The paper introduces PIVOTMIPL, a method for video classification that tackles multi‑instance partial‑label learning by jointly assigning classes and temporal evidence. It uses occupancy‑regularized spherical matching to link contextualized video features with nonuniform temporal mass, and a dual‑marginal KL projection to build a structured teacher that refines class beliefs while maintaining temporal occupancy. Experiments on VCMIPL benchmarks derived from Breakfast, DoTA, and FineAction show that PIVOTMIPL outperforms existing MIPL algorithms in both effectiveness and efficiency.
arXiv:2609.34581v2 Announce Type: replace Abstract: Temporal video grounding is a key capability of advanced Multimodal Large Language Models (MLLMs) for the thorough understanding of video events, w...
arXiv:2608.29611v1 Announce Type: new Abstract: Unsupervised action segmentation aims to discover latent action categories and their temporal organization without action annotations. Optimal transpor...
Dual-State Slot Attention (DSSA) is a self‑supervised framework that separates per‑frame appearance from temporally stable identity within each slot, addressing conflicts that cause slot swapping in video object‑centric learning. It introduces a learned recurrent transition for identity updates and competition‑modulated aggregation to suppress weakly matching slots, thereby improving segmentation quality and temporal consistency. Experiments on MOVi‑C, MOVi‑D, and YouTube‑VIS show DSSA outperforms prior methods and enhances downstream object recognition and video dynamics prediction.
CoRE is a weakly supervised framework that learns fine-grained temporal and entity support for perceived risk in driving videos using only coarse video-level judgments. It first trains a video-level predictor, freezes it, and then uses structured interventions over candidate temporal regions or entity tracks to generate graded prediction-effect targets. These targets train a student model that can predict temporal and entity support directly from the original video, enabling fine-grained evidence localization without requiring detailed annotations.
arXiv:2608.20814v1 Announce Type: new Abstract: Though Multimodal Large Language Models (MLLMs) have shown impressive potential in video understanding, long video understanding (LVU) remains challeng...
arXiv:2609.40055v1 Announce Type: cross Abstract: On-policy distillation (OPD) provides dense supervision directly on student-generated trajectories, making it an effective post-training strategy for...
The paper introduces Clue-OPSD, a clue‑privileged on‑policy self‑distillation framework that improves long‑video understanding by focusing on short, question‑relevant clue intervals rather than the entire video. Experiments on multiple benchmarks and Qwen3.5 model scales show that this approach consistently outperforms standard backbone models and competes strongly with supervised post‑training baselines, all while requiring fewer input frames and no additional inference modules.
arXiv:2604. 06614v2 Announce Type: replace-cross Abstract: Prompt learning has gained significant attention as a parameter-efficient approach for adapting large pre-trained vision-language models to downstream tasks.
LDDR (Linear DPP Dynamic Resolution) is a training‑free, budget‑aware video frame sampling framework designed for multimodal large language models (MLLMs). It uses query‑aware Determinantal Point Process (DPP) selection in a task‑conditioned feature space, achieving a 3× runtime speedup over standard DPP baselines. By assigning a non‑uniform token budget—allocating more tokens to informative, non‑redundant frames and downscaling or pruning less useful ones—LDDR improves performance by 2.5 points in low‑budget settings and 1.6 points in high‑budget scenarios across four video benchmarks and multiple MLLM families.
arXiv:2608.05707v2 Announce Type: replace Abstract: Frame selection is essential for applying Large Multimodal Models (LMMs) to long videos due to severe frame redundancy and limited context windows....
arXiv:2610.00757v1 Announce Type: new Abstract: Long-video question answering is limited by the high cost of visual tokens and by the fixed context width of current VLMs. A long-video question may re...
arXiv:2608. 03269v1 Announce Type: cross Abstract: Video dataset distillation aims to compress a large video dataset into a compact surrogate set that preserves its training utility.