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Found 18 papers, 2 papers with code
MedSeg-R: Reasoning Segmentation in Medical Images with Multimodal Large Language Models
In this paper, we introduce medical image reasoning segmentation, a novel task that aims to generate segmentation masks based on complex and implicit medical instructions.
Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding
With this goal, we present FarSight, a versatile plug-and-play decoding strategy to reduce attention interference from outlier tokens merely by optimizing the causal mask.
ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model
Unsupervised anomaly detection in hyperspectral images (HSI), aiming to detect unknown targets from backgrounds, is challenging for earth surface monitoring.
PG-SAM: Prior-Guided SAM with Medical for Multi-organ Segmentation
To address this, we propose Prior-Guided SAM (PG-SAM), which employs a fine-grained modality prior aligner to leverage specialized medical knowledge for better modality alignment.
ScalingNoise: Scaling Inference-Time Search for Generating Infinite Videos
Specifically, we perform one-step denoising to convert initial noises into a clip and subsequently evaluate its long-term value, leveraging a reward model anchored by previously generated content.
Domain Generalization in CLIP via Learning with Diverse Text Prompts
The text prompts guide DG model learning in three aspects: feature suppression, which uses these prompts to identify domain-sensitive features and suppress them; feature consistency, which ensures the model's features are robust to domain variations imitated by the diverse prompts; and feature diversification, which diversifies features based on the prompts to mitigate bias.
Understanding Fine-tuning CLIP for Open-vocabulary Semantic Segmentation in Hyperbolic Space
In this work, we explain this phenomenon from the perspective of hierarchical alignment, since during fine-tuning, the hierarchy level of image embeddings shifts from image-level to pixel-level.
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
+1
Star with Bilinear Mapping
Contextual modeling is crucial for robust visual representation learning, especially in computer vision.
Retaining Knowledge and Enhancing Long-Text Representations in CLIP through Dual-Teacher Distillation
Contrastive language-image pretraining models such as CLIP have demonstrated remarkable performance in various text-image alignment tasks.
OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining
Surgical practice involves complex visual interpretation, procedural skills, and advanced medical knowledge, making surgical vision-language pretraining (VLP) particularly challenging due to this complexity and the limited availability of annotated data.
Parameter-efficient Fine-tuning in Hyperspherical Space for Open-vocabulary Semantic Segmentation
Since the PEFT strategy is conducted symmetrically to the two CLIP modalities, the misalignment between them is mitigated.
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
+2
S$^2$Mamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification
In S$^2$Mamba, two selective structured state space models through different dimensions are designed for feature extraction, one for spatial, and the other for spectral, along with a spatial-spectral mixture gate for optimal fusion.
Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model
Parameter-efficient fine-tuning (PEFT) is an effective methodology to unleash the potential of large foundation models in novel scenarios with limited training data.
SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstruction
Most existing fine-tuning methods attempt to bridge the gaps among different scenarios by introducing a set of new parameters to modify SAM's original parameter space.
USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentation
Seed area generation is usually the starting point of weakly supervised semantic segmentation (WSSS).
A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction
Next, we summarize the existing label-efficient image segmentation methods from a unified perspective that discusses an important question: how to bridge the gap between weak supervision and dense prediction -- the current methods are mostly based on heuristic priors, such as cross-pixel similarity, cross-label constraint, cross-view consistency, and cross-image relation.
Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information
In the exploiting stage, we utilize the extracted NDI to construct a novel negative contrastive learning mechanism and a negative guided instance selection strategy for dealing with the issues of part domination and missing instances, respectively.
Adaptive Affinity Loss and Erroneous Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation
Semantic segmentation has been continuously investigated in the last ten years, and majority of the established technologies are based on supervised models.
