SAM2Dual is proposed, a training-free, plug-and-play inference-time enhancement that improves long-video robustness without updating model weights and Text-Aware Memory is presented, which extracts a compact word-level cue from early frames and uses text embeddings to reweight memory contributions based on semantic compatibility.
Abstract
Long-term video object segmentation (VOS) remains challenging due to error accumulation under extended occlusions, re-appearance, and scene changes. Although SAM2 provides strong zero-shot performance, its streaming memory can amplify drift over long horizons when recent, unreliable predictions dominate the memory state. We propose SAM2Dual, a training-free, plug-and-play inference-time enhancement that improves long-video robustness without updating model weights. SAM2Dual introduces a Dual Memory design that explicitly separates (i) short-term memory for rapid local adaptation and (ii) long-term memory built via interval-based sampling to preserve global identity cues, combined through a gated fusion strategy. In addition, we present Text-Aware Memory (TAM), which extracts a compact word-level cue from early frames and uses text embeddings to reweight memory contributions based on semantic compatibility, supporting identity preservation when visual evidence becomes weak or ambiguous. Across long-term benchmarks, SAM2Dual consistently improves stability on long videos, raising J&F from 49.33 to 50.65 on MOSEv2 and achieving consistent gains on LVOSv2.
This work proposes RECAP-Forcing, a training-free inference method with no additional learnable parameters that consistently improves visual quality and semantic fidelity across multiple strong baselines and outperforms existing memory methods.
LongVU-TTT is introduced, which inserts a convolutional Test-Time Training (TTT) resampler with causal fast-weight updates between the vision encoder and the LLM, and is stronger than attention- and fixed-state recurrent resamplers across three benchmarks.
Mahmoud Ahmed, Sameh Abdulah, Olatunji Ruwase et al.· 0 citations
This work proposes to decouple the frequencies of memory updates and Memory Caching, and introduces two critical mechanisms: an auxiliary Memory Loss that forces persistent internalization of the scene, and a Memory Caching strategy that regularizes active weights against catastrophic drift.
Baback Elmieh, Lynn Tsai, Zeman Li et al.· arXiv.org· 0 citations
Scaling video generation to long durations reveals a critical bottleneck: current models lack robust long-term memory. This deficiency can be studied along two critical aspects: object permanence, the ability to precisely reproduce the appearance of objects upon re-entry; and memory capacity, the ability to process ultra-long context and use information from distant history. Robust long-term memory requires both: object permanence without sufficient context handling limits the temporal scope, while long context length without permanence fails to maintain identity. To address this, we present Ring Forcing, an autoregressive video diffusion framework designed to robustly construct and precisely utilize long-term memory. Our ring-structured training strategy enforces retrieval from distant history, effectively reconciling the trade-off between strict historical adherence and generative diversity. To expand memory capacity, we introduce a compression and timestep composition strategy. Under fixed sequence length constraints, this method extends the effective historical span to minutes-long durations and achieves a comprehensive receptive field over the entire history. Furthermore, we present a sparse RoPE mechanism to enable flexible, scalable memory adaptation while fully exploiting pre-trained priors. Extensive experiments demonstrate that Ring Forcing achieves superior minutes-long coherence and object permanence, significantly outperforming state-of-the-art methods.
Bowen Xue, Brandon Y. Feng, Chenguo Lin et al.· 0 citations
This paper introduces TinyMem, a model built upon compact multimodal memory for long-form video action detection that outperforms a range of state-of-the-art models on AVA v2.2 while using 5 times fewer memory tokens than the baseline with dense visual memory embeddings.
Rui Tian, Qi Dai, Hang-Rui Hu et al.· IEEE Transactions on Pattern...· 0 citations
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