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Found 7 papers, 4 papers with code
Accelerating Training with Neuron Interaction and Nowcasting Networks
By accurately modeling neuron connectivity, we allow NiNo to accelerate Adam training by up to 50\% in vision and language tasks.
Can We Learn Communication-Efficient Optimizers?
Although many variants of these approaches have been proposed, they can sometimes lag behind state-of-the-art adaptive optimizers for deep learning.
Towards Scaling Difference Target Propagation by Learning Backprop Targets
As such, it is important to explore learning algorithms that come with strong theoretical guarantees and can match the performance of backpropagation (BP) on complex tasks.
SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation
Natural language instructions for visual navigation often use scene descriptions (e. g., "bedroom") and object references (e. g., "green chairs") to provide a breadcrumb trail to a goal location.
Contrast and Classify: Training Robust VQA Models
Recent Visual Question Answering (VQA) models have shown impressive performance on the VQA benchmark but remain sensitive to small linguistic variations in input questions.
Exploring 3 R's of Long-term Tracking: Re-detection, Recovery and Reliability
Recent works have proposed several long term tracking benchmarks and highlight the importance of moving towards long-duration tracking to bridge the gap with application requirements.
Long-Term Visual Object Tracking Benchmark
We propose a new long video dataset (called Track Long and Prosper - TLP) and benchmark for single object tracking.
