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Found 20 papers, 9 papers with code
PAD: Phase-Amplitude Decoupling Fusion for Multi-Modal Land Cover Classification
The fusion of Synthetic Aperture Radar (SAR) and RGB imagery for land cover classification remains challenging due to modality heterogeneity and the underutilization of spectral complementarity.
QIRL: Boosting Visual Question Answering via Optimized Question-Image Relation Learning
Furthermore, to validate our concept of reducing output errors through filtering unrelated question-image inputs, we propose a specialized metric to evaluate the performance of the ISI module.
SU-YOLO: Spiking Neural Network for Efficient Underwater Object Detection
Underwater object detection is critical for oceanic research and industrial safety inspections.
SpikeDerain: Unveiling Clear Videos from Rainy Sequences Using Color Spike Streams
Restoring clear frames from rainy videos presents a significant challenge due to the rapid motion of rain streaks.
Anomize: Better Open Vocabulary Video Anomaly Detection
Open Vocabulary Video Anomaly Detection (OVVAD) seeks to detect and classify both base and novel anomalies.
STAA-SNN: Spatial-Temporal Attention Aggregator for Spiking Neural Networks
The framework demonstrates exceptional performance across diverse datasets and exhibits strong generalization capabilities.
LLM Knows Geometry Better than Algebra: Numerical Understanding of LLM-Based Agents in A Trading Arena
Recent advancements in large language models (LLMs) have significantly improved performance in natural language processing tasks.
FocalCount: Towards Class-Count Imbalance in Class-Agnostic Counting
In class-agnostic object counting, the goal is to estimate the total number of object instances in an image without distinguishing between specific categories.
See What You Seek: Semantic Contextual Integration for Cloth-Changing Person Re-Identification
In this paper, we propose a novel prompt learning framework, Semantic Contextual Integration (SCI), for CC-ReID, which leverages the visual-text representation capabilities of CLIP to minimize the impact of clothing changes and enhance ID-relevant features.
OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action Recognition under Occlusions
We anticipate that the challenges posed by OccludeNet will stimulate further exploration of causal relations in occlusion scenarios and encourage a reevaluation of class correlations, ultimately promoting sustainable performance improvements.
Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks
In this paper, we propose Hybrid Step-wise Distillation (HSD) method, tailored for neuromorphic datasets, to mitigate the notable decline in performance at lower time steps.
Methodological Explainability Evaluation of an Interpretable Deep Learning Model for Post-Hepatectomy Liver Failure Prediction Incorporating Counterfactual Explanations and Layerwise Relevance Propagation: A Prospective In Silico Trial
This framework includes qualitative and quantitative assessments of explanations against recognized biomarkers, usability evaluations, and an in silico clinical trial.
DenseTrack: Drone-based Crowd Tracking via Density-aware Motion-appearance Synergy
Drone-based crowd tracking faces difficulties in accurately identifying and monitoring objects from an aerial perspective, largely due to their small size and close proximity to each other, which complicates both localization and tracking.
Zero-shot Object Counting with Good Exemplars
Zero-shot object counting (ZOC) aims to enumerate objects in images using only the names of object classes during testing, without the need for manual annotations.
DAOT: Domain-Agnostically Aligned Optimal Transport for Domain-Adaptive Crowd Counting
Domain adaptation is commonly employed in crowd counting to bridge the domain gaps between different datasets.
Good Is Bad: Causality Inspired Cloth-Debiasing for Cloth-Changing Person Re-Identification
Entangled representation of clothing and identity (ID)-intrinsic clues are potentially concomitant in conventional person Re-IDentification (ReID).
Refined Semantic Enhancement towards Frequency Diffusion for Video Captioning
In this paper, we introduce a novel Refined Semantic enhancement method towards Frequency Diffusion (RSFD), a captioning model that constantly perceives the linguistic representation of the infrequent tokens.
Visual-aware Attention Dual-stream Decoder for Video Captioning
Video captioning is a challenging task that captures different visual parts and describes them in sentences, for it requires visual and linguistic coherence.
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach
As a result, a meta-learner cannot be trained well in a high-dimensional parameter space to generalize to new tasks.
Ranked #18 on
Few-Shot Image Classification
on FC100 5-way (5-shot)
Image-to-Video Person Re-Identification by Reusing Cross-modal Embeddings
In this paper, we propose an end-to-end neural network framework for image-to-video person reidentification by leveraging cross-modal embeddings learned from extra information. Concretely speaking, cross-modal embeddings from image captioning and video captioning models are reused to help learned features be projected into a coordinated space, where similarity can be directly computed.
