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Found 13 papers, 8 papers with code
Adapting Segment Anything Model for Unseen Object Instance Segmentation
Unseen Object Instance Segmentation (UOIS) is crucial for autonomous robots operating in unstructured environments.
UC-NeRF: Uncertainty-aware Conditional Neural Radiance Fields from Endoscopic Sparse Views
In neural rendering, we design a base-adaptive NeRF network to exploit the uncertainty estimation for explicitly handling the photometric inconsistencies.
SR-Mamba: Effective Surgical Phase Recognition with State Space Model
Inspired by the recent success of Mamba, a state space model with linear scalability in sequence length, this paper presents SR-Mamba, a novel attention-free model specifically tailored to meet the challenges of surgical phase recognition.
Free-SurGS: SfM-Free 3D Gaussian Splatting for Surgical Scene Reconstruction
To tackle this problem, in this paper, we propose the first SfM-free 3DGS-based method for surgical scene reconstruction by jointly optimizing the camera poses and scene representation.
Ada-Tracker: Soft Tissue Tracking via Inter-Frame and Adaptive-Template Matching
Soft tissue tracking is crucial for computer-assisted interventions.
Advancing Vision Transformers with Group-Mix Attention
The attention map is computed based on the mixtures of tokens and group proxies and used to re-combine the tokens and groups in Value.
Speed Co-Augmentation for Unsupervised Audio-Visual Pre-training
This work aims to improve unsupervised audio-visual pre-training.
Soft Neighbors are Positive Supporters in Contrastive Visual Representation Learning
We evaluate our soft neighbor contrastive learning method (SNCLR) on standard visual recognition benchmarks, including image classification, object detection, and instance segmentation.
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
To address this challenge, we propose an effective adaptation approach for Transformer, namely AdaptFormer, which can adapt the pre-trained ViTs into many different image and video tasks efficiently.
Learning To Identify Correct 2D-2D Line Correspondences on Sphere
In contrast, we propose the first approach suitable for both structured and unstructured scenes.
Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics
Specifically, given an unlabeled video clip, we compute a series of spatio-temporal statistical summaries, such as the spatial location and dominant direction of the largest motion, the spatial location and dominant color of the largest color diversity along the temporal axis, etc.
Self-supervised Video Representation Learning by Pace Prediction
This paper addresses the problem of self-supervised video representation learning from a new perspective -- by video pace prediction.
Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics
We conduct extensive experiments with C3D to validate the effectiveness of our proposed approach.
Ranked #47 on
Self-Supervised Action Recognition
on HMDB51
