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Found 30 papers, 12 papers with code
CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data
Contrastive learning has emerged as a transformative method for learning effective visual representations through the alignment of image and text embeddings.
OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
To this end, we release OpenELM, a state-of-the-art open language model.
Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation
Despite the successes of large language models (LLMs), they exhibit significant drawbacks, particularly when processing long contexts.
Speculative Streaming: Fast LLM Inference without Auxiliary Models
Speculative decoding is a prominent technique to speed up the inference of a large target language model based on predictions of an auxiliary draft model.
Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving
Closed-set 3D perception models trained on only a pre-defined set of object categories can be inadequate for safety critical applications such as autonomous driving where new object types can be encountered after deployment.
3D Human Keypoints Estimation From Point Clouds in the Wild Without Human Labels
We show that by training on a large training set from Waymo Open Dataset without any human annotated keypoints, we are able to achieve reasonable performance as compared to the fully supervised approach.
GINA-3D: Learning to Generate Implicit Neural Assets in the Wild
Modeling the 3D world from sensor data for simulation is a scalable way of developing testing and validation environments for robotic learning problems such as autonomous driving.
Improving the Intra-class Long-tail in 3D Detection via Rare Example Mining
Continued improvements in deep learning architectures have steadily advanced the overall performance of 3D object detectors to levels on par with humans for certain tasks and datasets, where the overall performance is mostly driven by common examples.
Motion Inspired Unsupervised Perception and Prediction in Autonomous Driving
Learning-based perception and prediction modules in modern autonomous driving systems typically rely on expensive human annotation and are designed to perceive only a handful of predefined object categories.
Revisiting 3D Object Detection From an Egocentric Perspective
Given the insight that SDE would benefit from more accurate geometry descriptions, we propose to represent objects as amodal contours, specifically amodal star-shaped polygons, and devise a simple model, StarPoly, to predict such contours.
Offboard 3D Object Detection from Point Cloud Sequences
While current 3D object recognition research mostly focuses on the real-time, onboard scenario, there are many offboard use cases of perception that are largely under-explored, such as using machines to automatically generate high-quality 3D labels.
Scale Normalized Image Pyramids with AutoFocus for Object Detection
The resulting algorithm is referred to as AutoFocus and results in a 2. 5-5 times speed-up during inference when used with SNIP.
ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware Priors
The widely adopted sequential variant of Non Maximum Suppression (or Greedy-NMS) is a crucial module for object-detection pipelines.
DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes
In contrast, we propose a general-purpose method that works on both indoor and outdoor scenes.
Adversarial Training for Free!
Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks.
An Analysis of Pre-Training on Object Detection
We analyze how well their features generalize to tasks like image classification, semantic segmentation and object detection on small datasets like PASCAL-VOC, Caltech-256, SUN-397, Flowers-102 etc.
FA-RPN: Floating Region Proposals for Face Detection
We propose a novel approach for generating region proposals for performing face-detection.
AutoFocus: Efficient Multi-Scale Inference
Instead of processing an entire image pyramid, AutoFocus adopts a coarse to fine approach and only processes regions which are likely to contain small objects at finer scales.
Universal Adversarial Training
Standard adversarial attacks change the predicted class label of a selected image by adding specially tailored small perturbations to its pixels.
Generate, Segment and Refine: Towards Generic Manipulation Segmentation
The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet.
Soft Sampling for Robust Object Detection
Interestingly, we observe that after dropping 30% of the annotations (and labeling them as background), the performance of CNN-based object detectors like Faster-RCNN only drops by 5% on the PASCAL VOC dataset.
SNIPER: Efficient Multi-Scale Training
Our implementation based on Faster-RCNN with a ResNet-101 backbone obtains an mAP of 47. 6% on the COCO dataset for bounding box detection and can process 5 images per second during inference with a single GPU.
Ranked #113 on
Object Detection
on COCO test-dev
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
The proposed attacks use "clean-labels"; they don't require the attacker to have any control over the labeling of training data.
Face-MagNet: Magnifying Feature Maps to Detect Small Faces
In this paper, we introduce the Face Magnifier Network (Face-MageNet), a face detector based on the Faster-RCNN framework which enables the flow of discriminative information of small scale faces to the classifier without any skip or residual connections.
SSH: Single Stage Headless Face Detector
Surprisingly, with a headless VGG-16, SSH beats the ResNet-101-based state-of-the-art on the WIDER dataset.
Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
In this work, we propose an efficient and effective approach for unconstrained salient object detection in images using deep convolutional neural networks.
Supervised Incremental Hashing
In the first stage of classification, binary codes are considered as class labels by a set of binary SVMs; each corresponds to one bit.
On Large-Scale Retrieval: Binary or n-ary Coding?
To make large-scale search feasible, Distance Estimation and Subset Indexing are the main approaches.
Local Similarities, Global Coding: An Algorithm for Feature Coding and its Applications
Data coding as a building block of several image processing algorithms has been received great attention recently.
