The paper creates a real-world corruption benchmark for promptable video object segmentation and proposes MoGA, which uses object-specific memory to improve robustness and temporal consistency under adverse conditions.
Youtube-vos: A large-scale video object segmentation benchmark
8 Pith papers cite this work. Polarity classification is still indexing.
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VEBENCH is the first benchmark with 3.9K videos and 3,080 human-verified QA pairs that measures LMMs on video editing technique recognition and operation simulation, revealing a large gap to human performance.
YOSE accelerates DiT video object removal up to 2.5x by using BVI for adaptive token selection and DiffSim to simulate unmasked token effects, while preserving visual quality.
LILA learns temporally consistent semantic and geometric pixel features from uncurated videos via linear in-context learning on off-the-shelf depth and motion cues, yielding empirical gains on video object segmentation, surface normal estimation, and semantic segmentation.
X2SAM unifies any-segmentation across images and videos in one MLLM by adding a Mask Memory module for temporal consistency and joint training on mixed datasets.
Cross-modal token modulation enables better fusion of appearance and motion cues in two-stream models, leading to state-of-the-art results in unsupervised video object segmentation.
PanoSAM2 adapts SAM2 with a Pano-Aware Decoder, Distortion-Guided Mask Loss, and Long-Short Memory Module to improve 360 video object segmentation, reporting +5.6 and +6.7 gains over base SAM2 on two benchmarks.
SAM 2 delivers more accurate video segmentation with 3x fewer user interactions and 6x faster image segmentation than the original SAM by training a streaming-memory transformer on the largest video segmentation dataset collected to date.
citing papers explorer
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Robust Promptable Video Object Segmentation
The paper creates a real-world corruption benchmark for promptable video object segmentation and proposes MoGA, which uses object-specific memory to improve robustness and temporal consistency under adverse conditions.
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VEBench:Benchmarking Large Multimodal Models for Real-World Video Editing
VEBENCH is the first benchmark with 3.9K videos and 3,080 human-verified QA pairs that measures LMMs on video editing technique recognition and operation simulation, revealing a large gap to human performance.
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YOSE: You Only Select Essential Tokens for Efficient DiT-based Video Object Removal
YOSE accelerates DiT video object removal up to 2.5x by using BVI for adaptive token selection and DiffSim to simulate unmasked token effects, while preserving visual quality.
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Featurising Pixels from Dynamic 3D Scenes with Linear In-Context Learners
LILA learns temporally consistent semantic and geometric pixel features from uncurated videos via linear in-context learning on off-the-shelf depth and motion cues, yielding empirical gains on video object segmentation, surface normal estimation, and semantic segmentation.
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X2SAM: Any Segmentation in Images and Videos
X2SAM unifies any-segmentation across images and videos in one MLLM by adding a Mask Memory module for temporal consistency and joint training on mixed datasets.
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CMTM: Cross-Modal Token Modulation for Unsupervised Video Object Segmentation
Cross-modal token modulation enables better fusion of appearance and motion cues in two-stream models, leading to state-of-the-art results in unsupervised video object segmentation.
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PanoSAM2: Lightweight Distortion- and Memory-aware Adaptions of SAM2 for 360 Video Object Segmentation
PanoSAM2 adapts SAM2 with a Pano-Aware Decoder, Distortion-Guided Mask Loss, and Long-Short Memory Module to improve 360 video object segmentation, reporting +5.6 and +6.7 gains over base SAM2 on two benchmarks.
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SAM 2: Segment Anything in Images and Videos
SAM 2 delivers more accurate video segmentation with 3x fewer user interactions and 6x faster image segmentation than the original SAM by training a streaming-memory transformer on the largest video segmentation dataset collected to date.