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Found 9 papers, 6 papers with code
OCID-Ref: A 3D Robotic Dataset with Embodied Language for Clutter Scene Grounding
To effectively apply robots in working environments and assist humans, it is essential to develop and evaluate how visual grounding (VG) can affect machine performance on occluded objects.
Situation and Behavior Understanding by Trope Detection on Films
Experimental result demonstrates that modern models including BERT contextual embedding, movie tag prediction systems, and relational networks, perform at most 37% of human performance (23. 97/64. 87) in terms of F1 score.
End-to-End Video Question-Answer Generation with Generator-Pretester Network
Furthermore, using our generated QA pairs only on the Video QA task, we can surpass some supervised baselines.
xCos: An Explainable Cosine Metric for Face Verification Task
We study the XAI (explainable AI) on the face recognition task, particularly the face verification here.
Explainable Artificial Intelligence (XAI)
Face Recognition
+1
Investigating the Decoders of Maximum Likelihood Sequence Models: A Look-ahead Approach
We evaluate our look-ahead module on three datasets of varying difficulties: IM2LATEX-100k OCR image to LaTeX, WMT16 multimodal machine translation, and WMT14 machine translation.
Multimodal Machine Translation
Optical Character Recognition (OCR)
+2
Video Question Generation via Cross-Modal Self-Attention Networks Learning
We introduce a novel task, Video Question Generation (Video QG).
Scene Graph Parsing as Dependency Parsing
The scene graphs generated by our learned neural dependency parser achieve an F-score similarity of 49. 67% to ground truth graphs on our evaluation set, surpassing best previous approaches by 5%.
Adversarial Attacks Beyond the Image Space
Though image-space adversaries can be interpreted as per-pixel albedo change, we verify that they cannot be well explained along these physically meaningful dimensions, which often have a non-local effect.
Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks
Motivated by the observation that named entities are highly related to linguistic constituents, we propose a constituent-based BRNN-CNN for named entity recognition.
