%2C%20we%20demonstrate%20that%20our%20method%20outperforms%20existing%20unsupervised%20methods%20with%20consistent%20margins%20on%20various%20evaluation%20metrics%2C%20and%20even%20achieves%20performance%20close%20to%20supervised%20methods.)
%20are%20pivotal%20in%20diverse%20online%20web%20platforms%20and%20have%20garnered%20considerable%20attention%20in%20recent%20years.%20However%2C%20previous%20studies%20overlook%20the%20challenges%20of%20(1)%20noisy%20multi-modal%20content%2C%20(2)%20noisy%20user%20feedback%2C%20and%20(3)%20aligning%20multi-modal%20content%20with%20user%20feedback.%20In%20order%20to%20tackle%20these%20challenges%2C%20we%20propose%20Denoising%20and%20Aligning%20Multi-modal%20Recommender%20System%20(DA-MRS).%20To%20mitigate%20multi-modal%20noise%2C%20DA-MRS%20first%20constructs%20item-item%20graphs%20determined%20by%20consistent%20content%20similarity%20across%20modalities.%20To%20denoise%20user%20feedback%2C%20DA-MRS%20associates%20the%20probability%20of%20observed%20feedback%20with%20multi-modal%20content%20and%20devises%20a%20denoised%20BPR%20loss.%20Furthermore%2C%20DA-MRS%20implements%20Alignment%20guided%20by%20User%20preference%20to%20enhance%20task-specific%20item%20representation%20and%20Alignment%20guided%20by%20graded%20Item%20relations%20to%20provide%20finer-grained%20alignment.%20Extensive%20experiments%20verify%20that%20DA-MRS%20is%20a%20plug-and-play%20framework%20and%20achieves%20significant%20and%20consistent%20improvements%20across%20various%20datasets%2C%20backbone%20models%2C%20and%20noisy%20scenarios.)
%20have%20gained%20widespread%20adoption%20for%20continuous%20mobile%20object%20detection%20(OD)%20tasks%2C%20particularly%20in%20autonomous%20systems.%20However%2C%20a%20prevalent%20issue%20in%20their%20deployment%20is%20the%20one-size-fits-all%20approach%2C%20where%20a%20single%20DNN%20is%20used%2C%20resulting%20in%20inefficient%20utilization%20of%20computational%20resources.%20This%20inefficiency%20is%20particularly%20detrimental%20in%20energy-constrained%20systems%2C%20as%20it%20degrades%20overall%20system%20efficiency.%20We%20identify%20that%2C%20the%20contextual%20information%20embedded%20in%20the%20input%20data%20stream%20(e.g.%20the%20frames%20in%20the%20camera%20feed%20that%20the%20OD%20models%20are%20run%20on)%20could%20be%20exploited%20to%20allow%20a%20more%20efficient%20multi-model-based%20OD%20process.%20In%20this%20paper%2C%20we%20propose%20SHIFT%20which%20continuously%20selects%20from%20a%20variety%20of%20DNN-based%20OD%20models%20depending%20on%20the%20dynamically%20changing%20contextual%20information%20and%20computational%20constraints.%20During%20this%20selection%2C%20SHIFT%20uniquely%20considers%20multi-accelerator%20execution%20to%20better%20optimize%20the%20energy-efficiency%20while%20satisfying%20the%20latency%20constraints.%20Our%20proposed%20methodology%20results%20in%20improvements%20of%20up%20to%207.5x%20in%20energy%20usage%20and%202.8x%20in%20latency%20compared%20to%20state-of-the-art%20GPU-based%20single%20model%20OD%20approaches.)
%20with%20cross-modal%20attention.%20The%20proposed%20multi-modal%20system%20outperforms%20the%20previous%20state-of-the-art%20multi-modal%20system%20by%208.53%25%20in%20the%20weighted%20average%20F1%20score.)
%20information%20from%20the%20platform%20and%20understand%20the%20event.%20The%20paper%20introduces%20a%20new%20MM%20multi-teacher-based%20game%20event%20detection%20framework%2C%20with%20the%20ultimate%20goal%20of%20constructing%20a%20comprehensive%20framework%20that%20enhances%20the%20comprehension%20of%20the%20ongoing%20game%20situation.%20While%20conventional%20MM%20models%20typically%20prioritise%20aligning%20MM%20data%20through%20concurrent%20training%20towards%20a%20unified%20objective%2C%20our%20framework%20leverages%20multiple%20teachers%20trained%20independently%20on%20different%20tasks%20to%20accomplish%20the%20Game%20Event%20Detection.%20The%20experiment%20clearly%20shows%20the%20effectiveness%20of%20the%20proposed%20MM%20multi-teacher%20framework.)
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![What Is Multimodal AI? A Complete Guide [2025]](https://www.solulab.com/wp-content/uploads/2024/03/Future-of-Multimodal-AI.jpg)
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.%20Key%20problems%20in%20genomics%20intrinsically%20involve%20multiple%20modalities%2C%20but%20it%20remains%20unclear%20how%20to%20adapt%20general-purpose%20sequence%20models%20to%20those%20cases.%20In%20this%20work%20we%20propose%20a%20multi-modal%20model%20that%20connects%20DNA%2C%20RNA%2C%20and%20proteins%20by%20leveraging%20information%20from%20different%20pre-trained%20modality-specific%20encoders.%20We%20demonstrate%20its%20capabilities%20by%20applying%20it%20to%20the%20largely%20unsolved%20problem%20of%20predicting%20how%20multiple%20RNA%20transcript%20isoforms%20originate%20from%20the%20same%20gene%20(i.e.%20same%20DNA%20sequence)%20and%20map%20to%20different%20transcription%20expression%20levels%20across%20various%20human%20tissues.%20We%20show%20that%20our%20model%2C%20dubbed%20IsoFormer%2C%20is%20able%20to%20accurately%20predict%20differential%20transcript%20expression%2C%20outperforming%20existing%20methods%20and%20leveraging%20the%20use%20of%20multiple%20modalities.%20Our%20framework%20also%20achieves%20efficient%20transfer%20knowledge%20from%20the%20encoders%20pre-training%20as%20well%20as%20in%20between%20modalities.%20We%20open-source%20our%20model%2C%20paving%20the%20way%20for%20new%20multi-modal%20gene%20expression%20approaches.)
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![[论文审查] Adaptive Perception for Unified Visual Multi-modal Object Tracking](https://moonlight-paper-snapshot.s3.ap-northeast-2.amazonaws.com/arxiv/adaptive-perception-for-unified-visual-multi-modal-object-tracking-0.png){kind=tracking}
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