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Found 19 papers, 8 papers with code
Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion
The proposed method does not require any training or language dependency to extract quality segmentation for any images.
Ranked #1 on
Semantic Segmentation
on COCO-Stuff-27
HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual Federated Learning
In this paper, we attempt to tackle forgetting and heterogeneity while minimizing overhead costs and without requiring access to any stored data.
Trainable Projected Gradient Method for Robust Fine-tuning
To solve this problem, we propose Trainable Projected Gradient Method (TPGM) to automatically learn the constraint imposed for each layer for a fine-grained fine-tuning regularization.
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks
Specifically, Polyhistor achieves competitive accuracy compared to the state-of-the-art while only using ~10% of their trainable parameters.
FedFOR: Stateless Heterogeneous Federated Learning with First-Order Regularization
For the more typical applications of FL where the number of clients is large (e. g., edge-device and mobile applications), these methods cannot be applied, motivating the need for a stateless approach to heterogeneous FL which can be used for any number of clients.
Open-Set Semi-Supervised Object Detection
To address this problem, we consider online and offline OOD detection modules, which are integrated with SSOD methods.
A Closer Look at Rehearsal-Free Continual Learning
Next, we explore how to leverage knowledge from a pre-trained model in rehearsal-free continual learning and find that vanilla L2 parameter regularization outperforms EWC parameter regularization and feature distillation.
Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data
We are the first to propose a method that works well across both OOD detection and calibration and under different types of shifts.
A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition
It is well known that vision classification models suffer from poor calibration in the face of data distribution shifts.
Exploring Covariate and Concept Shift for Detection and Confidence Calibration of Out-of-Distribution Data
To this end, we theoretically derive two score functions for OOD detection, the covariate shift score and concept shift score, based on the decomposition of KL-divergence for both scores, and propose a geometrically-inspired method (Geometric ODIN) to improve OOD detection under both shifts with only in-distribution data.
Enhancing Multi-Robot Perception via Learned Data Association
In this paper, we address the multi-robot collaborative perception problem, specifically in the context of multi-view infilling for distributed semantic segmentation.
Overcoming Obstructions via Bandwidth-Limited Multi-Agent Spatial Handshaking
In this paper, we address bandwidth-limited and obstruction-prone collaborative perception, specifically in the context of multi-agent semantic segmentation.
Striking the Right Balance: Recall Loss for Semantic Segmentation
There are two major drawbacks to these methods: 1) constantly up-weighting minority classes can introduce excessive false positives in semantic segmentation; 2) a minority class is not necessarily a hard class.
Recall Loss for Imbalanced Image Classification and Semantic Segmentation
Many works have proposed to weigh the standard cross entropy loss function with pre-computed weights based on class statistics such as the number of samples and class margins.
Posterior Re-calibration for Imbalanced Datasets
Neural Networks can perform poorly when the training label distribution is heavily imbalanced, as well as when the testing data differs from the training distribution.
Ranked #31 on
Long-tail Learning
on CIFAR-100-LT (ρ=10)
Image Captioning with Compositional Neural Module Networks
In image captioning where fluency is an important factor in evaluation, e. g., $n$-gram metrics, sequential models are commonly used; however, sequential models generally result in overgeneralized expressions that lack the details that may be present in an input image.
When2com: Multi-Agent Perception via Communication Graph Grouping
While significant advances have been made for single-agent perception, many applications require multiple sensing agents and cross-agent communication due to benefits such as coverage and robustness.
Who2com: Collaborative Perception via Learnable Handshake Communication
In this paper, we propose the problem of collaborative perception, where robots can combine their local observations with those of neighboring agents in a learnable way to improve accuracy on a perception task.
UNO: Uncertainty-aware Noisy-Or Multimodal Fusion for Unanticipated Input Degradation
Specifically, we analyze a number of uncertainty measures, each of which captures a different aspect of uncertainty, and we propose a novel way to fuse degraded inputs by scaling modality-specific output softmax probabilities.
