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Found 11 papers, 4 papers with code
Generalization and Hallucination of Large Vision-Language Models through a Camouflaged Lens
Large Vision-Language Model (LVLM) has seen burgeoning development and increasing attention recently.
Towards Training-free Open-world Segmentation via Image Prompt Foundation Models
The realm of computer vision has witnessed a paradigm shift with the advent of foundational models, mirroring the transformative influence of large language models in the domain of natural language processing.
Zero-Shot Co-salient Object Detection Framework
Despite recent advancements in deep learning models, these models still rely on training with well-annotated CoSOD datasets.
Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection
In this study, we try to ask if SAM can address the COD task and evaluate the performance of SAM on the COD benchmark by employing maximum segmentation evaluation and camouflage location evaluation.
Scene Matters: Model-based Deep Video Compression
Video compression has always been a popular research area, where many traditional and deep video compression methods have been proposed.
Towards Stable Co-saliency Detection and Object Co-segmentation
In this paper, we present a novel model for simultaneous stable co-saliency detection (CoSOD) and object co-segmentation (CoSEG).
Detecting Camouflaged Object in Frequency Domain
We first design a novel frequency enhancement module (FEM) to dig clues of camouflaged objects in the frequency domain.
Highly Efficient Natural Image Matting
With a lightweight basic convolution block, we build a two-stages framework: Segmentation Network (SN) is designed to capture sufficient semantics and classify the pixels into unknown, foreground and background regions; Matting Refine Network (MRN) aims at capturing detailed texture information and regressing accurate alpha values.
Disentangled High Quality Salient Object Detection
As a pixel-wise classification task, LRSCN is designed to capture sufficient semantics at low-resolution to identify the definite salient, background and uncertain image regions.
Ranked #10 on
RGB Salient Object Detection
on DAVIS-S
CoSformer: Detecting Co-Salient Object with Transformers
Co-Salient Object Detection (CoSOD) aims at simulating the human visual system to discover the common and salient objects from a group of relevant images.
CLASS: Cross-Level Attention and Supervision for Salient Objects Detection
First, in order to leverage the different advantages of low-level and high-level features, we propose a novel non-local cross-level attention (CLA), which can capture the long-range feature dependencies to enhance the distinction of complete salient object.
