Parameter-Efficient Fine-Tuning (PEFT) methods for Transformers are designed for floating-point weights. When applied to extremely low-bit models (e.g., ternary {-1,0,1) they convert the base weights to floating point (dequantization) to add the update and then quantize again, which can diminish the benefits of aggressive quantization. We introduce a multiplicative ternary adapter that enables in-domain fine-tuning by applying an element-wise ternary mask to the base ternary Transformer weights, avoiding any dequantization to floating point and allowing direct merging back into the model. Constructed as the Kronecker product of two small trainable matrices and applied via a Hadamard product, the adapter preserves the ternary domain and merges with zero inference overhead. On a ternarized Llama-3.2-1B model, our method recovers substantial accuracy and surpasses stronger 2-bit baselines on most tasks, while retaining the efficiency advantages of ternary weights.

Event-based cameras represent a new alternative to traditional frame based sensors, with advantages in lower output bandwidth, lower latency and higher dynamic range, thanks to their independent, asynchronous pixels. These advantages prompted the development of computer vision methods on event data in the last decade, however event-based datasets are still in early stages in terms of size and complexity compared to normal datasets (e.g. ImageNet). This paper explores event data augmentation by superimposing two existing event datasets (N-MNIST and N-Caltech101) and by adding uniform noise. It shows that training an instance segmentation model on noisy datasets does not improve its performance, but the amount and type of noise added in the background decreases the performance of such model.