables precise motion modeling and alignment of the vision language. Our approach achieves state-of-the-art performance on the challenging MeViS dataset and demonstrates superior results in zero-shot settings on the Ref-Youtube-VOS and Ref-DAVIS datasets. Furthermore, SOLA exhibits strong generalization and robustness in corrupted settings, such as those with added Gaussian noise or motion blur. Our project page is available at https://cvlab-kaist.github.io/SOLA