Search Results for author:
Found 17 papers, 2 papers with code
Distilling Multi-modal Large Language Models for Autonomous Driving
Training with DiMA results in a 37% reduction in the L2 trajectory error and an 80% reduction in the collision rate of the vision-based planner, as well as a 44% trajectory error reduction in longtail scenarios.
DDIL: Improved Diffusion Distillation With Imitation Learning
Efforts such as progressive distillation or consistency distillation have shown promise by reducing the number of passes at the expense of quality of the generated samples.
Rapid Switching and Multi-Adapter Fusion via Sparse High Rank Adapters
In this paper, we propose Sparse High Rank Adapters (SHiRA) that directly finetune 1-2% of the base model weights while leaving others unchanged, thus, resulting in a highly sparse adapter.
Sparse High Rank Adapters
However, from a mobile deployment standpoint, we can either avoid inference overhead in the fused mode but lose the ability to switch adapters rapidly, or suffer significant (up to 30% higher) inference latency while enabling rapid switching in the unfused mode.
FouRA: Fourier Low Rank Adaptation
While Low-Rank Adaptation (LoRA) has proven beneficial for efficiently fine-tuning large models, LoRA fine-tuned text-to-image diffusion models lack diversity in the generated images, as the model tends to copy data from the observed training samples.
SciFlow: Empowering Lightweight Optical Flow Models with Self-Cleaning Iterations
Optical flow estimation is crucial to a variety of vision tasks.
OCAI: Improving Optical Flow Estimation by Occlusion and Consistency Aware Interpolation
We propose OCAI, a method that supports robust frame interpolation by generating intermediate video frames alongside optical flows in between.
FutureDepth: Learning to Predict the Future Improves Video Depth Estimation
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training.
Ranked #4 on
Monocular Depth Estimation
on KITTI Eigen split
MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation
We propose MAMo, a novel memory and attention frame-work for monocular video depth estimation.
Ranked #15 on
Monocular Depth Estimation
on KITTI Eigen split
DIFT: Dynamic Iterative Field Transforms for Memory Efficient Optical Flow
Also, we present a novel dynamic coarse-to-fine cost volume processing during various stages of refinement to avoid multiple levels of cost volumes.
DistractFlow: Improving Optical Flow Estimation via Realistic Distractions and Pseudo-Labeling
We propose a novel data augmentation approach, DistractFlow, for training optical flow estimation models by introducing realistic distractions to the input frames.
DejaVu: Conditional Regenerative Learning to Enhance Dense Prediction
Next, we use a conditional regenerator, which takes the redacted image and the dense predictions as inputs, and reconstructs the original image by filling in the missing structural information.
TransAdapt: A Transformative Framework for Online Test Time Adaptive Semantic Segmentation
Test-time adaptive (TTA) semantic segmentation adapts a source pre-trained image semantic segmentation model to unlabeled batches of target domain test images, different from real-world, where samples arrive one-by-one in an online fashion.
Oracle Analysis of Representations for Deep Open Set Detection
The second is to introduce Oracle representation learning, which produces a representation that is guaranteed to be sufficient for accurate anomaly detection.
Panoptic, Instance and Semantic Relations: A Relational Context Encoder to Enhance Panoptic Segmentation
A Panoptic Relational Attention (PRA) module is then applied to the encodings and the global feature map from the backbone.
Open Category Detection with PAC Guarantees
Further, while there are algorithms for open category detection, there are few empirical results that directly report alien detection rates.
