Modern detection transformers (DETRs) use a set of object queries to predict a list of
bounding boxes, sort them by their classification confidence scores, and select the top …

Temporal sentence grounding aims to localize moments relevant to a language description.
Recently, DETR-like approaches achieved notable progress by predicting the center and …

This work studies the recently proposed challenging and practical Multi-class Unsupervised
Anomaly Detection (MUAD) task, which only requires normal images for training while …

With the transformative impact of the Transformer DETR pioneered the application of the
encoder-decoder architecture to object detection. A collection of follow-up research eg …

Current end-to-end detectors typically exploit transformers to detect objects and show
promising performance. Among them, Deformable DETR is a representative paradigm that …

Traditional 2D pose estimation models are limited by their category-specific design, making
them suitable only for predefined object categories. This restriction becomes particularly …

Class-agnostic counting (CAC) aims to count objects of interest from a query image given
few exemplars. This task is typically addressed by extracting the features of query image and …

Detection Transformer (DETR) predicts object bounding boxes and classes from learned
object queries. However, DETR exhibits three major flaws:(1) Only a subset of object queries …

In this work, we present PoIFusion, a simple yet effective multi-modal 3D object detection
framework to fuse the information of RGB images and LiDAR point clouds at the point of …

In this paper, we present a light-weight detection transformer, LW-DETR, which outperforms
YOLOs for real-time object detection. The architecture is a simple stack of a ViT encoder, a …