object detection
Object detection is a computer vision task that involves identifying and localizing objects within an image or video. It combines the tasks of classification (recognizing objects) and localization (drawing bounding boxes around them).
- Availability-aware Sensor Fusion via Unified Canonical Space
- BurstDeflicker: A Benchmark Dataset for Flicker Removal in Dynamic Scenes
- CQ-DINO: Mitigating Gradient Dilution via Category Queries for Vast Vocabulary Object Detection
- Correlated Low-Rank Adaptation for ConvNets
- DetectiumFire: A Comprehensive Multi-modal Dataset Bridging Vision and Language for Fire Understanding
- DitHub: A Modular Framework for Incremental Open-Vocabulary Object Detection
- Dr. RAW: Towards General High-Level Vision from RAW with Efficient Task Conditioning
- ELDET: Early-Learning Distillation with Noisy Labels for Object Detection
- Embodied Crowd Counting
- FlexEvent: Towards Flexible Event-Frame Object Detection at Varying Operational Frequencies
- GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers
- Mars-Bench: A Benchmark for Evaluating Foundation Models for Mars Science Tasks
- Multimodal Causal Reasoning for UAV Object Detection
- Promptable 3-D Object Localization with Latent Diffusion Models
- Rethinking Scale-Aware Temporal Encoding for Event-based Object Detection
- SDPGO: Efficient Self-Distillation Training Meets Proximal Gradient Optimization
- T-norm Selection for Object Detection in Autonomous Driving with Logical Constraints
- Unveiling the Spatial-temporal Effective Receptive Fields of Spiking Neural Networks
- Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension