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title
Bottom Up Top Down Detection Transformers for Language Grounding in Images and Point Clouds
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Bottom Up Top Down Detection Transformers for Language Grounding in Images and Point Clouds
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2026-03-01 16:02:22
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Bottom Up and Top Down Detection Transformers for Language Grounding in Images and Point Clouds Bottom Up Top Down Detection Transformers for Language Grounding in Images and Point Clouds Ayush Jain * 1 , Nikolaos Gkanatsios * 1 , Ishita Mediratta 2 , Katerina Fragkiadaki 1 1 Carnegie Mellon University, 2 Meta AI ECCV 2022 * Equal Contribution (God may not play dice, we do) description Paper code Code Abstract Most models tasked to ground referential utterances in 2D and 3D scenes learn to select the referred object from a pool of object proposals provided by a pre-trained detector. This is limiting because an utterance may refer to visual entities at various levels of granularity, such as the chair, the leg of the chair, or the tip of the front leg of the chair, which may be missed by the detector. We propose a language grounding model that attends on the referential utterance and on the object proposal pool computed ...
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