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Found 10 papers, 1 papers with code
Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous Driving
Talk2BEV is a large vision-language model (LVLM) interface for bird's-eye view (BEV) maps in autonomous driving contexts.
Hardware Software Co-design of Statistical and Deep Learning Frameworks for Wideband Sensing on Zynq System on Chip
The first contribution of this work is efficiently mapping the OMP algorithm on the Zynq system-on-chip (ZSoC) consisting of an ARM processor and FPGA.
INDIGO: Intrinsic Multimodality for Domain Generalization
To this end, we propose IntriNsic multimodality for DomaIn GeneralizatiOn (INDIGO), a simple and elegant way of leveraging the intrinsic modality present in these pre-trained multimodal networks along with the visual modality to enhance generalization to unseen domains at test-time.
Unseen Classes at a Later Time? No Problem
Secondly, we introduce a unified feature-generative framework for CGZSL that leverages bi-directional incremental alignment to dynamically adapt to addition of new classes, with or without labeled data, that arrive over time in any of these CGZSL settings.
Resource Constrained Neural Networks for 5G Direction-of-Arrival Estimation in Micro-controllers
With the introduction of shared spectrum sensing and beam-forming based multi-antenna transceivers, 5G networks demand spectrum sensing to identify opportunities in time, frequency, and spatial domains.
Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains
Recent progress towards designing models that can generalize to unseen domains (i. e domain generalization) or unseen classes (i. e zero-shot learning) has embarked interest towards building models that can tackle both domain-shift and semantic shift simultaneously (i. e zero-shot domain generalization).
Structured Latent Embeddings for Recognizing Unseen Classes in Unseen Domains
The need to address the scarcity of task-specific annotated data has resulted in concerted efforts in recent years for specific settings such as zero-shot learning (ZSL) and domain generalization (DG), to separately address the issues of semantic shift and domain shift, respectively.
Learn from Anywhere: Rethinking Generalized Zero-Shot Learning with Limited Supervision
In this work, we present a practical setting of inductive zero and few-shot learning, where unlabeled images from other out-of-data classes, that do not belong to seen or unseen categories, can be used to improve generalization in any-shot learning.
Two-Level Adversarial Visual-Semantic Coupling for Generalized Zero-shot Learning
The performance of generative zero-shot methods mainly depends on the quality of generated features and how well the model facilitates knowledge transfer between visual and semantic domains.
Novel Deep Learning Framework for Wideband Spectrum Characterization at Sub-Nyquist Rate
Spectrum characterization involves the identification of vacant bands along with center frequency and parameters (energy, modulation, etc.)
