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Found 31 papers, 8 papers with code
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
We release two versions of MiniMax-M1 models with 40K and 80K thinking budgets respectively, where the 40K model represents an intermediate phase of the 80K training.
MiniMax-01: Scaling Foundation Models with Lightning Attention
This approach enables us to conduct efficient training and inference on models with hundreds of billions of parameters across contexts spanning millions of tokens.
GAPNet: Granularity Attention Network with Anatomy-Prior-Constraint for Carotid Artery Segmentation
Atherosclerosis is a chronic, progressive disease that primarily affects the arterial walls.
DSCA: A Digital Subtraction Angiography Sequence Dataset and Spatio-Temporal Model for Cerebral Artery Segmentation
Additionally, we propose DSANet, a spatio-temporal network for CA segmentation in DSA sequences.
RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness
Traditional feedback learning for hallucination reduction relies on labor-intensive manual labeling or expensive proprietary models.
Ranked #1 on
Visual Question Answering
on AMBER
Grouping Boundary Proposals for Fast Interactive Image Segmentation
These boundary proposals are then incorporated into the proposed image segmentation model, such that the target segmentation contours are made up of a set of selected boundary proposals and the corresponding geodesic paths linking them.
Prompt Switch: Efficient CLIP Adaptation for Text-Video Retrieval
In text-video retrieval, recent works have benefited from the powerful learning capabilities of pre-trained text-image foundation models (e. g., CLIP) by adapting them to the video domain.
Identity-Consistent Aggregation for Video Object Detection
In Video Object Detection (VID), a common practice is to leverage the rich temporal contexts from the video to enhance the object representations in each frame.
Ranked #13 on
Video Object Detection
on ImageNet VID
COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts
To give a more comprehensive robustness assessment, we introduce COCO-O(ut-of-distribution), a test dataset based on COCO with 6 types of natural distribution shifts.
Deep Feature Deblurring Diffusion for Detecting Out-of-Distribution Objects
For this task, the challenge mainly lies in how to only leverage the known in-distribution (ID) data to detect OOD objects accurately without affecting the detection of ID objects, which can be framed as the diffusion problem for deep feature synthesis.
AI enhanced finite element multiscale modelling and structural uncertainty analysis of a functionally graded porous beam
The error range of CNN models leads to an uncertain mechanical performance, which is further evaluated in a structural uncertainty analysis on the FG porous three-layer beam consisting of two thin high-density layers and a thick low-density one, where the imprecise CNN predicted moduli are represented as triangular fuzzy numbers in double parametric form.
Group frame neural network of moving object ghost imaging combined with frame merging algorithm
The nature of multiple samples to extract correlation information limits the applications of ghost imaging of moving objects.
Geodesic Models with Convexity Shape Prior
The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios.
Sketch, Ground, and Refine: Top-Down Dense Video Captioning
The dense video captioning task aims to detect and describe a sequence of events in a video for detailed and coherent storytelling.
An Elastica Geodesic Approach With Convexity Shape Prior
The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios.
Multiple Pairwise Ranking Networks for Personalized Video Summarization
In this paper, we investigate video summarization in the supervised setting.
Geodesic Paths for Image Segmentation with Implicit Region-based Homogeneity Enhancement
Minimal paths are regarded as a powerful and efficient tool for boundary detection and image segmentation due to its global optimality and the well-established numerical solutions such as fast marching method.
A Generalized Asymmetric Dual-front Model for Active Contours and Image Segmentation
The Voronoi diagram-based dual-front active contour models are known as a powerful and efficient way for addressing the image segmentation and domain partitioning problems.
Trajectory Grouping with Curvature Regularization for Tubular Structure Tracking
Tubular structure tracking is a crucial task in the fields of computer vision and medical image analysis.
Data-Free Adversarial Distillation
Knowledge Distillation (KD) has made remarkable progress in the last few years and become a popular paradigm for model compression and knowledge transfer.
A Region-based Randers Geodesic Approach for Image Segmentation
In this paper, we introduce a new variational image segmentation model based on the minimal geodesic path framework and the eikonal PDE, where the region-based appearance term that defines then regional homogeneity features can be taken into account for estimating the associated minimal geodesic paths.
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift
This strategy leads to severe meta shift issues across multiple tasks, meaning the learned prototypes or class descriptors are not stable as each task only involves their own support set.
Self-Supervised Learning For Few-Shot Image Classification
In this paper, we proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream tasks by learning from the data itself.
Ranked #4 on
Few-Shot Image Classification
on Mini-ImageNet - 1-Shot Learning
(using extra training data)
From Active Contours to Minimal Geodesic Paths: New Solutions to Active Contours Problems by Eikonal Equations
In this chapter, we give an overview of part of our previous work based on the minimal path framework and the Eikonal partial differential equation (PDE).
Deep Features Analysis with Attention Networks
Deep neural network models have recently draw lots of attention, as it consistently produce impressive results in many computer vision tasks such as image classification, object detection, etc.
Minimal Paths for Tubular Structure Segmentation with Coherence Penalty and Adaptive Anisotropy
In this paper, we propose a new minimal path model associated with a dynamic Riemannian metric embedded with an appearance feature coherence penalty and an adaptive anisotropy enhancement term.
A New Coherence-Penalized Minimal Path Model with Application to Retinal Vessel Centerline Delineation
In this paper, we propose a new minimal path model for minimally interactive retinal vessel centerline extraction.
Learn to Model Motion from Blurry Footages
It is difficult to recover the motion field from a real-world footage given a mixture of camera shake and other photometric effects.
Global Minimum for a Finsler Elastica Minimal Path Approach
In this paper, we propose a novel curvature-penalized minimal path model via an orientation-lifted Finsler metric and the Euler elastica curve.
A New Finsler Minimal Path Model With Curvature Penalization for Image Segmentation and Closed Contour Detection
This metric is non-Riemannian and asymmetric, defined on an orientation lifted space, incorporating the curvature penalty in the geodesic energy.
