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Found 17 papers, 8 papers with code
Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection
In this paper, we propose a new Multi-Glimpse LSTM (MG-LSTM) network, in which multi-scale contextual information is sequentially integrated to promote the human detection performance.
R-FCN-3000 at 30fps: Decoupling Detection and Classification
Our approach is a modification of the R-FCN architecture in which position-sensitive filters are shared across different object classes for performing localization.
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.
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
Clean-label poisoning attacks inject innocuous looking (and "correctly" labeled) poison images into training data, causing a model to misclassify a targeted image after being trained on this data.
Improved Training of Certifiably Robust Models
Convex relaxations are effective for training and certifying neural networks against norm-bounded adversarial attacks, but they leave a large gap between certifiable and empirical (PGD) robustness.
Learning from Noisy Anchors for One-stage Object Detection
State-of-the-art object detectors rely on regressing and classifying an extensive list of possible anchors, which are divided into positive and negative samples based on their intersection-over-union (IoU) with corresponding groundtruth objects.
Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers
Convex relaxations are effective for training and certifying neural networks against norm-bounded adversarial attacks, but they leave a large gap between certifiable and empirical robustness.
2D or not 2D? Adaptive 3D Convolution Selection for Efficient Video Recognition
Then, only frames and convolutions that are selected by the selection network are used in the 3D model to generate predictions.
Ranked #11 on
Action Recognition
on ActivityNet
HCMS: Hierarchical and Conditional Modality Selection for Efficient Video Recognition
Conventional video recognition pipelines typically fuse multimodal features for improved performance.
Rethinking Pseudo Labels for Semi-Supervised Object Detection
In this paper, we introduce certainty-aware pseudo labels tailored for object detection, which can effectively estimate the classification and localization quality of derived pseudo labels.
Ranked #8 on
Semi-Supervised Object Detection
on COCO 100% labeled data
(using extra training data)
Efficient Video Transformers with Spatial-Temporal Token Selection
Video transformers have achieved impressive results on major video recognition benchmarks, which however suffer from high computational cost.
AdaViT: Adaptive Vision Transformers for Efficient Image Recognition
To this end, we introduce AdaViT, an adaptive computation framework that learns to derive usage policies on which patches, self-attention heads and transformer blocks to use throughout the backbone on a per-input basis, aiming to improve inference efficiency of vision transformers with a minimal drop of accuracy for image recognition.
Semi-Supervised Single-View 3D Reconstruction via Prototype Shape Priors
In particular, we introduce an attention-guided prototype shape prior module for guiding realistic object reconstruction.
BMB: Balanced Memory Bank for Imbalanced Semi-supervised Learning
Exploring a substantial amount of unlabeled data, semi-supervised learning (SSL) boosts the recognition performance when only a limited number of labels are provided.
SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation
In-context segmentation aims at segmenting novel images using a few labeled example images, termed as "in-context examples", exploring content similarities between examples and the target.
Object Recognition as Next Token Prediction
We present an approach to pose object recognition as next token prediction.
MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding
However, existing LLM-based large multimodal models (e. g., Video-LLaMA, VideoChat) can only take in a limited number of frames for short video understanding.
Ranked #1 on
Video Classification
on COIN
