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Found 17 papers, 7 papers with code
Investigating Active Learning in Interactive Neural Machine Translation
It also improves the present state-of-the-art by 0. 35 and 0. 12 BLEU points for German-English and Spanish-English and respectively.
Product Review Translation using Phrase Replacement and Attention Guided Noise Augmentation
Given that 44% of Indian population speaks and operates in Hindi language and we address the above challenges by presenting an English–to–Hindi neural machine translation (NMT) system to translate the product reviews available on e-commerce websites by creating an in-domain parallel corpora and handling various types of noise in reviews via two data augmentation techniques and viz.
EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS
We validate the effectiveness of our approach on a variety of datasets and scenes preserving the visual quality while consuming 10-20x less memory and faster training/inference speed.
SHACIRA: Scalable HAsh-grid Compression for Implicit Neural Representations
Implicit Neural Representations (INR) or neural fields have emerged as a popular framework to encode multimedia signals such as images and radiance fields while retaining high-quality.
Chop & Learn: Recognizing and Generating Object-State Compositions
Recognizing and generating object-state compositions has been a challenging task, especially when generalizing to unseen compositions.
ASIC: Aligning Sparse in-the-wild Image Collections
We present a self-supervised technique that directly optimizes on a sparse collection of images of a particular object/object category to obtain consistent dense correspondences across the collection.
Teaching Matters: Investigating the Role of Supervision in Vision Transformers
We compare ViTs trained through different methods of supervision, and show that they learn a diverse range of behaviors in terms of their attention, representations, and downstream performance.
Robot to Human Object Handover using Vision and Joint Torque Sensor Modalities
We present a robot-to-human object handover algorithm and implement it on a 7-DOF arm equipped with a 3-finger mechanical hand.
Neural Space-filling Curves
We present Neural Space-filling Curves (SFCs), a data-driven approach to infer a context-based scan order for a set of images.
LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification
We introduce LilNetX, an end-to-end trainable technique for neural networks that enables learning models with specified accuracy-rate-computation trade-off.
PatchGame: Learning to Signal Mid-level Patches in Referential Games
We study a referential game (a type of signaling game) where two agents communicate with each other via a discrete bottleneck to achieve a common goal.
Multimodal Attention for Layout Synthesis in Diverse Domains
We address the problem of scene layout generation for diverse domains such as images, mobile applications, documents and 3D objects.
The Lottery Ticket Hypothesis for Object Recognition
The recently proposed Lottery Ticket Hypothesis (LTH) states that deep neural networks trained on large datasets contain smaller subnetworks that achieve on par performance as the dense networks.
Improved Modeling of 3D Shapes with Multi-view Depth Maps
Our simple encoder-decoder framework, comprised of a novel identity encoder and class-conditional viewpoint generator, generates 3D consistent depth maps.
LayoutTransformer: Layout Generation and Completion with Self-attention
Generating a new layout or extending an existing layout requires understanding the relationships between these primitives.
Generalized Grasping for Mechanical Grippers for Unknown Objects with Partial Point Cloud Representations
We present a generalized grasping algorithm that uses point clouds (i. e. a group of points and their respective surface normals) to discover grasp pose solutions for multiple grasp types, executed by a mechanical gripper, in near real-time.
PatchVAE: Learning Local Latent Codes for Recognition
Unsupervised representation learning holds the promise of exploiting large amounts of unlabeled data to learn general representations.
