2.4k citations · 8k across the 63 of their papers we have counts for
67 papers
Edge-aware Feature Aggregation Network for Polyp Segmentation
Tao Zhou, Yizhe Zhang, Geng Chen +3
Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer (CRC) in clinical practice. However, due to scale variation and blurry polyp boundar…
How Good is Google Bard's Visual Understanding? An Empirical Study on Open Challenges
Haotong Qin, Ge-Peng Ji, Salman Khan +3
Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational AI. Notably, Bard has recently been updated to handle visual inputs alongsid…
OnUVS: Online Feature Decoupling Framework for High-Fidelity Ultrasound Video Synthesis
Han Zhou, Dong Ni, Ao Chang +10
Ultrasound (US) imaging is indispensable in clinical practice. To diagnose certain diseases, sonographers must observe corresponding dynamic anatomic structures to gather comprehen…
CalibNet: Dual-branch Cross-modal Calibration for RGB-D Salient Instance Segmentation
Jialun Pei, Tao Jiang, He Tang +4
We propose a novel approach for RGB-D salient instance segmentation using a dual-branch cross-modal feature calibration architecture called CalibNet. Our method simultaneously cali…
Instructive Feature Enhancement for Dichotomous Medical Image Segmentation
Lian Liu, Han Zhou, Jiongquan Chen +5
Deep neural networks have been widely applied in dichotomous medical image segmentation (DMIS) of many anatomical structures in several modalities, achieving promising performance.…
Referring Camouflaged Object Detection
Xuying Zhang, Bowen Yin, Zheng Lin +3
We consider the problem of referring camouflaged object detection (Ref-COD), a new task that aims to segment specified camouflaged objects based on a small set of referring images…
Mindstorms in Natural Language-Based Societies of Mind
Mingchen Zhuge, Haozhe Liu, Francesco Faccio +23
Both Minsky's "society of mind" and Schmidhuber's "learning to think" inspire diverse societies of large multimodal neural networks (NNs) that solve problems by interviewing each o…
WinDB: HMD-free and Distortion-free Panoptic Video Fixation Learning
Guotao Wang, Chenglizhao Chen, Aimin Hao +2
To date, the widely adopted way to perform fixation collection in panoptic video is based on a head-mounted display (HMD), where users' fixations are collected while wearing an HMD…
CamDiff: Camouflage Image Augmentation via Diffusion Model
Xue-Jing Luo, Shuo Wang, Zongwei Wu +4
The burgeoning field of camouflaged object detection (COD) seeks to identify objects that blend into their surroundings. Despite the impressive performance of recent models, we hav…
RGB-D Indiscernible Object Counting in Underwater Scenes
Guolei Sun, Xiaogang Cheng, Zhaochong An +5
Recently, indiscernible/camouflaged scene understanding has attracted lots of research attention in the vision community. We further advance the frontier of this field by systemati…
Advances in Deep Concealed Scene Understanding
Deng-Ping Fan, Ge-Peng Ji, Peng Xu +3
Concealed scene understanding (CSU) is a hot computer vision topic aiming to perceive objects exhibiting camouflage. The current boom in terms of techniques and applications warran…
SAM Struggles in Concealed Scenes -- Empirical Study on Segment Anything
Ge-Peng Ji, Deng-Ping Fan, Peng Xu +3
Segmenting anything is a ground-breaking step toward artificial general intelligence, and the Segment Anything Model (SAM) greatly fosters the foundation models for computer vision…
CamoFormer: Masked Separable Attention for Camouflaged Object Detection
Bowen Yin, Xuying Zhang, Qibin Hou +3
How to identify and segment camouflaged objects from the background is challenging. Inspired by the multi-head self-attention in Transformers, we present a simple masked separable…
Source-free Depth for Object Pop-out
Zongwei Wu, Danda Pani Paudel, Deng-Ping Fan +5
Depth cues are known to be useful for visual perception. However, direct measurement of depth is often impracticable. Fortunately, though, modern learning-based methods offer promi…
Masked Vision-Language Transformer in Fashion
Ge-Peng Ji, Mingcheng Zhuge, Dehong Gao +3
We present a masked vision-language transformer (MVLT) for fashion-specific multi-modal representation. Technically, we simply utilize vision transformer architecture for replacing…
OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers
Jialun Pei, Tianyang Cheng, Deng-Ping Fan +3
We present OSFormer, the first one-stage transformer framework for camouflaged instance segmentation (CIS). OSFormer is based on two key designs. First, we design a location-sensin…
GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector
Peng Zheng, Huazhu Fu, Deng-Ping Fan +5
In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in na…
Deep Gradient Learning for Efficient Camouflaged Object Detection
Ge-Peng Ji, Deng-Ping Fan, Yu-Cheng Chou +3
This paper introduces DGNet, a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branc…
Towards Deeper Understanding of Camouflaged Object Detection
Yunqiu Lv, Jing Zhang, Yuchao Dai +3
Preys in the wild evolve to be camouflaged to avoid being recognized by predators. In this way, camouflage acts as a key defence mechanism across species that is critical to surviv…
Implicit Motion Handling for Video Camouflaged Object Detection
Xuelian Cheng, Huan Xiong, Deng-Ping Fan +4
We propose a new video camouflaged object detection (VCOD) framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from…
Video Polyp Segmentation: A Deep Learning Perspective
Ge-Peng Ji, Guobao Xiao, Yu-Cheng Chou +4
We present the first comprehensive video polyp segmentation (VPS) study in the deep learning era. Over the years, developments in VPS are not moving forward with ease due to the la…
Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis
Kai Zhang, Yawei Li, Jingyun Liang +6
While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, su…
High-resolution Iterative Feedback Network for Camouflaged Object Detection
Xiaobin Hu, Shuo Wang, Xuebin Qin +5
Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the…
Highly Accurate Dichotomous Image Segmentation
Xuebin Qin, Hang Dai, Xiaobin Hu +3
We present a systematic study on a new task called dichotomous image segmentation (DIS) , which aims to segment highly accurate objects from natural images. To this end, we collect…