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Found 7 papers, 3 papers with code
System Description on Automatic Simultaneous Translation Workshop
This paper describes our system submitted on the third automatic simultaneous translation workshop at NAACL2022.
VisualPuzzles: Decoupling Multimodal Reasoning Evaluation from Domain Knowledge
Current multimodal benchmarks often conflate reasoning with domain-specific knowledge, making it difficult to isolate and evaluate general reasoning abilities in non-expert settings.
Uni-Sign: Toward Unified Sign Language Understanding at Scale
Sign language pre-training has gained increasing attention for its ability to enhance performance across various sign language understanding (SLU) tasks.
Ranked #1 on
Sign Language Recognition
on WLASL100
(using extra training data)
Computational Efficiency
Gloss-free Sign Language Translation
+3
Scaling up Multimodal Pre-training for Sign Language Understanding
To facilitate communication between the deaf-mute and hearing people, a series of sign language understanding (SLU) tasks have been studied in recent years, including isolated/continuous sign language recognition (ISLR/CSLR), gloss-free sign language translation (GF-SLT) and sign language retrieval (SL-RT).
SEDS: Semantically Enhanced Dual-Stream Encoder for Sign Language Retrieval
Furthermore, existing RGB-based sign retrieval works suffer from the huge memory cost of dense visual data embedding in end-to-end training, and adopt offline RGB encoder instead, leading to suboptimal feature representation.
Complete Instances Mining for Weakly Supervised Instance Segmentation
To address this problem, we propose a novel approach for WSIS that focuses on the online refinement of complete instances through the use of MaskIoU heads to predict the integrity scores of proposals and a Complete Instances Mining (CIM) strategy to explicitly model the redundant segmentation problem and generate refined pseudo labels.
Ranked #2 on
Weakly-supervised instance segmentation
on PASCAL VOC 2012 val
(mAP@0.25 metric)
MDS-Net: A Multi-scale Depth Stratification Based Monocular 3D Object Detection Algorithm
Monocular 3D object detection is very challenging in autonomous driving due to the lack of depth information.
