75 research outputs found
SDDNet: Style-guided Dual-layer Disentanglement Network for Shadow Detection
Despite significant progress in shadow detection, current methods still
struggle with the adverse impact of background color, which may lead to errors
when shadows are present on complex backgrounds. Drawing inspiration from the
human visual system, we treat the input shadow image as a composition of a
background layer and a shadow layer, and design a Style-guided Dual-layer
Disentanglement Network (SDDNet) to model these layers independently. To
achieve this, we devise a Feature Separation and Recombination (FSR) module
that decomposes multi-level features into shadow-related and background-related
components by offering specialized supervision for each component, while
preserving information integrity and avoiding redundancy through the
reconstruction constraint. Moreover, we propose a Shadow Style Filter (SSF)
module to guide the feature disentanglement by focusing on style
differentiation and uniformization. With these two modules and our overall
pipeline, our model effectively minimizes the detrimental effects of background
color, yielding superior performance on three public datasets with a real-time
inference speed of 32 FPS.Comment: Accepted by ACM MM 202
Query-guided Prototype Evolution Network for Few-Shot Segmentation
Previous Few-Shot Segmentation (FSS) approaches exclusively utilize support
features for prototype generation, neglecting the specific requirements of the
query. To address this, we present the Query-guided Prototype Evolution Network
(QPENet), a new method that integrates query features into the generation
process of foreground and background prototypes, thereby yielding customized
prototypes attuned to specific queries. The evolution of the foreground
prototype is accomplished through a \textit{support-query-support} iterative
process involving two new modules: Pseudo-prototype Generation (PPG) and Dual
Prototype Evolution (DPE). The PPG module employs support features to create an
initial prototype for the preliminary segmentation of the query image,
resulting in a pseudo-prototype reflecting the unique needs of the current
query. Subsequently, the DPE module performs reverse segmentation on support
images using this pseudo-prototype, leading to the generation of evolved
prototypes, which can be considered as custom solutions. As for the background
prototype, the evolution begins with a global background prototype that
represents the generalized features of all training images. We also design a
Global Background Cleansing (GBC) module to eliminate potential adverse
components mirroring the characteristics of the current foreground class.
Experimental results on the PASCAL- and COCO- datasets attest to the
substantial enhancements achieved by QPENet over prevailing state-of-the-art
techniques, underscoring the validity of our ideas.Comment: Accepted by IEEE TMM 202
Duplication and Remolding of tRNA Genes in the Mitochondrial Genome of \u3cem\u3eReduvius tenebrosus\u3c/em\u3e (Hemiptera: Reduviidae)
Most assassin bugs are predators that act as important natural enemies of insect pests. Mitochondrial (mt) genomes of these insects are double-strand circular DNAs that encode 37 genes. In the present study, we explore the duplication and rearrangement of tRNA genes in the mt genome of Reduvius tenebrosus, the first mt genome from the subfamily Reduviinae. The gene order rearranges from CR (control region)-trnI-trnQ-trnM-ND2 to CR-trnQ-trnI2-trnI1-trnM-ND2. We identified 23 tRNA genes, including 22 tRNAs commonly found in insects and an additional trnI (trnI2), which has high sequence similarity to trnM. We found several pseudo genes, such as pseudo-trnI, pseudo-CR, and pseudo-ND2, in the hotspot region of gene rearrangement (between the control region and ND2). These features provided evidence that this novel gene order could be explained by the tandem duplication/random loss (TDRL) model. The tRNA duplication/anticodon mutation mechanism further explains the presence of trnI2, which is remolded from a duplicated trnM in the TDRL process (through an anticodon mutation of CAT to GAT). Our study also raises new questions as to whether the two events proceed simultaneously and if the remolded tRNA gene is fully functional. Significantly, the duplicated tRNA gene in the mitochondrial genome has evolved independently at least two times within assassin bugs
DuBox: No-Prior Box Objection Detection via Residual Dual Scale Detectors
Traditional neural objection detection methods use multi-scale features that
allow multiple detectors to perform detecting tasks independently and in
parallel. At the same time, with the handling of the prior box, the algorithm's
ability to deal with scale invariance is enhanced. However, too many prior
boxes and independent detectors will increase the computational redundancy of
the detection algorithm. In this study, we introduce Dubox, a new one-stage
approach that detects the objects without prior box. Working with multi-scale
features, the designed dual scale residual unit makes dual scale detectors no
longer run independently. The second scale detector learns the residual of the
first. Dubox has enhanced the capacity of heuristic-guided that can further
enable the first scale detector to maximize the detection of small targets and
the second to detect objects that cannot be identified by the first one.
Besides, for each scale detector, with the new classification-regression
progressive strapped loss makes our process not based on prior boxes.
Integrating these strategies, our detection algorithm has achieved excellent
performance in terms of speed and accuracy. Extensive experiments on the VOC,
COCO object detection benchmark have confirmed the effectiveness of this
algorithm
SSR-Encoder: Encoding Selective Subject Representation for Subject-Driven Generation
Recent advancements in subject-driven image generation have led to zero-shot
generation, yet precise selection and focus on crucial subject representations
remain challenging. Addressing this, we introduce the SSR-Encoder, a novel
architecture designed for selectively capturing any subject from single or
multiple reference images. It responds to various query modalities including
text and masks, without necessitating test-time fine-tuning. The SSR-Encoder
combines a Token-to-Patch Aligner that aligns query inputs with image patches
and a Detail-Preserving Subject Encoder for extracting and preserving fine
features of the subjects, thereby generating subject embeddings. These
embeddings, used in conjunction with original text embeddings, condition the
generation process. Characterized by its model generalizability and efficiency,
the SSR-Encoder adapts to a range of custom models and control modules.
Enhanced by the Embedding Consistency Regularization Loss for improved
training, our extensive experiments demonstrate its effectiveness in versatile
and high-quality image generation, indicating its broad applicability. Project
page: https://ssr-encoder.github.i
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