ETCTrack compresses template tokens by 60% in visual trackers via an adaptive compressor and hierarchical interaction, cutting MACs 21.4% with 0.4% accuracy drop on seven benchmarks.
Improved baselines with visual instruction tuning
2 Pith papers cite this work. Polarity classification is still indexing.
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Vision-language models fail at zero-shot detection of climate-specific classes in social media videos, while DINOv2 and ConvNeXt V2 embeddings yield meaningful clusters via minimum-cost multicut.
citing papers explorer
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An Efficient Token Compression Framework for Visual Object Tracking
ETCTrack compresses template tokens by 60% in visual trackers via an adaptive compressor and hierarchical interaction, cutting MACs 21.4% with 0.4% accuracy drop on seven benchmarks.
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ClimateVID -- Social Media Videos Analysis and Challenges Involved
Vision-language models fail at zero-shot detection of climate-specific classes in social media videos, while DINOv2 and ConvNeXt V2 embeddings yield meaningful clusters via minimum-cost multicut.