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Found 10 papers, 7 papers with code
Language Model Evolution: An Iterated Learning Perspective
With the widespread adoption of Large Language Models (LLMs), the prevalence of iterative interactions among these models is anticipated to increase.
Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement
The ability to derive underlying principles from a handful of observations and then generalize to novel situations -- known as inductive reasoning -- is central to human intelligence.
Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks
The impressive performance of recent language models across a wide range of tasks suggests that they possess a degree of abstract reasoning skills.
QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking
In this paper, we present Quasi-Dense Similarity Learning, which densely samples hundreds of object regions on a pair of images for contrastive learning.
Ranked #4 on
Multiple Object Tracking
on BDD100K test
Generate-and-Retrieve: use your predictions to improve retrieval for semantic parsing
Then, it retrieves exemplars with outputs similar to the preliminary prediction which are used to generate a final prediction.
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing
Meanwhile, recent work has shown considerable improvements on many NLP tasks from model scaling.
Improving Compositional Generalization with Latent Structure and Data Augmentation
Generic unstructured neural networks have been shown to struggle on out-of-distribution compositional generalization.
Visually Grounded Concept Composition
We investigate ways to compose complex concepts in texts from primitive ones while grounding them in images.
Systematic Generalization on gSCAN: What is Nearly Solved and What is Next?
We analyze the grounded SCAN (gSCAN) benchmark, which was recently proposed to study systematic generalization for grounded language understanding.
Quasi-Dense Similarity Learning for Multiple Object Tracking
Compared to methods with similar detectors, it boosts almost 10 points of MOTA and significantly decreases the number of ID switches on BDD100K and Waymo datasets.
Ranked #1 on
One-Shot Object Detection
on PASCAL VOC 2012 val
