DynaNav: Dynamic Feature and Layer Selection for Efficient Visual Navigation
Authors: Jiahui Wang
Changhao Chen
Venue: NeurIPS 2025 poster ()
Keywords: Visual Navigation
Dynamic Neural Network
Primary area: applications

Abstract: Visual navigation is essential for robotics and embodied AI. However, existing foundation models, particularly those with transformer decoders, suffer from high computational overhead and lack interpretability, limiting their deployment on edge devices. To address this, we propose DynaNav, a Dynamic Visual Navigation framework that adapts feature and layer selection based on scene complexity. It employs a trainable hard feature selector for sparse operations, enhancing efficiency and interpretability. Additionally, we integrate feature selection into an early-exit mechanism, with Bayesian Optimization determining optimal exit thresholds to reduce computational cost. Extensive experiments in real-world-based datasets and simulated environments demonstrate the effectiveness of DynaNav. Compared to ViNT, DynaNav achieves a $2.6\times$ reduction in FLOPs, 42.3% lower inference time, and 32.8% lower memory usage while improving navigation performance across four public datasets.

DynaNav: Dynamic Feature and Layer Selection for Efficient Visual Navigation
Jiahui Wang' Changhao Chen?
Coollege of Design and Engineering, National University of Singapore 2PEAK-Lab, The Hong Kong University of Science and Technology (Guangzhou)
wjiahui @u edu changhaacchemhkuus ceressttgz.euu.c..
Abstract
Visual navigation is essential for robotics and embodied AL. However, existing foundation models, particularly those with transformer decoders, suffer from high computational overhead and lack interpretability. limiting their deployment resource-tight scenarios. To address this, we propose DynaNav, Dynamic Vi- sual Navigation framework that adapts feature and layer selection based on scene complexity. employs trainable hard feature selector for sparse operations, enhancing efficiency and interpretability. Additionally, we integrate feature se- lection into an early-exit mechanism, with Bayesian Optimization determining optimal exit thresholds to reduce computational cost. Extensive experiments in real-world- based datasets and simulated environments demonstrate the effective- ness of DynaNav. Compared to ViNT, DynaNav achieves a 2.26x reduction in FLOOss, 42.3% lower inference time, and 32.8% lower memory usage, while im- proving navigation performance across four public datasets.
Introduction
Visual navigation is a fundamental capability for robotics and embodied AI, enabling autonomous agents to perceive, interpret, and navigate complex 3D environments based on visual inputs [0-3]. Its applications span real-world scenarios, such as delivery and logistics, as well as virtual domains, including gaming and simulation. By bridging perception and action, visual navigation plays a cru- cial role in intelligent systems. Recently, there has been growing interest in developing foundation models for visual navigation [+]
[D, M.] ViNT [S]] notable example that learns from large-scale egocentric observations using transformer layers on CNN-extracted features, demonstrating strong generalization across robotic platforms and environments. NoMad [K] further builds on this by incor- porating diffusion policy and goal-masking mechanism. PixNav [D]] utilizes textual heuristics and large language models(LLMMs to explore zero-shot possibility. However, these approaches, particu- larly those relying deep neural architectures such transformer decoders, introduce significant computational overhead, posing challenges for edge deployment where efficiency is paramount.
Robotic applications demand greater efficiency than large cloud-based models. As the trend toward efficient foundation models continues [[TI [12], reducing the computational burden of visual navi- gation models is key challenge. Additionally, existing models function "black boxes," raising concerns about interpretability. As humans and intelligent agents increasingly coexist, explainability becomes essential. These challenges lead to two critical research questions:
Is it necessary to activate all transformer layers for every navigation scenario?
Which features most important in the decoding process, and can we identify the most salient regions or pixels for navigation?
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Humans do not always activate all neurons for visual tasks [[3]]; rather. the brain dynamically recruits resources based on task complexity. Inspired by this, we propose that visual navigation models should adopt dynamic inference mechanisms, selectively utilizing features and neural layers based on scene complexity. In simpler scenarios, the model should rely on fewer features and layers for efficient computation, whereas in more complex tasks, it should allocate additional resources to ensure accurate decision-making.
To this end, we propose DynaNav, a highly efficient Dynamic Visual Navigation framework that adaptively selects relevant features and neural layers based on visual observations. Our approach employs a trainable hard feature selector to create sparse representations, enabling computationally efficient sparse operations at the feature level. This dynamic feature masking not only lowers com- putational overhead but also improves the understanding of which regions more relevantly influence the inference of visual navigation models, thereby enhancing explainability. Additionally, we in- troduce an early-exit strategy for deep Transformer layers by integrating feature selection into the early-exit mechanism, improving stability and computational efficiency. After training the decoder, Bayesian Optimization determines optimal early-exit thresholds. During inference, if a layer's fea- ture meets its threshold, computation terminates early, significantly reducing overall computational cost. Extensive experiments real-world datasets and in simulated environments demonstrate the effectiveness of our proposed DynaNav. Compared to ViNT [S], DynaNav achieves a 2.26 re- duction in FLOPs, 42.3% lower inference time, and 32.8% lower memory usage while improving navigation performance across four public datasets. To the best of our knowledge, this is the first work to introduce dynamic network mechanisms visual navigation models. To sum up. the main contributions of our work can be summarized as follows:
We propose DynaNav, highly efficient and effective dynamic neural model for visual navigation, introducing a novel feature and layer selection strategy to improve efficiency without compromising performance.
We integrate sparse feature selection into the early exit mechanism, improving the stabil- ity and success rate of dynamic layer inference, while the visualized mask enhances the interpretability of the navigation decision process.
Extensive experiments and simulations demonstrate that DynaNav achieves more than twice the efficiency while maintaining comparable success rates.
2
Related Work
2.1 End-to-end Visual Navigation
Nowadays, conducting robot learning from diverse datasets to obtain general model is becoming more and more popular [ [6]. Nonetheless, current approaches rely on real-world data, which is usually costly to obtain, lacks generalization, and is highly coupled with specific robot settings that are hard to transfer to different platforms [17] 18). Instead, our paper follows the paradigm of learning navigation behavior from data collected across multiple different real- real-world robotic sys- tems [T% 20 while focusing on training foundation model that can be adapted for various downstream tasks in zero-shot with small amounts of data. To this end, models like RT-1, I20, and GNM [2TI H, [ provide useful insights that study broad generalization across environments and embodiments for robots deployed in real-world settings. GNM [+]] demonstrates policy learning from heterogeneous RGB datasets but focuses on the singular task of reaching image goals in the zero-shot setting. ViNT [5]] trains an effective visual navigation policy that solve range of downstream tasks, such as navigating to GPS goals [23], goal images [22]] and skill-conditioned driving [25]. Building upon extensive prior work in visual navigation, ViNT combines two key el- ements: it uses topological graphs to keep track of how spaces are connected in the environment while employing trained policies handle the detailed movement controls [26-DZ 29, Z 30J. Recently, NoMaD [G] boosted the navigation task in previously unseen environments with goal masking tech- niques and diffusion policy.
2.2 Dynamic Network and Early Exit
Dynamic networks [31]-33] tend to optimize models that can modify their architecture or parameters based the input during the inference process. There are many techniques to achieve dynamic

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Training
Random Sampling
EfficientNe et Encoder
Record
Post Frmmes
Feature Map
Poe Framos
Feature
Past Fnimes
Transformer Layer
Difference
Trans Taassoommer Layer
Trans Transformen former Layer
Prediction Head
Observation Feature Map
Chservation
Feature Mask
Pred Action
Inferenee
N
ExII
Gool
Feature Map
Featua Mask
Exit?
Figure 1: The architecture of our DynaNav framework. DynaNav employs two encoder instances of the same architecture: one processes the concatenated observation and historical frame sequence, while the other extracts features from the early-fused combination of the current observation and goal image. Two independent feature selectors generate masks for observations and the goal, which then tokenized and fed into Transformer decoder. Training incorporates stochastic early-exit triggers intermediate decoder layers. During inference, decision step at each layer evaluates optimized thresholds to determine whether early exit conditions met.
network depth-wise and width-wise. For instance, layer-skipping [G4]), neurons-skipping [65]]. and low rank approximation (LoRA) [[6]]. Moreover, some dynamic networks focus adjusting the shape and value of weights adaptively during the inference, such as deformable convolution [GZ]], dynamic filter network [38], etc.. Among these techniques, early exiting gained popularity because of the prevalent Transformer-based model, which fits the inherent architecture with stacked blocks.
Early exiting is depth-wise dynamic method for halting forward propagation at certain layer based on intermediate predictions. This technique has been well explored both computer vi- sion [G 95], language processing [HS 5I]], and multimodality [52 [3]]. One challenge in imple- menting carly-cxiting models lies in finding a suitable metric to decide when to make an intermediate prediction. Commonly, metrics like probability confidence [Н]]] entropy value [Н7] are employed
traditional vision tasks. Some research also pointed out the possibility of using learning- baaed
early exit, which relies trained network [54] Ad 155, 56).. Recent research [322] [4] has extended
early exiting to the LLMs, which treat the autoregressive task as classification subgoal.
In this work, we are the first to utilize the idea of early exiting model. We further develop the current method [[ЛI] with the integration of sparse feature selection to the Bayesian optimization process in obtaining the desirable metric.
end-to-end visual navigation
3 Dynamic Visual Navigation Model
3.1 Framework Overview
To enable efficient and effective visual navigation, we propose DynaNav, dynamic navigation pipeline illustrated in Figure MI Unlike previous end-to-end models with static network inference, DynaNav integrates dynamic feature selector and an early-exit mechanism, reducing computa- tional costs while enhancing explainability and robustness. DynaNay begins with an EfficientNet backbone [577]] to extract features from RGB image sequences. Building on ViNT [5]] we introduce feature selector module that generates masks before feature processing in the Transformer decoder, allowing sparse computation. Additionally, we implement dynamic Transformer decoder, en- abling predictions at intermediate layers to improve efficiency. Finally, Bayesian optimization [[I] determines the optimal early-exit thresholds for our jointly trained model, further minimizing com- putational overhead.

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Feature Extraction. We chose EfficientNet-BO as our visual encoder due to its innovative com- pound scaling method, which optimally balances network width, depth, and resolution. Mathemat- ically, the encoder processes an input sequence consisting of consecutive visual observations Og, where E p, f), along with goal image Og. Fach observation is first mapped to latent space representation by the encoder, denoted as ((,,) HR xxC, where +b(-) represents the network, and H, W, C' correspond to the height, width, and number of channels in the feature map. To en- hance the connection between the current observation and the goal, we adopt an early fusion strategy. Specifically, the goal image is processed separately by another EfficientNet instance, producing a feature representation denoted o(oo og]) R xx* Xxxx where [:] represents concatenation. The detailed hyperparameter settings are provided in Section B.I
Dynamic Feature Selector. However, when the embedded feature map is large [[]]] computing such a tensor in transformer incurs significant computational costs [58], which limits the navi- gation model's s efficiency. Moreover, not all pixels in the observations and goal are essential; some redundant pixels can be ignored to improve processing efficiency [5%]. To address this, we introduce dynamic hard feature selector that generates mask to filter out pixels with minimal relevance to the final prediction.
Transformer Decoder. After the feature selection process, a transformer decoder D is employed to extract contextual features for action prediction, as illustrated in Figure II The stacked multi- head self-attention (MHSA) layers continuously refine the contextual information of visual tokens. Formally, we define the intermediate token as:
(1) where my-pet represents the generated masks for o;, i E [t - p. t). and D1.,, E [1.1]. denotes the first i layers of the decoder. The process for obtaining m detailed Section 3.21
X; D1:s ([m--p:t (b)--x))); m * <((o;;
Navigational Action Prediction. Finally, a head network is trained to predict both the action a, and the waypoint distance vector W,- When feature selection is applied, this prediction process can be formulated as a, Wt = /xx;), where l represents the prediction head. In our implementation, h consists of 4-layer transformer followed by an MLP with single hidden layer.
Training Objective. During training, we sample a sequence of visual images from the dataset to construct the observation 0t-pxt- A goal image o, is randomly selected for valid prediction length, where o, Ou+d and d [Imin |max]] The corresponding action sequence a a and waypoint w serve as ground truth. The objective of training is to maximize the likelihood of the predicted outputs aligning with the ground truth, formulated as:
L = E log (af" ax) + A Alog p (w. ww:]]
3.2 Dynamic Sparse Feature Selection
HxWxC
x2C
End-to-end visual navigation models often operate as black boxes [+] 1SI 50, WI mak- ing it unclear which parts of observa- tion contribute most to action prediction. Understanding these key elements could enable targeted preprocessing to enhance model performance. This leads to a fun- damental question: should all pixels be treated equally? Intuitively, the answer is noemphasizing only relevant pixels im- proves robustness. In real-world scenar- ios, indiscriminate reliance on all pixels can lead to failures, especially when ob- stacles obstruct a robots camera. To ad- dress this, we introduce feature selection approach based on the Gumbel-Softme mechanism [61],, dynamically prioritizing critical features. This improves perfor- mance and adaptability across diverse en- vironments and provides meaningful insights into the model's region of interest. The feature selector
1x1 Conv
[x] Conv
ReLU
ReL.
Input Feature
Expansion ML.
Gumble SoftMax
#xWxC
HxWxC
HxW xCC
Mask Filter
Sparse Feature
Feature Mask
Figure 2: Architecture of the dynamic feature selec- tor. A trainable MLP projects the input features to twice their original dimension. A pixel-wise Gumbel- Softmax operation then applied to compute selection probabilities.

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functions classification network. assigning each pixel probability score to generate masks for different input features. As shown in Figure the feature selector f(-) takes encoded features as input and outputs corresponding masks as follows,
m: (()().)); m. f((((o,;)))) C R#xw
(3)
Within the feature selector, the latent feature (f,),) is projected into a higher-dimensional space:
Z MLP(v((.)) "xxxxxxx2
where M.P(-) denotes a multi-layer perceptron. Here, 24 2.cck represents the unnormalized log probability of the k-th category for the n-th pixel and e-th channel. To obtain the one-hot mask, utilize the Gumble-SoftMax trick, which first adds a log term to each element and then conducts the SoftMax. The logarithm term is defined
n.c.k log log (u) 9:
u m.ckk U(O,1) 1),
(5)
and the processed value in Z, is 2 znick nick the last dimension:
2i + gi
n.ck
Then the SoftMax is applied on Z; along
gmick exp(
sn.ck
h: == 1,2,
(6)
Σx== exp(
where T is a temperature hyperparameter. At last, we manually define the last dimension of Z, as the generated mask, i.e. m, = 2p.c.2 The feature selector will gradually filter out the undesired
features as the training continues.
Figure B presents the visualized input and its gradients that are processed through the feature selector. The spatial importance weights are visu- alized through saliency maps, where the attention mask is upsampled to match the input dimensions. The brightness intensity of each pixel in the visualization corresponds to its se- lection probability by the feature se- lection mechanism. The results in- dicate redundancy within the input data, while the navigation model does not specifically foeus the largest common object between the observa- tion and the goal. This finding only supports the feasibility of filter- ing pixels but also enhances the interpretability of the navigation process. After selection, we can utilize data sparselization techniques [62) 53 to save space.
Obs RGB
Obs Sallency
Goal RGB
Goal Sallency Map
Figure 3: Visualization of saliency maps for observation and goal images.
3.3 Dynamic Transformer Layer Inference
3.3.1 Feature-Aware Early Exit Strategy
Transformer-basee decoders are highly effective in visual navigation, leveraging long-range depen- dencies and flexible adaptation [64[622]] However, models like ViNT [R]] employ a scene-agnostic decoder that activates all layers indiscriminately, disregarding scene complexity and task require- ments. While beneficial for -ragesccaleaoe training, this approach imposes excessive computational demands on edge devices. We argue that activating every layer often unnecessarysimilar to how humans selectively engage neurons for cognitive tasks [[33, 68). To address this, we propose a dy- namic navigation decoder with an early-exit mechanism, allowing the model to halt computation based scene complexity and navigation needs. By leveraging intermediate features for action prediction, this methodthe first to introduce early exiting in visual navigationeliminates redundant computations. Additionally, we enhance efficiency and robustness by integrating feature selection as
5

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an initial step in the early-exit strategy. Our approach significantly reduces computational overhead while maintaining performance, making it well-suited for resource-constrained deployment.
Figure W outlines our early-exiting strategy workflow. DeeR-VLA [III] proposes a metric for robotic tasks, arguing that transformer layer features are inherently distinct. It uses an action consistency condition, measuring the difference in action outputs from an action head h:
(xx)) hx1,11)2 The Vi E (1,
However, this still requires activating multiple layers, limiting computational savings. To improve efficiency, we introduce aggressive early-exit strategy. When the L.2 difference between the goal state and the current observation falls below a training-derived threshold (based on masked pixel counts), we bypass the transformer decoder entirely and compute actions directly from the encoded tokens and a prediction head.
3.3.2 Adaptive Threshold Optimization
To determine the optimal early-exit threshold, we employ Bayesian Optimization [69, [ to itera- tively search for the best value under given constraints. Specifically, we consider the predicted action a, and waypoint w, alongside their respective ground truth values, al and w Our objective is to maximize the cosine similarity between predictions and ground truth by optimizing the early-exiting thresholds, denoted as 1) = {m1 m7: .......... : J. Consequently, the objective function is formulated as:
max ((n) T
1
Σ Sim(ar, m)) + A Sim(w,, w)):
(8)
7
where Sim(u, u, vi represents the cosine similarity between two vectors with a given early exit threshold 17. À is a hyperparameter that balances waypoint and action prediction, and T the total number of time steps in the task. To optimize this objective function, we introduce penalty function P(n) that enforces the required constraints. This function assigns positive value when n violates any constraint and remains zero otherwise. Incorporating this penalty into the optimization framework, we reformulate the problem as:
max V(n) = ( P(n),
where the penalty term, P(n) = Ex & max (0,gg())))) (1))), captures the weighted sum of constraint violations. Here, gk (7)) quantifies the extent to which the k-th constraint is violated, while & rep- resents its associated weight (remain constant). The specific constraints that the model must satisfy are outlined below.
Inference Time Constraint. Let Time(1) denote the average inference time over the entire test set, where 7/ represents the early exit decision parameters. To ensure the efficiency of the network, we impose a constraint that the average inference time remains below predefined threshold Tmax- Mathematically, this can be formulated as:
Time (1)
(n), s.t. Time(1) Tmux--
(10)
T
is the total number of test samples, Tmax is the maximum time, and Time:(1) denotes the inference time for the t-th sample under the given early exit strategy. This constraint ensures that the optimiza- tion selects an early exit criterion that balances computational efficiency, predictive performance, and real-time or ppplication-specifcc latency requirements.
GPU Memory Constraint. To ensure efficient deployment under limited GPU resources, we define Mem(1) the GPU memory usage when applying the early exit strategy. Since memory consump- tion can fluctuate during inference, we consider the peak memory usage across all inference steps and enforce an upper bound constraint:
Mem(n) = max Mem, (17), s.t. Mem(n) S G Gmax t=.....?
(11)
where M1m,(1) represents the memory consumption at time step L, and Gmax denotes the maximum allowable GPU memory. This constraint ensures that Bayesian optimization selects an early exit criterion that not only improves efficiency but also maintains fcasibility within hardware limitations.

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Table 1: Quantitative Comparison on Benchmarks. We highlight our method with the colored font the best and the second best value of each metric are reported with bold and underlined fonts, re- spectively.
Method
Sim(ax
Sim(w,, w
Cartion
Case
FLOPs (10")
Time(s/tra)
Menory (Gb)
VINI [S]
94.49
96.20
0.285
6,94
4.37
0.379
19.07
Rocon [X]
NoMad [8
96.64
0.207
6.44
7.46
11188
21.36
Ours
94,92
96.53
0.191
6,26
1,93
0.228
13.35
VINI [S]
88.50
93.47
0.531
15,80
4.37
0.379
19.07
Go-Stanford
NoMad
93.51
0.507
12.93
1.118
Ours
89.07
93.66
0.449
14.23
1.68
0.209
12.27
VINT [S]
89.66
93,16
0.686
10.95
4.37
0.379
19.07
SACSoN TD
NoMad [E]
93,69
0.501
9,66
7.46
1.118
21.36
Ours
90.54
93.72
0.493
9.62
1.68
0.209
12.27
[S]
96.89
0.141
16.08
0.379
SCAND [Q]
NoMad [E]
97.79
0.121
13.05
7.46
1.118
21.36
Ours
97.03
0.130
14.41
0.228
FLOPs Constraint. One of the most critical considerations in optimizing the early exit strategy controlling the computational cost, particularly the transformer decoder. To achieve this, define FLOPs(1) as the average floating point operations (FLOPs) required per trajectory. Our goal to ensure that the computational complexity remains within predefined upper bound, Fmax while maintaining the models performance. Formally, we express this constraint as:
T
FLOPPs(1) = FLOPs; s.t. FLOPs(n) S Fmax-
(12)
Here, FL)Ps;(1) represents the computational cost each exit point By integrating this con- straint into our Bayesian optimization framework, we explore the trade-off between computational efficiency and model accuracy, enabling us to identify the most effective early exit criteria within the computational limits.
Experiment
4.1 Experimental Setups
4.1.1 Datasets
We evaluated our method in two experimental settings: benchmark datasets and simulated envi- ronments. For the benchmark datasets, we select four diverse datasets assess the performance of our approach under various conditions. These include the Recon dataset [B]] which provides medium- speed (2m/s) outdoor data evaluate our method in real-world. dynamic outdoor settings, and the SCAND dataset [[/O], medium-speer speed dataset featuring environmental interactions. Addi- tionally, we include the Go-Stanford dataset [[ and the SACSoN dataset [[ZI], representing low- speed (0.5m/s) and medium-speed indoor scenarios, respectively. These datasets allow us to test our method across environments with varying speed characteristics. All datasets are pre-processed following the ViNT method [S]] to ensure consistency across experiments. For each dataset, randomly split the data into training (80%) and testing (20%) sets. The implementation detail is illustrated in Section B in the Appendix.
4.1.2 Evaluation Metrics
For the benchmark comparison, we report the cosine similarity between action angles and pre- dicted waypoints, denoted as Sim(a,, a and Sim(w,, w respectively (in percentage). Since NoMad [[i] only outputs waypoints through the diffusion process, we omit the action angle term for this model. To highlight the efficiency advantages our approach, we report the FLOPs and mem- ory usage of each model on the entire evaluation set. For inference, we measure the time required to predict single trajectory for each model. Additionally, we report the loss values for the action vector and distance.
7

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simulation, we track the progress of our model-driven agent until it either reaches the target or encounters collision. The success rate calculated as the mean of the ratio of progress length to total trajectory length.
For the CARLA [
4.2 Evaluation in Real-world Benchmarks
Table II illustrates the performance of our method on RECON [S], Go-Stanford [[],
Ours
2.26x
SACSoN [Z and SCAND [ datasets.
WNT
We compare the performance with
ViNT [R] and NoMad [B]. All models are
Maximal FLOPs 110"!
trained from scratch. Our model saves about 58% FLOPS across all benchmarks compared to ViNT [S]] while maintaining comparable accuracy. Figure B depicts the efficiency advantages of our model com- pared ViNT [5]] Our approach achieves 0.83% improvement in Sam, at) and 0.28% increase in Sim( W., wx") com- pared to ViNT [S] across four benchmarks.
Ours
1.66x
VINI
0.00
0.05
0:10
0:15
0.20
0.25
0:30
0.35
Interence Time (sutrajectory)
Figure 4: Efficiency comparison of upper: FLOPs, bottom: inference time between ours and ViNT on RE- CON dataset.
In terms of time efficiency. we save O. 16 seconds compared ViNT [[] and 0.89 seconds compared to NoMaD [B]. Despite this, NoMaD [6]] due to its diffusion refinement procedure, achieves an average performance that 0.2% higher than ours. However, NoMaD [6i] requires approximately four times the FLOPs of our method, making it less efficient. Notably, the average FLOPs of our dynamic model in RECON [5]] and SCAND [X]] are higher than those in SACSoN [ and Go-Stanford [ One reason for this is that the former two datasets are from outdoor environments,
while the latter two consist of indoor scenarios. The indoor datasets benefit from lower speeds, more controllable environments, and less complex lighting conditions. This finding also validates
the assumption that for more complex scene, activating more layers for accurate navigation.
4.3 Real-time Robotic Navigation in CARLA Simulation Environment
For the simulation, we first collected 200 trajectories of inline navigation data from CARLA [22] Town01 to fine-tune the pre-trained model. The data was gathered using an RGB cam- era and various sensors mounted on an autopilot agent operating at fre- quency of 4Hz. To ensure consis- tency, we standardized the image size to 640 x 480 pixels with a 90 field of view (FOV). We evaluated our method across three distinct CARLA environments: Town02 (Scene A), Town03 (Scene B), and 1own10 (Scene C). Scene A, with a small-town layout and simple residential- commercial mix, represents an "easy task" for the agent. Scene B, a larger urban map with round- abouts and large junctions, is considered a "medium task." Scene C, downtown area filled with skyscrapers, residential buildings, and parked cars, presents highly dynamic and complex envi- ronment, making it a "hard task." " For each scene, we collected 20 trajectories, which were used in the subsequent testing phase. The car was driven by BehaviorAgent [[Z2], maintaining consis- tent maximum speed of 20 km/h across all environments. More details are illustrated Appendix Section B
GNM
VINT
Ours
Figure 5: The simulation result CARLA Town02 environ- ment. The green dots represent the discrete goals, and the red dots represent the predicted waypoints.
Figure illustrates the visualized navigation performance of baselines and ours. GNM [ lacks enough generalization ability to achieve the target task. ViNT [5] and our method can successfully reach the target points. Note that the trajectory of ViNT [5]] has some drift. This due ViNT [S]] utilizing all features and decoder layers, potentially overfitting to training data and producing sub-

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Table 3: Ablation study of the effectiveness of individual modules the RECON dataset.
Dynamic decoder
Feature selector
Sim(ay, a.")
Sim(w;, w."
Laction
Edtst
FLOPPs (10")
Time
Memory
Half layers Half channel
91.05
93.28
0.332
7.53
2.61
0.306
17.48
89.70
92.41
0.390
7.71
2.19
0.270
12.11
0.285
6.94
4.37
0.379
93.68 68
95,42
0.274
7.08
2,41
0.251
16.49
94.81
96.44
0.205
6.30
4.06
0.377
18.22
94,92
96.53
0.191
6.26
1.93
0.228
13.35
optimal trajectories. Our approach dynamically activates transformer layers and selectively filters features, resulting superior trajectory performance.
Table 0 presents the success rate of dif- ferent models on the CARLA [[22] simu- lation. NoMad [[]] is unable to achieve agile real-time simulation on our test plat- form due to the computationally intensive nature of its diffusion process. The results show that, although our model has higher FLOOPs than GNM [ success rate shows a 38% improvement, demonstrating the effectiveness of our approach. Com- pared to ViNT [S], method not only achieves comparable performance but also reduces FLOPs by more than factor of two. Furthermore, as the simulation environment becomes more challenging (Scene A-ene C), the FLOPs required by model increase. This is because we use unified early exit metric across all three simulation environments. As the visual discrepancy between the observation and goal increases, model needs more decoder blocks to extract contex- tual information effectively.
Table 2: The comparison of our model with baselines in the CARLA under various environments. The best and the second best values of each metric are reported with bold and underlined fonts, respectively.
Environment
Model
Successful Rate
FLOPs (10%)
GNM
0.297
1.09
Scene A
ViNT
0.724
4.37
Ours
0.727
1.58
GNM [
0.288
1.09
Scene B
VINT S
0.659
4.37
Ours
0.664
1.70
GNM [
0.251
1.09
Scene C
ViNT [S]
0.589
4.37
Ours
0.588
1.93
4.4 Ablation Study
Ablation into Individual Modules: Table illustrates the effectiveness of proposed module. The dynamic decoder column represents whether we are using early exit on the transformer decoder. The first row shows the result when we simply deactivate half of the decoder layers. Similarly, the second row presents the result when we deliberately reduce the hidden channel size from C to C. Although these settings can improve efficiency, they usually lead to decreased performance and poor generalization (i.c., high accuracy on the training set but low accuracy on the testing set).. The rest of Table elaborates that without the dynamic decoder, the efficiency does not vary too much compared to the baseline. Moreover, by using the feature selector, the performance will be better, and the efficiency will also be boosted. This is because our proposed feature selector sparsifies the features and stabilizes the early exit process.
Ablation into Threshold Optimiza- tion: Table shows the different performances of whether we imple- ment an extra Bayesian Optimiza- tion (BO) after training as DeeR- VLA [[]] Moreover, it reports the in- fluence of whether allow an early exit before the decoder. Results show that without BO, early exit pro- cess can be impaired due subop- timal threshold. Besides, if we allow the early exit before the transformer decoder, although it can gain efficiency improvement, the over-
Table 4: Ablation study on whether using post-training Bayesian Optimization (BO) and allowing exit before the decoder. The best and the second best values of each metric are reported with bold and underlined fonts, respectively.
BO Pre-decoder Exit
Sim We, w!t)
Successful Rate
FLOPs (10")
96,30
0.725
2.46
96.22
0.719
2.27
96.58
/
96.53
0.727
1.93

---

Jurae sclecke
Average Jumped
2.0
Irequencyy
10.0
1.5
7.5
1.0
5.0
0.5
2.5
0.0
5800
38,50
5900
3950
6000
6050
0
Number of Selected Features
Number of jumped layers
Visualized relationship between the number of selected features and the skipped layers (Left) and the frequency of different numbers of skipped layers the context of with or without the feature selector (right).
Figure 6:
all accuracy will slightly decrease. Therefore, such a technique ought to be trade-off that requires careful design.
Ablation into Feature Selection for Early Exit: To assess the impact of proposed feature selector on the early exit mechanism, we evaluate the model on the RECON [S] test set with a batch size of I, both with and without the feature selector. For each sample, record the MEAN value of the action difference between each layer (0-1,1-2,2-3). Moreover, we record the early exit index, referring to it the number of skipped layers. In Figure we observe that within certain range of selected feature numbers. the average number of skipped layers remains high. This finding suggests that our feature selector helps determine an appropriate early exit threshold, thereby enhancing the frequency of early exiting. Additionally, Figure & shows that proposed feature selector increases the frequency 2-to-4 layer jumps, leading to improved efficiency. Therefore, by integrating the feature selector with early exit, our method achieves both more stable and more efficient performance.
5 Conclusion
In this work, we propose DynaNav, novel, highly efficient visual navigation model. We first introduce dynamic feature selector that filters observations and goals to extract robust, memory- efficient features. We also introduce feature-aware early exit criteria for the transformer decoders, using action consistency metrics optimized via Bayesian techniques. Our experimental results show significant reduction in computational overhead compared to existing foundation navigation mod- els while maintaining high performance across standard benchmarks and the CARLA simulation environment. The empirical evidence validates the effectiveness of our approach in achieving effi- cient and robust visual navigation.
To achieve optimal performance, our model requires an additional optimization process. Although the Bayesian optimization helps fine-tune the model and determine optimal thresholds, the added labor cost is non-negligible. Future work could involve implementing these optimization techniques concurrently with training to create more streamlined end-to-end system. Furthermore, the pro- posed feature selection mechanism can be integrated with various CNN-based encoder models to improve their overall efficiency.
6
Acknowledgement
This research is supported by the National University of Singapore under the NUS College of Design and Engineering Industry-focused Ring-Fenced PhD Scholarship programme. Changhao Chen is funded by the Young Elite Scientist Sponsorship Program by CAST (No. YESS20220181) and the National Natural Science Foundation China (NFSC) under the Grant Number 62573370.
References
[1] Y. Zhang, Z. Ma. J. Li. Y. Qiao, Z. Wang. J. Chai, Q. Wu, M. Bansal, and P. Kordjamshidi, "Vision- and-lang navigation today and tomorrow; A survey in the era of foundation models." arXiv preprint arXiv:2407.07035. 2024.
10

---

[2] Li, M. Li, Z. Cheng, Y. Dong. Y. Zhou, J.-Y. He. Q. Dai, T. Mitamura, and A. Hauptmann, "Human- aware vision-and-languaae navigation: bridging simulation to reality with dynamic human interactions," Advances in Neural Information Processing Systems, vol. 37, pp. 119411-119 442, 2025.
[3] A. Bar, G. Zhou, D. Tran, Darrell, and Y. LeCun, "Navigation world models," Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025, pp. 15 791-115 801.
[4] D. Shah, A. Sridhar, A. Bhorkar, N. Hirose, and S. Levine, "Gnm: A general navigation model to drive any robot," in 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 7226-7233.
[5] D. Shah, A. Sridhar, N. Dashora, K. Stachowicz, K. Black, N. Hirose, and S. Levine, "Vint: A foundation model for visual navigation," Conference on Robot Learning (CoRL.). PMLR, 2023, pp. 711-733.
[6] A. Sridhar, D. Shah, C. Glossop, and S. Levine, "Nomad: Goal masked diffusion policies for navigation and exploration." in 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024, pp. 63-70.
[7] D. Shah, B. Eysenbach, G. Kahn, N. Rhinehart, and S. Levine, "Ving: Learning open-world navigation with visual goals," in 2021 IEEE International Conference on Robotics and Automation (ICRA). TEEE, 2021, pp. 13 1321551332 222
[8] D. Shah, B. Osiński, S. Levine er al., "Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action," in Conference on robot learning. PMLR, 2023, pp. 492-504.
[9] W. Cai, S. Huang, G. Cheng, Y. Long, P. Gao, C. Sun, and H. Dong, "Bridging zero-shot object navigation and foundation models through pixel-guided navigation skill." in 2024 IEEE International Conference on Robotics and Automation (ICRA). TEEE, 2024, pp. 5228-5234.
[10] G. Zhou, Y. Hong, and Wu, "Navgpt: Explicit reasoning in assion-and-lane and-language navigation with large language models," in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 7, 2024, pp. 7641-7649.
[11] Y. Yue, Y. Wang. B. Kang, Y. Han, S. Wang, S. Song. J. Feng, and G. Huang. "Deer-vla: Dynamic inference of multimodal large language models for efficient robot execution," in The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.
[12] X. Sun, P. Zhang, Zhang, H. Shah, K. Saenko, and X. Xia, "Dime-fm: Distilling multimodal and efficient foundation models," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 15 55111155533
[13] A. A. Bharath and M. Petrou, Next generation artificial vision systems: Reverse engineering the human visual system. Artech House, 2008.
[14] C. Devin, A. Gupta, T. Darrell, P. Abbeel, and S. Levine, "Learning modular neural network policies for multi-task and multi-robot transfer," in 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017, pp. 2169-2176.
[15] S. Dasari, F. Ebert, S. Tian, S. Nair, B. Bucher, K. Schmeckpeper, S. Singh, Levine, and C. Finn, "Robonet: Large-scale multi-robot learning." in Conference on Robot Learning (CoRL). PMLR, 2020, pp. 885-897.
[16] F. Yu, H. Chen, X. Wang. W. Xian, Y. Chen, F. Liu, V. Madhavan, and T. Darrell, "Bdd100k: A diverse driving datasct for heterogeneous multitask learming" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp.
[17] A. Kadian, J. Truong, A. Gokaslan, A. Clegg. E. Wijmans, S. Lee, M. Savva, S. Chernova, and D. Batra, "Sim2real predictivity: Does evaluation in simulation predict real-world performance?" IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6670-6677, 2020.
[18] P. Auderson, Shrivastava, J. Truong, A. Majumdar, D. Parikh, D. Batra, and S. Lee, "Sim-to-real transfer for vision-ande aoooo-and-aaggeee lan-lane navigation." " in Conference on Robot Learning (CoRL). PMLR, 2021, pp. 671 671-681.
[19] N. Hirose, D. Shah, A. Sridhar, and S. Levine, "Exaug: Robot-conditioned navigation policies via geo- metric experience augmentation," in 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 4077-4084.
11

---

[20] A. Loquercio. A. I. Maqueda, C. R. Del-Blanco. and D. Scaramuzza, "Dronet: Learning to fly by driving." IEEE Roboticy and Automation Letters, vol. no. 2. pp. 1088-1095, 2018.
[21] J. T.uronn A. Zitkovich, S. Chernova, D. Batra, T. Zhang, J. Tan, and W. Yu, "Indoorsim-to-on -olutoooa- learning to navigate outdoors without any outdoor experience," IEEE Robotics and Automation Letters, 2024.
[22] A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu et al., "Rt-1: Robotics transformer for real-world control at scale," arXiv preprint arXiv:2212.06817 2022.
[23] M. Savva, A. Kadian, O. Maksymets, Y. Zhao, E. Wijmans, B. Jain, J. Straub, J. Liu, V. Koltun, J. Malik et al., "Habitat: A platform for embodied ai research," in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 9339-9347.
[24] Y. Zhu, R. Mottaghi, E. Kolve, J. J. Lim, A. Gupta, L. Fei-Fei, and A. Farhadi, "Target-driven visual navigation in indoor scenes using deep reinforcement lcarning." in 2017 IEEE International Conference on Robotics and Automation (ICRA). JEEE, 2017, pp. 3357-3364.
[25] F. Codevilla, M. Müller, A. López. V. Koltun, and A. Dosovitskiy. "End-to-end driving via conditional imitation learning." in 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018, pp. 4693-4700.
[26] N. Savinov, A. Dosovitskiy, and V. Koltun, "Semi- parametric topological memory for navigation," in International Conference Learning Representations (ICLR), 2018.
[27] J. Bruce, N. Sunderhauf, P. Mirowski, R. Hadsell, and M. Milford, "Learning deployable navigation policies at kilometer scale from single traversal," in Conference on Robot Learning (CoRL). PMLR, 2018, pp. 346-361.
[28] A. Faust, K. Oslund, O. Ramirez, A. Francis, L. Tapia, M. Fiser, and J. Davidson, "Prm-rl: Long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning." in 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018, pp. 5113-5120.
[291 X. Meng, N. Ratliff, Y. Xiang, and D. Fox, "'Scaling local control to large-scale topological navigation," "
in 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020, pp. 672-678.
[30] N. Hirose, F. Xia, R. Martin-Martin, A. Sadeghian, and S. Savarese, "Deep visual mpe-policy learning for navigation." IEEE Robotics and Automation Letters, vol. 4, no. 4. pp. 3184-3191, 2019.
[31] Y. Han, G. Huang, Song, L. Yang, H. Wang, and Y. Wang, "Dynamic neural networks: survey," TPAMI, vol. 44, no. 11. pp. 7436- 7456, 2021.
[32] L. Del Corro, A. Del Giorno, Agarwal, B. Yu, A. Awadallah, and S. Mukherjee, "Skipdecode: Autoregressive skip decoding with batching and caching for efficient Il inference." " arXiv preprint arXiv:2307.02628, 2023.
[33] D. Raposo, Ritter, Richards, T. Lillicrap, C. Humphreys, and A. Santoro, "Mixture-of-depths: Dy- namically allocating compute in transformer-basee language models," arXiv preprint arXiv:2404.0225. 2024.
[34] M. Elhoushi, A. Shrivastava, D. Liskovich, B. Hosmer, B. Wasti, L.. Lai, A. Mahmoud, B. Acun, S. Agar- wal, A. Roman et al., "Layer skip: Enabling carly exit inference and self-speculative decoding," arXiv preprint Xix:22444 16710, 2024.
[35] Y. Bengio, N. Léonard, and A. Courville, "Estimating or propagating gradients through stochastic neurons for conditional computation." arXiv preprint arXiv:1308.3433 2013.
[36] A. Davis and I. Arel, "Low-rank approximations for conditional feedforward computation in deep neural networks," arXiv preprint arXiv:1312.44 44611 2013.
[37] J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, "Deformable convolutional networks," in Proceedings of the IEEE international conference on computer vision, 2017, pp. 764-773.
[38] X. Jia, B. De Brabandere, T. Tuytelaars, and L. V. Gool, "Dynamic filter networks," Advances in neural information processngg systems, vol. 29, 2016.
[39] Y. Wang, R. Huang. S. Song, Z. Huang, and G. Huang, "Not all images are worth 16x16 words: Dynamic transformers for efficient image recognition," Advances in neural information processing systems, vol. 34, pp. 11 960 11 973, 2021.
12

---

[40] T. Bolukbasi, J. Wang, O. Dekel, and V. Saligrama. "Adaptive neural networks for efficient inference," in ICML, 2017.
[41] G. Huang, D. Chen. T. F., F. Wu, L. Van Der Maaten, and K. Q. Weinberger, "Multi-scale dense networks for resource efficient image classification." ICLR, 2018.
[42] Y. Han, D. Han, Z. Liu, Y. Wang, X. Pan, Y. Pu, C. Deng, J. Feng, Song, and G. Huang, "Dynamic perceiver for efficient visual recognition," in ICCV, 2023.
[43] L. Yang. H. Jiang, R. Cai, Y. Wang. S. Song. G. Huang, and Q. Tian, "Condensenet v2: Sparse feature reactivation for deep networks," " in CVPR, 2021.
[44] Y. Han, Z. Liu, Z. Yuan, Y. Pu, C. Wang, S. Song, and G. Huang, "Latency-aware unified dynamic net- works for efficient image recognition." IEEE Transactions Pattern Analysis and Machine Intelligence, pp. 1-17, 2024.
[45] M. Elbayad, J. Gu, E. Grave, and M. Auli, "Depth Seepth-adaptive transformer." ICLR, 2020.
[46] J. Xin, R. Tang. Y. Yu, and J. Lin. "Berxit: Early exiting for bert with better fine-tuning and extension regression." " ACL, 2021.
[47] Xin, Tang, Lee, Y. Yu, and J. Lin, "Deebert: Dynamic early exiting for accelerating bert inference," arXiv preprint arXiv:2004. 12993, 2020.
[48] Liu, P. Zhou, Z. Zhao, Z. Wang, H. Deng, and Q Ju, "Fasthert: a self-distilling bert with adaptive inference time," arXiv preprint arXiv:2004.02178. 2020.
[49] S. Mangrulkar, A. MS, and V. Sembium, "Be3r: Bert based early-exit using expert routing," in KDD, 2022.
[50] Y. Chen, X. Pan. Y. Li. B. Ding, and J. Zhou, "Ee-llm: Large-scale training and inference of early-exit large language models with 3d parallelism." arXiv preprint arXiv:231..04916, 2023.
[51] T. Schuster, A. Fisch, J. Gupta, M. Dehghani, D. Bahri, V. Tran, Y. Tay, and D. Metzler, "Confident adaptive language modeling," NeurIPS, 2022.
[52] Z. Fei. Yan. S. Wang. and Q. Tian, "Deecap: Dynamic early exiting for efficient image captioning." in CVPR, 2022, pp. 12216-12226.
[53] S. Tang, Y. Wang, Z. Kong. T. Zhang. Y. Li, C. Ding. Y. Wang. Y. Liang, and D. Xu, "You need multiple exiting: Dynamic early exiting for accelerating unified vision language model," in CVPR, 2023.
[54] A. Ghodrati, B. E. Bejnordi, and A. Habibian, "Framcexit: Conditional early exiting for efficient vidco recognition," in CVPR, 2021.
[55] Z. Ni, Y. Wang, R. Zhou, R. Lu, J. Guo, J. Hu, Z. Liu, Y. Yao, and G. Huang, "Adanat: Exploring adaptive policy for token-based image generation." in ECCV, 2024.
[56] C. Fang, C. He, Xiao, Y. Zhang, L. Tang. Y. Zhang, K. Li, and X. Li, "Real- world image dehazing with coherence-based label generator and cooperative unfolding network," arXiv preprint arXiv:2406.0796 2024.
[57] M. Tan and Q Le, "Efficientnet: Rethinking model scaling for convolutional neural networks," in ICML, 2019.
[58] A. Vaswani, N. Shazcer, N. Parmar, J. Uszkorcit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polo- sukhin, "Attention is all you need," in Advances in Neural Information Processing Systems, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, Fergus, S. Vishwanathan, and R. Gameut, Eds., vol. 30, 2017.
[59] K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, "Masked autoencoders are scalable vision learners," Proceedings of the IEFF/CVF conference on computer vision and pattern recognition, 2022, pp. 16 1600-160099
[60] D. Shah and S. Levine, "ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints"" in Proceedings of Robotics: Science and Systems, 2022.
[61] E. Jang, S. Gu. and B. Poole, "Categorical reparameterization with gumbel-softmax: arXiv preprint arXiv: 1,01 144, 2016.
13

---

[62] B. Wheatman and H. Xu. "Packed compressed sparse row: A dynamic graph representation," in 2018 IEEE High Performance extreme Computing Conference (HPEE). IEEE, 2018, pp. 1-7.
[63] S. Ruiter. S. Wolfgang, M. Tumnell. T. Triche, E. Carrier, and Z. DeBruine, "Yalue-compresse" sparse column (vesc): Sparse matrix storage for redundant data," in 2024 Data Compression Conference (DCC). JEEE, 2024, pp. 580-580.
[64] L. Yuan, Y. Chen, T. Wang. W. Yu, Y. Shi, Z.-H. Jiang, F. E. Tay, J. Feng, and S. Yan, "Tokens-to-tokon vit: Training vision transformers from scratch on imagenet," ICCV, 2021.
[65] A. Awadalla, I. Gao, J. Gardner, J. Hessel, Y. Hanafy, W. Zhu, K. Marathe, Y. Bitton, S. Gadre, S. Sagawa et al., "Openftamingo: open-source framework for training large autoregressive vision-language mod- cls," arXiv preprint ar arXiv:2308.0133 01390, 2023.
[66] H. Liu, Ci, Q. Wu, and Y.J. Lee, "Visual instruction tuning." " Advances in neural information processing systems (NeurIPS), vol. 36, pp. 34 892-84 916, 2023.
[67] Oquab, T. Darcet, Moutakanni, Massa, El-Nouby et al., "Dinov2: Learning robust visual features without supervision." Transac- tions on Machine Learning Research Journal, pp. 1-31, 2024.
Vo, M. Szafranice, Khalidov, Fernandez, D. Haziza,
[68]
Ramamurthy and Lakshminarayanan, "Human vision and perception." Handbook of advanced light- ing technology. pp. 1-23, 2015.
[69] B. Shahriari, K. Swersky, Z. Wang, R. Adams, and Freitas, "Taking the human out the loop: A review of bayesian optimization," Proceedings of the IEEE, 2015.
[70] Karnan, A. Nair, Xiao, G. Warnell, Pirk, Toshev, J. Hart, Biswas, and Stone, "Socially compliant navigation dataset (scand): A large-scale dataset of demonstrations for social navigation," JEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 11 808807-11 814, 2022.
[71] N. Hirose, D. Shah, A. Sridhar, and S. Levine, "Sacson: Scalable autonomous control for social naviga- tion," IEEE Robotics and Automation Letters, vol. 9, no. 1, pp. 49-56, 2023.
[72] A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, "Carla: An open urban driving simulator," in Conference on robot learning. PMLR, 2017, pp. 1-16.
[73] A. Gu and T. Dao, "Mamba: Linear-time sequence modeling with selective state spaces," arXiv preprint arXiv:2312.00752, 2023.
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Appendices of DynaNav: Dynamic Feature and Layer Selection for Efficient Visual Navigation
A
Overview
Section B illustrates the hyperparameters and details for training and inference. Section shows more experimental results. Section D illustrates more visualized results of the saliency heatmaps on observations and goals.
B Implementation Details
For pre-training, we adopt the same parameter settings ViNT [S] to ensure a fair comparison. notable difference, however, is that directly use the encoded features from EfficientNet-BO [57]] as tokens to the transformer layer, bypassing the MLP projection used by ViNT to reduce the dimen- sionality from 1280 to 512. This modification helps save computational resources and time. The input RGB images are resized to a resolution of 85 x64, with a batch size of 256. Both VINT and models are trained for 100 epochs under these conditions. For fine-tuning, set the learning rate to lc-4 and train for 80 epochs, deactivating the warm-up stage during this process.
Table 5: Hyperparameter Settings.
Hyperparameter
Value
General
Train Epochs
100
Fine-tuning Epochs
80
Input Resolution
85 x 64
Training LR
0.0005
Fine-tuning LRR
0.0001
Warmup Epochs
3
Optimizer
AdamW Cosine Annealing 256
LR Scheduler
Batch Size
À in loss
0.5
Backbone
Type
EfficientNet-b0
Hidden Dim
1280
Data
Length of past frames
5
Length of predicted waypoints
5
Max obs-goal distance(meter)
20
Min obs-goal distance(meter)
0
Transformer Decoder
Number of layers
4
Attention Heads
4
Bayesian Optimization
Sim ar at Constraint
0.950
Sim( w., w Constraint
0.960
FLOPs Constraint (109)
2,0
Time Constraint (sec)
0.3
Memory Constraint (GB)
14
Optimization Epochs
20
Constraint of Masked Pixels (obs)
2770
Constraint of Masked Pixels (goal)
3400
[0.8,0.5.1.0]
CARLA Realted
Max Speed
20km/h
Max Distance
900m
Capture Frequency
4Hz
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B.1 Hyper-parameter Setting
The detailed pepee--rrameter settings for our training and fne-tuning are Table
C More Experiment Result
C.1 Results with Mamba Decoder
We also test the performance of the Mamba [73 mamba. the resolution of our feature maps not large, the advantage of Mamba [X3]] can not be fully explored. On the other hand, the Mamba's [Z3] core computing structure the state space model (SSM) and its high-order recursive calculations- causes its calculation volume to increase rapidly under high-ding gigh-dimensional features. Comparing the Sim(w,, w²"), the performance using Mamba [[7]] blocks is lower than ours. This may result from the fact that Mamba [[3]] has advantages on long sequences but may not be able to fully utilize its recursive modeling capabilities on short sequences. Transformer [58] is more suitable for capturing global dependencies. Even if the sequence is short, can still use the self-attention mechanism to efficiently model the relationship between features.
block. Table BI illustrates the results of substituting
During the CARLA [72] simulation, the models predictions are normalized to compute the necessary waypoint offsets. These offsets, combined with the vehicles current location, determine the target waypoint. A PID controller is employed to generate control signals based on the target waypoint. To ensure smooth trajectory generation within the CARLA environment, we use an image captured six timestamps ahead of the current observation as the objective, carefully tracking the models progress over each run. As ViNT [S]] processes the waypoints in relative coordinates, represented follows:
W: (Pe+h - P.) R(O.),
(13)
where P and P. denote the position vectors of the goal and current points in world coordinates, respectively, and @ indicates matrix multiplication. 8r represents the vehicle's yaw, and R is the rotation matrix. Thus, the final target point is calculated as: P PI+h- P, w, 3 R(0,) T, where w, is the predicted waypoint offset.
Table 6: Quantitative Comparison on Benchmarks of ours and Mamba blocks.
Dataset
Method
Sim Wr, w
FLOPs(109)
Mamba A
95.09
4.41
RECON [S]]
Ours
96.53
1.93
Mamba 7Z
93.34
4.41
Go-Stanford 4
Ours
93.66
1.68
Mamba [73
92.92
4.41
SacSoN
Ours
93.72
1.68
Mamba 73
97.28
4.41
SCAND 70
Ours
97.43
1.93
C.2 Goal Image Viewpoint Investigation
In real-world scenarios, goal images often come from diverse sourcessuch as human-captured pho- tosand may not exactly align with the agents ego-centric view. Understanding how such domain and viewpoint differences impact performance is critical.
To investigate this, we conducted additional experiments in CARLA under three challenging condi- tions:
Same location, different angle: Goal image is taken from the same waypoint but with
camera orientation offset (within +
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Nearby location, same angle: Image is captured from a nearby position (within 5 meters), keeping the same orientation.
Nearby location, different angle: Goal is from nearby waypoint (within meters) and a different orientation.
Table 0 illustrates the quantitative results. Our model exhibits graceful degradation as the domain gap increasesi.c.. greater viewpoint positional differences. However, it consistently outperforms the ViNT bascline across all settings, highlighting the robustness and generalization our approach to goal images with moderate domain shifts.
Table 7: Comparison of model and ViNT under varying goal image settings in CARLA Scene A
Setting
Model
Success Rate
FLOPs (x10%)
ViNT
0.724
4.37
Same Position, Same Angle
Ours
0.727
1.58
ViNT
0.723
4.37
Nerby Position, Same Angle
Ours
0.725
1.58
ViNT
0.694
4.37
Same Position, Different Angle
Ours
0.708
1.74
ViNT
0.688
4.37
Nearby Position, Different Angle
Ours
0.691
1.79
C.3 Timestep-wise Consistency
To investigate the potential timestep-wise inconsistency, conducted an in-depth analysis 700- frame trajectory. We segmented trajectory into 100-frame intervals and computed the average FLOPs and inference time for each segment, comparing model against the baseline ViNT. As shown in Table our model consistently reduces both computational cost and inference time across all intervals, while maintaining or improving action similarity Sim (a,, a over 96% of the eval- uated trajectories, our approach is more efficient than ViNT without any degradation in navigation accuracy.
These results demonstrate that, despite the dynamic nature of carly exiting, our model exhibits stable, consistent, and efficient performance over time in practice.
Table Timestep-wise results compared with ViNT
No. of Frame
100
200
300
400
500
600
700
FLOPs (109)
4.37
4.37
4.37
4.37
4.37
4.37
4.37
ViNT
Avg Time (s)
0.218
0.218
0.218
0.218
0.218
0.218
0.218
Sim(ar, af"
94.41
94.46
94,49
94.48
94.52
94.51
94.49
FLOPs (109)
2.02
1.95
1.92
1.96
1.85
1,93
1.93
Ours
Avg Time (s) Sim (a, af"
0.194
0.190
0.189
0.191
0.185
0.190
0.190
94.76
94.88
94.92
94.90
94.92
94.92
94.92
C.4
Additional Ablation Study of Constraints
Our adaptive threshold optimization incorporates three constraints designed to jointly enhance model efficiency across FLOPs, time, and memory usage. To evaluate their individual contributions, we conducted an ablation study by removing each constraint separately.
As shown in Table H, enforcing the FLOPs constraint encourages more frequent layer skipping. effectively reducing inference time and memory consumption. However, removing either the time or memory constraint results in noticeable degradation across all efficiency metrics. This confirms that jointly optimizing all three constraints achieves the best overall performance and balanced resource utilization.
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Table Ablation Study On RECON Dataset
Setting
FLOPs (10")
Time (s/traj)
Memory (GB)
Ours
1.93
0.228
13.35
w/o FLOPs constrain
2.84
0,291
15.62
w/o Time constrain
2.55
0,273
15.09
w/o Memory constrain
2.16
0,255
14.75
C,5
Study on Robustness
We conducted each navigation trajectory in CARLA 10 times to evaluate the robustness of our method. Table M reports the FLOPs, average execution time, and average successful rate for se- lected trajectories across these runs. As shown, the results exhibit minimal variance, indicating strong consistency and low randomness. This stability is attributed to the synergy between our fea- ture selector and Bayesian optimization, which together enable adaptive yet reliable behavior across diverse scenarios.
Table 10:
Results of different separation simulations.
No. of Trajectory
3
4
6
7
9
10
FLOPPs (109)
1.91
1.93
1.92
1.91
1.90
1.92
1.94
1.93
1.93
1.91
Avg Time (s)
0.258
0.260
0.258
0.257
0.260
0.257
0.262
0.260
0.257
0.258
Success Rate
0.725
0.727
0.727
0.726
0.726
0.726
0.728
0.727
0.726
0.727
D More Visualizations
In this section, added more visualizations of saliency maps. Such saliency map helps identify the interest area after being processed by our proposed feature selector. From Figure D to Figure ID tell that the region of interest is not always located in the biggest common object between observation and goal images. The model "considers" more spatial information, which results in higher "attention" along the target direction. These findings support claims in Section II that there is redundant information in the observation and goal. In other words, it proves the rationality of using the proposed feature selector to filter features.
24

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Observation Image
Osservation Saliency Map
Goal Image
Goel Saliency Map
Observation Image
Observation Saliency Map
Goal Image
Figure 7: Salieny map of observation and goal images.
25

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Image
Sallency
Goal Image
Goal Saliency Map
Observation Image
Observetion Sallency Map
Gosl
Goal Saliency
Figure Salieny map of observation and goal images.
26

---

Observation
Osservation Saliency
Gool Image
Goal Saliancy Map
Observation Image
Sallency
Goal Image
Goal Sellency Map
Figure Salieny map of observation and goal images.
27

---

vation
Osservation Saliency
Goal Image
Goal Saliancy Mao
Observation Image
Sallency
Goal Image
Goal Sallency Map
Figure 10: Salieny map of observation and goal images.
28


Reviews:
[{"review_id": "XdG933PFOh", "reviewer": "Reviewer_rkEx", "summary": "A dynamic visual navigation framework called DynaNav is proposed, aiming to improve the efficiency and interpretability of visual navigation with the transformer architecture by dynamically selecting features and neural network layers.", "questions": "1. How is the navigation task in this paper set up? What is the input, and what is the prediction? Is it a supervised learning task or a reinforcement learning task? Can the model be trained on map A but navigate on map B? Is there a goal location to reach?\n2. What is the computational device for the model in training and evaluation?\n3. Is there a video of the experiment?", "limitations": "The limitations and negative societal impacts are not clearly stated. The authors may consider the potential impact if it is applied to robotics and mobile robots.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strength:\n\nThis paper introduces the dynamic network mechanism into the visual navigation model, which improves the efficiency through dynamic feature selection and layer selection while maintaining performance.\n\nWeaknesses:\n\nThe navigation task is not very clear. The example environment presented in Figure 5 seems simple.", "was_revised": true, "final_justification": "This paper presents a novel method for visual navigation; however, fundamental details are not sufficiently clear for readers who are not experts in the research direction and are unfamiliar with the methodology. Although I recommend a borderline acceptance, this paper should be revised to include those details, especially the differences compared with other visual navigation methods.", "rebuttal": "Thank you for your positive feedback. We’re pleased that you recognized our key contribution—introducing a dynamic network mechanism into visual navigation. Your acknowledgment that our method improves efficiency through dynamic feature and layer selection while maintaining performance underscores the practical and technical significance of our work. In the following parts, we provide detailed responses to each of your questions and hope they effectively address your concerns.\n\n> **Q1:** How is the navigation task in this paper set up? What is the input, and what is the prediction? Is it a supervised learning task or a reinforcement learning task? Can the model be trained on map A but navigate on map B? Is there a goal location to reach?\n\n**A1:** Thank you for your insightful and constructive feedback. Our work addresses the end-to-end visual navigation task by introducing a novel, efficient navigation foundational model. The input to the model consists of a sequence of images, including the current observation, the goal image, and the past five frames. The model outputs both navigational waypoints ($\\mathbf{w}$) and low-level actions ($\\mathbf{a}$), which guide the agent toward the target.\n\nThe task is formulated as an imitation learning problem, where the model learns to mimic human-controlled trajectories. Training is performed using real-world public datasets containing image sequences, corresponding waypoints, and actions derived from robot demonstrations. This allows the model to generalize to previously unseen environments. Specifically, as you mentioned, the model can be trained on map A and effectively navigate on a different map B.\n\nThis generalization capability stems from our proposed efficient Visual Navigation Foundational Model (VNFM), which is trained across diverse multi-scene datasets and leverages attention-based mechanisms for spatial reasoning. These design choices enable robust performance in unfamiliar environments.\n\nThe navigation goal is defined by a target image. During inference, a global topological map is first constructed to identify intermediate sub-goals. The VNFM then sequentially guides the agent through these sub-goals to reach the final destination. We will clarify and expand on these aspects in the revised version of the paper by updating the method section with a more precise description of the task setup.\n\n> **Q2:** What is the computational device for the model in training and evaluation?\n\n**A2:** Thank you for your thoughtful question. All training experiments were conducted on a workstation equipped with two NVIDIA RTX 3090 GPUs (24GB each), and training typically took around 35 hours. For evaluation, we used two computing platforms: one with an RTX 3090 and another with a consumer-grade RTX 4060 Ti GPU. The model achieves satisfactory performance even on a consumer-grade device, highlighting its efficiency and practicality for real-world deployment.\n\n> **Q3:** Is there a video of the experiment?\n\n**A3:** Thank you for your valuable feedback. We plan to release a demo showcasing the navigation performance on both real-world datasets and within the CARLA simulation environment. Our code and pretrained models will be made publicly available as open source. Additionally, we are happy to create a project page and include these demonstration videos in the revised version of the submission."}, {"review_id": "ZvkglyIE2h", "reviewer": "Reviewer_nvvJ", "summary": "The method designs an end-to-end dynamic model for the task of Visual Navigation. It improves the computational efficiency of the model through feature selection and an early-exit mechanism. The approach demonstrates strong performance on both real-world and multiple simulated benchmarks, while significantly reducing computational FLOPs.", "questions": "In the example shown in Figure 1, the goal image appears to be captured based on the agent's ego view. However, under the Visual Navigation task, the model should ideally enable the agent to reach the position where images are taken from the same viewpoint but different perspectives—for instance, having the agent locate the viewpoint of a picture taken by a human. Have there been any related attempts in this regard? How much does the Visual Goal from different domains impact the model's performance?\n\nDuring the Feature Extraction process, the original structure of the image may be disrupted. How is this issue addressed here? Is Position Embedding alone sufficient to ensure the model's performance?", "limitations": "Yes.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n1. The method takes into account the computational bottlenecks faced during the actual deployment of the model, and the designed dynamic model exhibits highly efficient computational performance. Meanwhile, the pruned additional information also significantly enhances the model's performance.\n\n2. Optimizing the early-exit threshold based on the deployed hardware conditions is a relatively innovative solution.\n\n**Weakness**\n\n1. In my opinion, the related methods do not seem to be tightly coupled with the Visual Navigation task. Tasks involving Transformer-based processing of sequential visual observations, such as objectNav, VLN, etc., could likely be accomplished using similar approaches. It would be helpful to have more discussion on this topic—for example, why the Visual Navigation task was chosen to validate this pruning scheme, or what specific characteristics of Visual Navigation make the proposed method particularly effective.\n\n2.Since the method primarily addresses the performance bottleneck issues faced during deployment, including real-world deployment experiments would make the article more comprehensive.", "was_revised": true, "final_justification": "The author gave a convincing reply to my question, I will keep the current rate.", "rebuttal": "We sincerely thank the reviewer for the positive feedback. We’re pleased that our focus on visual navigation challenges and the design of a dynamic, efficient model were well recognized. We especially appreciate your acknowledgment of our dynamic early-exit mechanism as a novel and practical solution for navigation foundation models, as well as the effectiveness of our pruning strategy in enhancing performance. Below, we provide detailed responses to each of your questions and hope they address your concerns. \n\n> **W1:** In my opinion, the related methods do not seem to be tightly coupled with the Visual Navigation task. Tasks involving Transformer-based processing of sequential visual observations, such as objectNav, VLN, etc., could likely be accomplished using similar approaches. It would be helpful to have more discussion on this topic—for example, why the Visual Navigation task was chosen to validate this pruning scheme, or what specific characteristics of Visual Navigation make the proposed method particularly effective?\n\n**A1:** Thank you for your thoughtful and constructive feedback. We agree that our method has broader applicability and could be extended to related tasks such as VLN and ObjectNav. VLN requires agents to follow natural language instructions to reach a target, involving complex multimodal reasoning. ObjectNav focuses on locating specific object categories based solely on visual input, emphasizing semantic understanding and efficient exploration. In contrast, our visual navigation task centers on visual navigation in unfamiliar scenes using goal images, with minimal semantic or language cues, highlighting low-level planning and spatial reasoning. \n\nWe chose visual navigation as the testbed for our pruning framework for two key reasons. First, it is a fundamental capability for embodied agents that does not rely on additional high-level supervision (e.g., language or object labels). Second, visual navigation models are often deployed on resource-constrained platforms, where efficiency and model compactness are crucial. While prior pruning methods have been largely used for static or closed-domain tasks, visual navigation introduces dynamic and open-world challenges, such as diverse scene geometries, varying illumination, and motion dynamics, offering a unique and meaningful opportunity to study the generalization and efficiency of lightweight models.\n\nOverall, we will expand the related work and discussion sections in the revised version to better clarify these task distinctions and the motivation behind our choice of visual navigation as the primary evaluation setting for DynaNav.\n\n> **W2:** Since the method primarily addresses the performance bottleneck issues faced during deployment, including real-world deployment experiments, it would make the article more comprehensive.\n\n**A2:** Thank you for your insightful suggestion. We fully agree that real-world deployment experiments would further strengthen the practical relevance of our method. In the current version, our approach is thoroughly trained and evaluated on four real-world datasets—Recon, Go-Stanford, SACSoN, and SCAND—comprising over 125 hours of video data. This extensive evaluation under consistent experimental settings allows for fair and comprehensive comparisons with existing baselines, as shown in Table 1 of our submission.\n\nIn addition, we conduct evaluations across three diverse CARLA simulation environments, further validating the robustness and generalizability of our approach in dynamic scenarios. These results demonstrate the effectiveness of our method in terms of both success rate and computational efficiency.\n\nAs part of our future work, we plan to deploy our model on a real robotic platform and will release a demonstration video to showcase its real-world performance. We will also mention this plan in the revised version of the paper to clarify our roadmap for deployment validation.\n\n> **Q1:** In the example shown in Figure 1, the goal image appears to be captured based on the agent's ego view. However, under the Visual Navigation task, the model should ideally enable the agent to reach the position where images are taken from the same viewpoint but different perspectives—for instance, having the agent locate the viewpoint of a picture taken by a human. Have there been any related attempts in this regard? How much does the Visual Goal from different domains impact the model's performance?\n\n**A3:** Thank you for this insightful and important question. We agree that in real-world scenarios, goal images often come from diverse sources—such as human-captured photos—and may not exactly align with the agent’s ego-centric view. Understanding how such domain and viewpoint differences impact performance is critical.\n\nTo investigate this, we conducted additional experiments in CARLA under three challenging conditions:\n\n- **Same location, different angle:** Goal image is taken from the same waypoint but with a camera orientation offset (within ±15°).\n- **Nearby location, same angle:** Image is captured from a nearby position (within 5 meters), keeping the same orientation.\n- **Nearby location, different angle:** Goal is from a nearby waypoint (within 5 meters) and a different orientation.\n\nAs shown in the table below, our model exhibits graceful degradation as the domain gap increases—i.e., greater viewpoint or positional differences. However, it consistently outperforms the ViNT baseline across all settings, highlighting the robustness and generalization of our approach to goal images with moderate domain shifts.\n\n\n\n|Setting|Model|Success Rate|FLOPs (×10⁹)|\n|-|-|-|-|\n|Same Position, Same Angle| ViNT | _0.724_| 4.37|\n||Ours|**0.727**| **1.58**|\n|Nearby Position, Same Angle| ViNT| _0.723_| 4.37|\n||Ours| **0.725**| **1.58**|\n| Same Position, Different Angle| ViNT | _0.694_| 4.37|\n|| Ours| **0.708**| **1.74**|\n| Nearby Position, Different Angle | ViNT | _0.688_| 4.37|\n|| Ours| **0.691**| **1.79**|\n\nIn the revised version, we will add more discussion and explanation on this point.\n\n> **Q2:** During the Feature Extraction process, the original structure of the image may be disrupted. How is this issue addressed here? Is Position Embedding alone sufficient to ensure the model's performance?\n\n**A4** Thank you for your valuable feedback. We agree that preserving the structural integrity of visual inputs during feature extraction is crucial for effective spatial reasoning. Our approach addresses this concern through two key strategies:\n\n1) Structured Positional Embedding:\nWhile the original spatial layout may be altered during patch embedding or dynamic token selection, we explicitly encode spatial information using structured positional embeddings. These embeddings are added to each visual token before the Transformer layers, allowing the model to retain and reason about the relative spatial relationships of different regions in the image. This mechanism is analogous to how the human visual system maintains spatial coherence despite shifting focus.\n\n2) Dynamic Token Selection with Implicit Spatial Awareness:\nOur model processes only a subset of informative tokens, which introduces sparsity but is guided by the model’s learned attention to task-relevant and spatially meaningful regions. This ensures that essential semantic and geometric information is preserved, even without processing the full image.\n\nAdditionally, the self-attention mechanism enables the model to capture pairwise relationships between tokens. When combined with positional encodings, this allows the network to reason about spatial structure even when parts of the input are omitted. Empirically, our results show that this design not only maintains performance but also enhances efficiency and robustness. As part of future work, we plan to incorporate explicit 3D priors or learned geometric representations to further strengthen spatial reasoning and improve generalization to more complex environments. In the revised version we will include these discussions and explainations to the corresponding section."}, {"review_id": "iUYIg6ILlU", "reviewer": "Reviewer_bh3K", "summary": "This paper proposes a dynamic feature and layer selection method to accelerate transformer-based visual navigation models. The authors reduce redundancy in frame processing by employing early exiting to halt forward propagation. Their approach achieves SOTA-level navigation performance while demonstrating significant advantages in time/memory costs.", "questions": "- To better evaluate the actual performance in terms of speed and memory usage, I would suggest including a timestep-wise comparison of time and memory costs.\n\n- This work appears to fall within the domain of image-goal navigation. While there exist other visual navigation methods [1,2] focusing on different navigation tasks, the current discussion lacks comparison with these approaches. If the authors intend to claim contributions to the broader field of visual navigation, a disscusion would be necessary.\n\n[1] NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation.\n[2] NaVILA: Legged Robot Vision-Language-Action Model for Navigation", "limitations": "yes (a short discussion in conclusion)", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths：\n\nThe idea of accelerating transformer-based visual navigation methods is valuable, and the proposed method is technically sound, which could inspire other research. The paper is well-written, with informative illustrations, and provides comprehensive details to facilitate understanding of the implementation. The experiments demonstrate that the proposed method significantly outperforms previous approaches in time/memory costs.\n\nWeaknesses:\n\n- While the early exiting strategy could reduce memory and time costs in the long run, it may introduce time-varying inference speeds (e.g., some time steps could be faster than others). Could this variability lead to inconsistent navigation performance?\n\n- How are the selected features organized after dynamic sparse feature selection? If the number of patches remains consistent, positional embeddings can simply indicate the spatial information of each patch. However, if the number of sparse features change dynamically, how does the transformer layer maintain an understanding of the spatial relationships between patches?\n\n- The lack of real-world experiments/videos makes it hard for me to analyze the actual performance of this method.", "was_revised": true, "final_justification": "I thank the author for the thoughtful response, which addresses all my concerns.\n\nFor real-world experiments/demos, I agree that the model is trained on large-scale real-world videos and could demonstrate real-world navigation performance like ViNT. Nevertheless, I encourage the author to include real-world experiments/demos in the revision.\n\nOverall, I will increase my score to 4.", "rebuttal": "We sincerely thank the reviewer for recognizing the value and soundness of our approach to accelerating transformer-based visual navigation. We appreciate your acknowledgment of the clarity of our presentation and the thoroughness of our implementation, which we designed with reproducibility in mind. The strong experimental results—demonstrating significant gains in time and memory efficiency—underscore the practical impact of our method. Your positive feedback affirms that our work contributes meaningful advances to the field. We summarize and provide detailed responses to each of your questions below, and hope they address your concerns.\n\n> **Q1:** While the early exiting strategy could reduce memory and time costs in the long run, it may introduce time-varying inference speeds (e.g., some time steps could be faster than others). Could this variability lead to inconsistent navigation performance? To better evaluate the actual performance in terms of speed and memory usage, I would suggest including a time-step-wise comparison of time and memory costs.\n\n**A1:** Thank you for the thoughtful and constructive feedback. Our model adopts a dynamic early-exiting strategy, where the number of activated Transformer layers at each timestep is adaptively determined based on the complexity of the input. More layers are engaged for challenging frames, while simpler inputs may exit earlier, promoting computational efficiency without sacrificing performance. As shown in Fig. 6 (right) of our submission, in most cases, the model exits after skipping 2–3 layers, while only a small fraction of inputs require the full network depth. This results in a consistent behavior pattern across sequences, rather than erratic per-frame variance.\n\nTo directly address your concern regarding potential timestep-wise inconsistency, we conducted an in-depth analysis on a 700-frame trajectory. We segmented the trajectory into 100-frame intervals and computed the average FLOPs and inference time for each segment, comparing our model against the baseline ViNT. As shown in the table below, our model consistently reduces both computational cost and inference time across all intervals, while maintaining or improving action similarity $\\text{Sim}(\\mathbf{a}_t,\\mathbf{a}_t^{gt})$. In over 96\\% of the evaluated trajectories, our approach is more efficient than ViNT without any degradation in navigation accuracy.\n||No. of Frame|100|200|300|400|500|600|700|\n|-|-|-|-|-|-|-|-|-|\n|| FLOPs (×10⁹)|4.37|4.37|4.37|4.37|4.37|4.37|4.37|\n|**ViNT**|Avg Time (s)| 0.218 |0.218|0.218|0.218|0.218|0.218|0.218|\n||$\\text{Sim}(\\mathbf{a}_t,\\mathbf{a}_t^{gt})$ (%)|94.41|94.46|94.49|94.48|94.52|94.51|94.49|\n||FLOPs (×10⁹)|2.02|1.95|1.92|1.96|1.85|1.93|1.93|\n|**Ours**|Avg Time (s)| 0.194|0.190|0.189|0.191|0.185|0.190|0.190|\n||$\\text{Sim}(\\mathbf{a}_t,\\mathbf{a}_t^{gt})$ (%)|94.76|94.88|94.92|94.90|94.92|94.92|94.92|\n\nThese results demonstrate that, despite the dynamic nature of early exiting, our model exhibits stable, consistent, and efficient performance over time in practice.\n\n\n> **Q2:** How are the selected features organized after dynamic sparse feature selection? If the number of patches remains consistent, positional embeddings can simply indicate the spatial information of each patch. However, if the number of sparse features changes dynamically, how does the transformer layer maintain an understanding of the spatial relationships between patches?\n\n**A2:** Thank you for your insightful question. After dynamic sparse feature selection, all patch tokens—both selected and unselected—are retained in their original spatial order. Positional embeddings are first added to all patch tokens, encoding their absolute positions within the image grid. Subsequently, unselected patches are zeroed out and excluded from further computation using attention masking or dropout. While these zeroed tokens do not contribute meaningfully to attention computations, their presence in the sequence ensures that the spatial structure of the full image is preserved. This allows the transformer to reason over the spatial layout consistently, even when only a subset of tokens carries meaningful information. By preserving the original token order and applying zero masking after positional encoding, the model learns to attend selectively to informative regions while maintaining awareness of the global spatial context. This is especially important in visual navigation tasks, where understanding both local saliency and global scene layout is critical for effective decision-making. In the revised version, we will add these explanations to the methodology section.\n\n> **Q3:** The lack of real-world experiments/videos makes it hard for me to analyze the actual performance of this method.\n\n**A3:** Thank you for your insightful suggestion. We fully agree that real-world deployment experiments would further strengthen the practical relevance of our method. In the current version, our approach is thoroughly trained and evaluated on four real-world datasets—Recon, Go-Stanford, SACSoN, and SCAND—comprising over 125 hours of video data. This extensive evaluation under consistent experimental settings allows for fair and comprehensive comparisons with existing baselines, as shown in Table 1 of our submission.\n\nIn addition, we conduct evaluations across three diverse CARLA simulation environments, further validating the robustness and generalizability of our approach in dynamic scenarios. These results demonstrate the effectiveness of our method in terms of both success rate and computational efficiency.\n\nAs part of our future work, we plan to deploy our model on a real robotic platform and will release a demonstration video and open-source code/pretrained model to showcase its real-world performance. We will also mention this plan in the revised version of the paper to clarify our roadmap for deployment validation.\n\n> **Q4:** This work appears to fall within the domain of image-goal navigation. While there exist other visual navigation methods [1,2] focusing on different navigation tasks, the current discussion lacks comparison with these approaches. If the authors intend to claim contributions to the broader field of visual navigation, a discussion would be necessary. \n\n**A1:** We appreciate the reviewer’s insightful comment. While our work focuses on image-goal navigation, we acknowledge the significant advances in vision-and-language navigation (VLN) made by recent methods such as NaVid and NaVILA. These approaches operate in multi-modal settings, requiring agents to interpret natural language instructions to navigate, which introduces additional complexity in semantic understanding and language grounding. In contrast, our study deliberately focuses on the image-goal navigation paradigm, where the goal is defined by a visual observation. This setup allows us to isolate and address the challenges of visual reasoning, control, and efficiency without relying on external linguistic cues. Our proposed method introduces a lightweight, dynamic visual navigation framework that combines sparse feature selection with a confidence-based early-exit strategy. These components enable adaptive inference, reduced computational cost, and improved interpretability through visualized attention masks, tailored specifically for visual input-driven navigation.\n\nWe agree that the broader field of visual navigation includes VLN as an important subdomain. While our current work is centered on pure visual input, our modular design naturally lends itself to extension into multimodal tasks. We see strong potential in integrating our efficient visual reasoning framework with language components for future research in VLN. We will update the related work section in the revised version to explicitly discuss the differences and connections between image-goal navigation and VLN, including recent advances such as NaVid and NaVILA."}, {"review_id": "XnEa0Z8Oxl", "reviewer": "Reviewer_vBgN", "summary": "This paper proposes a dynamic visual navigation framework that adapts feature and layer selection based on scene complexity and an early-exit mechanism is introduced to reduce computational cost. Experimental results show that the proposed method achieves comparable performance with a faster running speed.", "questions": "1. The motivation for applying Gumble-SoftMax should be discussed. Does Gumble-SoftMax make the system achieve better performance?\n2. In Fig. 3, why can the Obs Salency Map interpret the navigation process? Why are these highlighted parts essential for navigation?\n3. Can this method achieve a better success rate when not using the exit strategy?\n4. Can the early exit strategy be applied to other methods and increase their performance?\n5. Why not use GNM for comparison in Tab. 1?\n6. How many experiments were performed to obtain the successful rate of Tab. 2? How random are the results of the experiment?", "limitations": "Yes. I think this method has no negative societal impact .", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengthen\n1. This paper effectively articulates the issues associated with visual navigation,  providing a clear and comprehensive explanation from my points.\n2. Their motivation is clear, i.e. reducing the resource requirements by adapting feature and layer selection based on scene complexity.\n3. The experimental part is well constructed and well-informed.\n4. The research questions are of great practical significance.\n\nWeaknesses\n1. More details of the module and parameter settings should be provided. such as the \\eta in Eq. (9). The influence of \\eta should be discussed. \n2. Experimenting with only three scenes is not convincing enough. Are these the only scenes in the CARLA dataset? How similar are the contents of these three scenes?\n3. The ablation study about the three constraints in adaptive threshold optimisation should be given.\n4. From the experimental results, we can see that the success rate is still not high. Pointing out some limitations of this method can make the article more complete.\n5. More visual results may be provided (The authors do not need to solve this point in the rebuttal period).", "was_revised": true, "final_justification": "My concerns have been addressed in the rebuttal. I keep with my positive score. I believe that the contributions and presentation of the paper have basically met the requirements of the conference.", "rebuttal": "Thank you for the thoughtful and encouraging review. We appreciate your recognition of our motivation to reduce resource demands via adaptive feature and layer selection based on scene complexity. We're glad the paper clearly presents the challenges of visual navigation and demonstrates the effectiveness of our approach. Your feedback reinforces the value of our work. Below, we summarize and respond to each of your questions in detail.\n\n> **W1:**  More details of the module and parameter settings should be provided. such as the $\\eta$ in Eq. (9). The influence of $\\eta$ should be discussed.\n\n**A1:** Thank you for your valuable feedback. The parameter $\\eta$ is optimized via our online Bayesian optimization process. For example, at the threshold between layers 2 and 3—where layer skipping frequently occurs as shown in Figure 6—we found the optimized $\\eta$ for the Recon dataset to be 39.3.\n\nTo study its impact, we evaluated two additional manually chosen values, 34.3 and 44.3. The table below shows the corresponding FLOPs and action similarity results. A lower $\\eta$ reduces skipping, increasing FLOPs, while a higher $\\eta$ leads to performance degradation due to overly aggressive skipping.\n\n|η|FLOPs|$\\text{Sim}(\\mathbf{a}_t,\\mathbf{a}_t^{gt})$|\n|-|-|-|\n|34.3|2.45|94.88|\n|39.3 (optimized)|1.93|94.92|\n|44.3|1.66|93.77|\n\nWe will include additional related experiments in the appendix in the revised version.\n\n> **W2:** Experimenting with only three scenes is not convincing enough. Are these the only scenes in the CARLA dataset? How similar are the contents of these three scenes?\n\n**A2:** Thank you for your comment. Our approach is first extensively trained and evaluated on four real-world datasets—Recon, Go-Stanford, SACSoN, and SCAND—covering over 125 hours of video, which provides a thorough and fair comparison with existing baselines (see Table 1).\n\nFor the CARLA simulation, we manually categorized environments into three difficulty levels and selected one representative scene per level to ensure diversity. Scene A is a small-town layout with simple residential and commercial areas (easy); Scene B is a larger urban area featuring roundabouts and wide junctions (moderate); Scene C is a dense downtown environment with complex layouts and dynamic elements (hard). This progressive selection allows us to systematically evaluate our method across varying complexity levels. We believe these scenes form a balanced benchmark but are open to adding more experiments and discussion in the revision to further strengthen the evaluation.\n\n> **W3:** The ablation study about the three constraints in adaptive threshold optimisation should be given\n\n**A3:** Thank you for the valuable suggestion. Our adaptive threshold optimization employs three joint constraints targeting FLOPs, inference time, and memory usage. To assess each constraint's contribution, we performed an ablation study removing them individually. \n\n|Setting|FLOPs (×10⁹)|Time (s/traj)|Memory (GB)|\n|-|-|-|-|\n|Ours|1.93| 0.228 | 13.35 |\n|w/o FLOPs constraint|2.84|0.291|15.62|\n|w/o Time constraint|2.55|0.273|15.09|\n|w/o Memory constraint|2.16|0.255|14.75|\n\nThe results demonstrate that the FLOPs constraint promotes layer skipping, reducing both inference time and memory consumption. However, removing either the time or memory constraint degrades all efficiency metrics. This confirms that joint optimization of all three constraints delivers optimal performance and balanced resource utilization.\n\n> **W4:** From the experimental results, we can see that the success rate is still not high. Pointing out some limitations of this method can make the article more complete.\n\n**A4:** Thank you for your valuable feedback. We agree that the success rate can be further improved, although our method already outperforms existing baselines. The main limitations stem from dataset size constraints and limited spatial reasoning capabilities. Additionally, models trained on real-world data often face domain shift challenges when applied to simulated environments such as CARLA. Despite these challenges, our approach, which mainly focuses on efficiency, consistently demonstrates robustness. For future work, we plan to collect larger-scale datasets for training and evaluation. Moreover, we aim to enhance spatial understanding by incorporating LLMs or VLMs to further improve success rates without losing too much efficiency. We will include a discussion of these limitations and future directions in the revised manuscript’s conclusion and limitations section.\n\n> **W5:** More visual results may be provided (The authors do not need to solve this point in the rebuttal period).\n\n**A5:** Thank you for your valuable feedback. We agree that additional visual results, such as video demonstrations, would further showcase the practical applicability of our method. In the future version, we plan to include more visual materials, such as agent observation videos from both simulated and real-world settings, along with detailed visualizations such as saliency maps and predicted actions or waypoints to offer more intuitive insights.\n\n> **Q1:** The motivation for applying Gumble-SoftMax should be discussed. Does Gumble-SoftMax make the system achieve better performance?\n\n**A6:** Thank you for your insightful feedback on our use of Gumbel-Softmax. We employ Gumbel-Softmax in the feature selector to address the non-differentiability of discrete feature masking, enabling end-to-end gradient-based optimization. It offers smooth gradient flow, stochastic exploration via Gumbel noise, and adjustable softness through the temperature parameter. Compared to the Straight-Through Estimator (STE), Gumbel-Softmax provides stable gradients and better feature subset exploration, avoiding STE’s noisy and deterministic limitations. Empirically, it improved convergence and achieved a 0.4% accuracy gain on the Recon dataset. We will clarify this comparison in the revised manuscript.\n\n> **Q2:** In Fig. 3, why can the Obs Salency Map interpret the navigation process? Why are these highlighted parts essential for navigation?\n\n**A7:** Thank you for your comment. The saliency maps in Fig. 3 visualize gradients of features selected by our feature selector, with brighter regions indicating higher importance to navigation decisions due to their lower likelihood of being masked. These areas, often distinctive landmarks like buildings or spatially informative cues like the ground ahead, strongly correlate with the agent’s goal image and future target position.\n\nThis relevance likely arises from visual cues in the goal image that guide the agent’s attention toward navigationally critical regions. This observation suggests promising future work, such as dynamically adjusting the agent’s attention throughout navigation, focusing on prominent landmarks early for coarse direction, then shifting to finer spatial details for trajectory refinement. We will expand on this discussion in the revised manuscript.\n\n> **Q3:** Can this method achieve a better success rate when not using the exit strategy?\n\n**A8:** We thank the reviewer for the comment. As shown in Table 3 of the current submission, when the dynamic decoder is disabled and only the feature selector is used, the performance remains similar but slightly lower. This is likely because the reduced feature set fed to a fixed-scale decoder can cause slight overfitting. Additionally, in experiments on CARLA’s Scene A, the success rates with and without the exit strategy are comparable (72.7% vs. 72.6%). However, the FLOPs differ significantly, demonstrating the computational efficiency advantage of our proposed dynamic exit strategy.\n\n> **Q4:** Can the early exit strategy be applied to other methods and increase their performance?\n\n**A9:** Thank you for your insightful feedback. We confirm that our proposed Dynamic Transformer Layer Inference is a general solution and can be seamlessly integrated into various transformer-based models across diverse embodied AI tasks such as Open-VLA and RT-2. Our early exit strategy is designed to flexibly accommodate custom metric functions, making it well-suited for adaptation to different models and application scenarios. In the revised manuscript, we will expand the discussion and explore these possibilities further.\n\n\n> **Q5:** Why not use GNM for comparison in Table 1?\n\n**A10:** Thank you for your comment. GNM relies solely on fully connected layers, resulting in limited performance on real-world benchmarks—for instance, it achieves a score below 90 in $\\text{Sim}(\\mathbf{a}_t,\\mathbf{a}_t^{gt})$ on the RECON dataset. Although Table 2 shows that GNM is computationally efficient in simulation, its navigation capability remains weak. Therefore, we did not include it as a main baseline in Table 1. Nonetheless, we appreciate your suggestion and will include a comparative analysis with GNM in the revised version.\n\n\n> **Q6:** How many experiments were performed to obtain the successful rate of Table 2? How random are the results of the experiment?\n\n**A11:** Thank you for your valuable question. We conducted each navigation trajectory in CARLA 10 times to evaluate the robustness of our method. The table below reports the FLOPs, average execution time, and average successful rate for selected trajectories across these runs. As shown, the results exhibit minimal variance, indicating strong consistency and low randomness. \n\n|No. of Test|1 |2|3|4|5|6|7|8|9|10|\n|-|-|-|-|-|-|-|-|-|-|-|\n|FLOPs (×10⁹)|1.91|1.93|1.92|1.91|1.90|1.92|1.94|1.93|1.93|1.91|\n|Avg Time (s)|0.258|0.260|0.258|0.257|0.260|0.257|0.262|0.260|0.257|0.258|\n|Success Rate|0.725|0.727|0.727|0.726|0.726|0.726|0.728|0.727|0.726|0.727|\n\nThis stability is attributed to the synergy between our feature selector and Bayesian optimization, which together enable adaptive yet reliable behavior across diverse scenarios. We will include this discussion in the experimental section of the revised manuscript."}]

Author rebuttal:

Meta-review: This paper introduces a dynamic visual navigation framework that adaptively selects features and transformer layers conditioned on scene complexity. A trainable hard feature selector yields sparse, interpretable representations, while integration with an early-exit mechanism -- optimized via bayesian search -- reduces computational cost and stabilizes inference. Experiments on real-world and simulated datasets show 2.26 times fewer flops, ~40% lower latency, and ~30% lower memory use, with improved nav success. The work establishes dynamic feature + layer selection as an efficient alternative to static large-scale models for navigation.

In the original round of reviews the committee appreciated the novel introduction of dynamic feature and layer selection for transformer-based nav, improving efficiency without compromising accuracy (reviewers= rkEx,bh3K,vBgN), the integration of early-exit thresholds optimized for hardware deployment (R#nvvJ), and the strong empirical evidence of reduced flops, inference time, and memory usage across benchmarks (R-nvvJ,R-bh3K). The overall technical soundness and practical relevance of the framework were also recognized.

The rebuttal was generally well received, with all reviewers noting that their questions were satisfactorily addressed. Reviewer rkEx acknowledged that clarifications on task setup and inputs/outputs resolved their main concerns. R#nvvJ accepted the broader justification for choosing visual navigation and was convinced by additional robustness results. Reviewer #bh3K found the timestep-wise analysis and spatial reasoning clarifications compelling and raised their score to 4. R--vBgN’s detailed questions on parameters, ablations, and experimental scope were answered thoroughly, and they confirmed no remaining concerns. Overall, the rebuttal led to stronger consensus, with concerns effectively resolved and one score increase. 

The AC concurs with the unanimous consensus of the committee’s reviews and post-rebuttal discussions.
Decision: Accept (poster)