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Found 5 papers, 2 papers with code
Align before Adapt: Leveraging Entity-to-Region Alignments for Generalizable Video Action Recognition
With the aligned entities, we feed their text embeddings to a transformer-based video adapter as the queries, which can help extract the semantics of the most important entities from a video to a vector.
PBFormer: Capturing Complex Scene Text Shape with Polynomial Band Transformer
We present PBFormer, an efficient yet powerful scene text detector that unifies the transformer with a novel text shape representation Polynomial Band (PB).
Video Action Recognition with Attentive Semantic Units
Supervised by the semantics of action labels, recent works adapt the visual branch of VLMs to learn video representations.
Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints
In this task, the correct camera pose is the key to generating accurate lanes, which can transform an image from perspective-view to the top-view.
Ranked #6 on
3D Lane Detection
on Apollo Synthetic 3D Lane
End-to-end Lane Shape Prediction with Transformers
To tackle these issues, we propose an end-to-end method that directly outputs parameters of a lane shape model, using a network built with a transformer to learn richer structures and context.
Ranked #20 on
Lane Detection
on TuSimple
